Relationship between mitochondrial respiratory dysfunction of blood mononuclear cells and heart failure severity

Chronic heart failure(CHF)is a clinical syndrome manifested by reduced pumping ability of the heart,increased pressure in heart chambers in both physical activity and at rest.The symptoms of this syndrome are dyspnea,undue fatigability,peripheral edema,which follow structural and functional changes of the myocardium.[1]The growing incidence of CHF,especially among elderly people,is an urgent problem for medicine in the vast majority of industrialized countries.For instance,in Russian Federation,CHF is diagnosed in about 7%of cardiovascular patients.At the same time,this indicator varies from 0.3%in young people(20-29 years old)to 70%in the older age group.[2,3].

ASLP-DL—A Novel Approach Employing Lightweight Deep Learning Framework for Optimizing Accident Severity Level Prediction

Highway safety researchers focus on crash injury severity,utilizing deep learning—specifically,deep neural networks(DNN),deep convolutional neural networks(D-CNN),and deep recurrent neural networks(D-RNN)—as the preferred method for modeling accident severity.Deep learning’s strength lies in handling intricate relation-ships within extensive datasets,making it popular for accident severity level(ASL)prediction and classification.Despite prior success,there is a need for an efficient system recognizing ASL in diverse road conditions.To address this,we present an innovative Accident Severity Level Prediction Deep Learning(ASLP-DL)framework,incorporating DNN,D-CNN,and D-RNN models fine-tuned through iterative hyperparameter selection with Stochastic Gradient Descent.The framework optimizes hidden layers and integrates data augmentation,Gaussian noise,and dropout regularization for improved generalization.Sensitivity and factor contribution analyses identify influential predictors.Evaluated on three diverse crash record databases—NCDB 2018–2019,UK 2015–2020,and US 2016–2021—the D-RNN model excels with an ACC score of 89.0281%,a Roc Area of 0.751,an F-estimate of 0.941,and a Kappa score of 0.0629 over the NCDB dataset.The proposed framework consistently outperforms traditional methods,existing machine learning,and deep learning techniques.

Impact of interleukin 6 levels on acute lung injury risk and disease severity in critically ill sepsis patients

BACKGROUND Sepsis is a life-threatening condition characterized by a dysregulation of the host response to infection that can lead to acute lung injury(ALI)and multiple organ dysfunction syndrome(MODS).Interleukin 6(IL-6)is a pro-inflammatory cytokine that plays a crucial role in the pathogenesis of sepsis and its complications.AIM To investigate the relationship among plasma IL-6 levels,risk of ALI,and disease severity in critically ill patients with sepsis.METHODS This prospective and observational study was conducted in the intensive care unit of a tertiary care hospital between January 2021 and December 2022.A total of 83 septic patients were enrolled.Plasma IL-6 levels were measured upon admission using an enzyme-linked immunosorbent assay.The development of ALI and MODS was monitored during hospitalization.Disease severity was evaluated by Acute Physiology and Chronic Health Evaluation II(APACHE II)and Sequential Organ Failure Assessment(SOFA)scores.RESULTS Among the 83 patients with sepsis,38(45.8%)developed ALI and 29(34.9%)developed MODS.Plasma IL-6 levels were significantly higher in patients who developed ALI than in those without ALI(median:125.6 pg/mL vs 48.3 pg/mL;P<0.001).Similarly,patients with MODS had higher IL-6 levels than those without MODS(median:142.9 pg/mL vs 58.7 pg/mL;P<0.001).Plasma IL-6 levels were strongly and positively correlated with APACHE II(r=0.72;P<0.001)and SOFA scores(r=0.68;P<0.001).CONCLUSIONElevated plasma IL-6 levels in critically ill patients with sepsis were associated with an increased risk of ALI andMODS.Higher IL-6 levels were correlated with greater disease severity,as reflected by higher APACHE II andSOFA scores.These findings suggest that IL-6 may serve as a biomarker for predicting the development of ALI anddisease severity in patients with sepsis.

Effect of safety valve types on the gas venting behavior and thermal runaway hazard severity of large-format prismatic lithium iron phosphate batteries

The safety valve is an important component to ensure the safe operation of lithium-ion batteries(LIBs).However,the effect of safety valve type on the thermal runaway(TR)and gas venting behavior of LIBs,as well as the TR hazard severity of LIBs,are not known.In this paper,the TR and gas venting behavior of three 100 A h lithium iron phosphate(LFP)batteries with different safety valves are investigated under overheating.Compared to previous studies,the main contribution of this work is in studying and evaluating the effect of gas venting behavior and TR hazard severity of LFP batteries with three safety valve types.Two significant results are obtained:(Ⅰ)the safety valve type dominates over gas venting pressure of battery during safety venting,the maximum gas venting pressure of LFP batteries with a round safety valve is 3320 Pa,which is one order of magnitude higher than other batteries with oval or cavity safety valve;(Ⅱ)the LFP battery with oval safety valve has the lowest TR hazard as shown by the TR hazard assessment model based on gray-fuzzy analytic hierarchy process.This study reveals the effect of safety valve type on TR and gas venting,providing a clear direction for the safety valve design.

Association between Low Ambient Temperature and the Severity of Acute Ischemic Stroke

Objective:To explore the relationships among ambient temperature,ischemic stroke severity,and blood pressure.Methods:Meteorological data(2005–2015)were collected from the Guangzhou Meteorological Data Service.Ischemic stroke patients from the Department of Neurology of the First Affiliated Hospital,Sun Yat-sen University were retrospectively evaluated,each winter from 2005 to 2015.Patient demographics,baseline measurements,and National Institute of Health Stroke Scale(NIHSS)score were evaluated.Results:Three hundred sixty-two patients were included.The median latency from symptom onset to admission was 2 d(IQR:1–3 d).During recruitment,the highest and lowest temperatures were 39℃and 1.3℃,respectively.Hypertension was the most common comorbidity(75.1%).NIHSS scores at admission and discharge were higher in the cold-exposed group than in the controls regardless of the average temperature at admission.In addition,systolic and diastolic blood pressure values at admission were higher in the cold-exposed group than in the controls.When stratified by hypertensive status,the average and minimum temperatures at admission were negatively associated with systolic and diastolic blood pressure values in hypertensive patients.Reductions in the average and minimum temperatures at symptom onset were associated with more severe stroke.Conclusion:Ischemic stroke patients with symptom onset in winter had higher systolic blood pressure values and more serious neurologic deficits upon admission.

Severity Risk Analysis Matrix Ranking (SRAMR) for Oil-Spill Contingency Planning: Asemoku-Agip Pipeline in Perspective

One of the down sides of crude oil exploration and exploitation in the developing nations is its impacts on the environment. A major manifestation of poor crude oil management is oil-spillages. Mitigation strategies have been too expensive, but a cheaper recent way of managing crude-spills is by developing a severity risk analysis matrix ranking (SRAMR). The spatial data-sets deployed in this study were acquired from the USGS, Google Earth Pro, and NOSDRA. A buffer zone of 100 - 400 meters was created to characterize the LULC characteristics of the area. Also, this was to help develop a risk sensitivity characteristic. The study found that the vegetal cover was the environmental resource at high risk to crude-spills in the area, while other land-uses were at low risk of crude-spill. It is hoped that the finding from this study informs policy development and planning for crude oil spill incidents.

Association between serum albumin levels and disease severity in adult patients with dengue: A 7-year retrospective cohort study in China's Mainland

Objective:To identify the association between serum albumin levels and disease severity among adult patients with dengue in China's Mainland.Methods:This retrospective cohort study analyzed the data of adult patients(aged≥18 years)hospitalized with laboratory-confirmed dengue in a tertiary center for infectious diseases from 2013 to 2019 in China's Mainland.Serum albumin levels were estimated and compared between patients with severe dengue and non-severe dengue.Additionally,the association between serum albumin levels and severe dengue was evaluated using a generalized linear model[relative risks(RR)].Multivariate logistic regression analysis was performed to identify the potential predictors of severe dengue.Results:Overall,1568 patients were included in this study.Of these patients,34(2.17%)developed severe dengue.The median serum albumin levels were significantly lower in patients with severe dengue than that in those with non-severe dengue(33 g/L vs.37 g/L,P<0.001).After adjustment for age,sex,and comorbidities,hypoalbuminemia(RR 4.877,95%CI 2.193-11.461,P<0.001)was found to be a predictor of severe dengue.Serum albumin levels(OR 1.303,95%CI 1.161-1.462,P<0.001)and age(OR 1.038,95%CI 1.017-1.061,P<0.001)were significant risk factors for severe dengue.The area under the curve for serum albumin levels to distinguish severe dengue was 0.787.Conclusions:Lower serum albumin levels were significantly associated with disease severity in adult patients with dengue.Hypoalbuminemia on admission resulted in at least a four-fold increased risk of severe dengue.

Acute pancreatitis:A review of diagnosis,severity prediction and prognosis assessment from imaging technology,scoring system and artificial intelligence

Acute pancreatitis(AP)is a potentially life-threatening inflammatory disease of the pancreas,with clinical management determined by the severity of the disease.Diagnosis,severity prediction,and prognosis assessment of AP typically involve the use of imaging technologies,such as computed tomography,magnetic resonance imaging,and ultrasound,and scoring systems,including Ranson,Acute Physiology and Chronic Health Evaluation II,and Bedside Index for Severity in AP scores.Computed tomography is considered the gold standard imaging modality for AP due to its high sensitivity and specificity,while magnetic resonance imaging and ultrasound can provide additional information on biliary obstruction and vascular complications.Scoring systems utilize clinical and laboratory parameters to classify AP patients into mild,moderate,or severe categories,guiding treatment decisions,such as intensive care unit admission,early enteral feeding,and antibiotic use.Despite the central role of imaging technologies and scoring systems in AP management,these methods have limitations in terms of accuracy,reproducibility,practicality and economics.Recent advancements of artificial intelligence(AI)provide new opportunities to enhance their performance by analyzing vast amounts of clinical and imaging data.AI algorithms can analyze large amounts of clinical and imaging data,identify scoring system patterns,and predict the clinical course of disease.AI-based models have shown promising results in predicting the severity and mortality of AP,but further validation and standardization are required before widespread clinical application.In addition,understanding the correlation between these three technologies will aid in developing new methods that can accurately,sensitively,and specifically be used in the diagnosis,severity prediction,and prognosis assessment of AP through complementary advantages.

Predictive value of presepsin and acylcarnitines for severity and biliary drainage in acute cholangitis

BACKGROUND Bacteremia,which is a major cause of mortality in patients with acute cholangitis,induces hyperactive immune response and mitochondrial dysfunction.Presepsin is responsible for pathogen recognition by innate immunity.Acylcarnitines are established mitochondrial biomarkers.AIM To clarify the early predictive value of presepsin and acylcarnitines as biomarkers of severity of acute cholangitis and the need for biliary drainage.METHODS Of 280 patients with acute cholangitis were included and the severity was stratified according to the Tokyo Guidelines 2018.Blood presepsin and plasma acylcarnitines were tested at enrollment by chemiluminescent enzyme immunoassay and ultra-high-performance liquid chromatography-mass spectrometry,respectively.RESULTS The concentrations of presepsin,procalcitonin,short-and medium-chain acylcarnitines increased,while long-chain acylcarnitines decreased with the severity of acute cholangitis.The areas under the receiver operating characteristic curves(AUC)of presepsin for diagnosing moderate/severe and severe cholangitis(0.823 and 0.801,respectively)were greater than those of conventional markers.The combination of presepsin,direct bilirubin,alanine aminotransferase,temperature,and butyryl-L-carnitine showed good predictive ability for biliary drainage(AUC:0.723).Presepsin,procalcitonin,acetyl-L-carnitine,hydroxydodecenoyl-Lcarnitine,and temperature were independent predictors of bloodstream infection.After adjusting for severity classification,acetyl-L-carnitine was the only acylcarnitine independently associated with 28-d mortality(hazard ratio 14.396;P<0.001)(AUC:0.880).Presepsin concentration showed positive correlation with direct bilirubin or acetyl-L-carnitine.CONCLUSION Presepsin could serve as a specific biomarker to predict the severity of acute cholangitis and need for biliary drainage.Acetyl-L-carnitine is a potential prognostic factor for patients with acute cholangitis.Innate immune response was associated with mitochondrial metabolic dysfunction in acute cholangitis.

Comparison of the Clinical Profile and Severity Factors of COVID-19 during the First 4 Waves in a Paediatric Population of Yaounde

Introduction: COVID-19 is an infectious disease that has been causing a global pandemic since 2019. Although clinical forms are generally less severe in children than in adults, children nevertheless present polymorphous clinical forms and severe cases that can lead to death. Objective: To describe the clinical presentations found in the different waves of COVID-19, and to highlight the different factors of severity. Materials and Methods: We conducted a cross-sectional study with retrospective and prospective data collection which lasted 7 months (from November 2021 to June 2022) and covered a study period from 6 March 2020 to 22 June 2022, i.e. 27 months. All patients aged 0 to 18 years, suspected of having COVID-19, confirmed by real-time RT-PCR or an antigenic Rapid Diagnostic Test or antibody were included. These patients were to be managed in the Mother and Child Centre of the Chantal Biya Foundation, as well as in the Specialised Centre for the Management of COVID patients, annex number II of the Yaoundé Central Hospital. The results were analysed using IBM SPSS.23 software. Results: We included 163 patients in our study. No paediatric patients were registered during the 3rd wave. The mean age of the patients in the study population was 13 ± 5 years with extremes from 15 days to 18 years. We had a female predominance with a sex ratio of 0.83. The most common comorbidity was asthma. The first wave presented mainly with respiratory symptoms such as dry cough and signs of respiratory distress. The second wave presented mainly with digestive symptoms such as diarrhoea, abdominal pain and vomiting. The fourth wave presented with ENT signs such as sore throat, and rhinorrhea. Factors associated with severity were mainly age less than five years (OR = 17.69), vomiting (OR = 6.50), presence of comorbidities (OR = 3.39), and alteration of vital parameters such as bradypnoea (OR = 19.68), bradycardia (OR = 6.34), tachycardia (OR = 3.73), oxygen saturation Conclusion: Clinical presentations varied between waves and the main risk factor was age under 5 years. The fourth wave was less severe than the second wave, which in turn was less severe than the first. (4th nd st) which allows us to humbly recommend more screening for patients under 18 years of age with respiratory, ENT and digestive signs and special attention for those under 5 years of age.

Plant Disease Severity Assessment Based on Machine Learning and Deep Learning: A Survey

The world’s agricultural production suffers huge losses estimated between 20% and 40% annually. 40% to 50% of such losses are due to pest and diseases which cause significant economic losses every year. Precise assessment of severity is crucial for suitable management of crop diseases. It helps famers to avoid yield losses, reduce production costs, ensure good disease management and so on. This paper is a review of plant diseases severity estimation solutions proposed by researchers the last few years and based on Image Processing Techniques (IPT), classical Machine Learning (ML) and Deep Learning (DL) algorithms. The analysis of these solutions has allowed us to identify their limitations and potential challenges in plant disease severity assessment.

Assessing fire severity in Turkey's forest ecosystems using spectral indices from satellite images

Fire severity classifications determine fire damage and regeneration potential in post-fire areas for effective implementation of restoration applications.Since fire damage varies according to vegetation and fire characteristics,regional assessment of fire severity is crucial.The objectives of this study were:(1)to test the performance of different satellite imagery and spectral indices,and two field—measured severity indices,CBI(Composite Burn Index)and GeoCBI(Geometrically structured Composite Burn Index)to assess fire severity;(2)to calculate classification thresholds for spectral indices that performed best in the study areas;and(3)to generate fire severity maps that could be used to determine the ecological impact of forest fires.Five large fires in Pinus brutia(Turkish pine)and Pinus nigra subsp.pallasiana var.pallasiana(Anatolian black pine)—dominated forests during 2020 and 2021 were selected as study sites.The results show that GeoCBI provided more reliable estimates of field—measured fire severity than CBI.While Sentinel-2 and Landsat-8/OLI images performed similarly well,MODIS performed poorly.Fire severity classification thresholds were determined for Sentinel-2 based RdNBR,dNBR,dSAVI,dNDVI,and dNDMI and Landsat-8/OLI based dNBR,dNDVI,and dSAVI.Among several spectral indices,the highest accuracy for fire severity classification was found for Sentinel-2 based RdNBR(72.1%)and Landsat-8/OLI based dNBR(69.2%).The results can be used to assess and map fire severity in forest ecosystems similar to those in this study.

Investigating the Relationship between TyG, TyG-BMI Index and Laboratory Indicators and COVID-19 Severity

Objective: The objective of this study was to investigate the relationship between the triglyceride-glucose (TyG) index, triglyceride-glucose-BMI (TyG-BMI) index, laboratory indices, and disease severity in patients with COVID-19. Methods: A retrospective analysis of COVID-19 patients treated at a tertiary hospital in Mianyang City, Sichuan Province, China, from 1 May to 31 May 2023 was performed. The patients were divided into two groups: 66 cases in the moderate group and 61 cases in the severe group. Additionally, 69 uninfected individuals from the medical examination center during the same period were selected as the control group. Spearman rank correlation was used to determine the correlation between the indices and COVID-19 severity. Multiple logistic regression analysis was performed to identify the factors affecting COVID-19 severity. ROC curves were constructed to assess the predictive value of the TyG and TyG-BMI indices for severe COVID-19. Results: There were significant differences in smoking and diabetes between the three groups (P Conclusion: Smoking, AST, ALB, TyG index, and TyG-BMI index are valuable in assessing the severity of COVID-19, with the TyG-BMI index having a higher predictive value than the TyG index.

The role of quick Sepsis-related Organ Failure Assessment score as simple scoring system to predict Fournier gangrene mortality and the correlation with Fournier’s Gangrene Severity Index: Analysis of 69 patients

Objective:Fournier’s gangrene is a rare but life-threatening infection disease with high mortality rate.The quick Sepsis-related Organ Failure Assessment(qSOFA)is a new and simpler scoring system that may identify patients with suspected infection who are at greater risk for a poor outcome.The purpose of this study was to find out role of qSOFA in determining prognosis of Fournier’s gangrene patients.Methods:This study is a case control with retrospective review of Fournier’s gangrene patients treated at Hasan Sadikin Hospital from January 2013 to December 2017 who met inclusion criteria.Participants were divided into two groups according to qSOFA score as high qSOFA(2-3)and low qSOFA(0-1).Results:From 69 patients,the mortality rate was 24.6%.The sensitivity of qSOFA score to predict mortality was 88.2%;the specificity was 94.2%;positive predictive value was 83.3%;negative predictive value was 96.1%;positive likelihood ratio was 15.2;negative likelihood ratio was 0.12;and the area under the receiver operating characteristic curve of qSOFA was 94.2%.There was significant association between qSOFA scale and mortality with p-value of 0.0001.The qSOFA score has strong positive correlation with Fournier’s Gangrene Severity Index(p<0.0001,r=0.704).Conclusion:qSOFA scoring system has a high prognostic value and can be used to determine prognosis of Fournier’s gangrene patients.

Risk Factors for Severity and Mortality of Patients Hospitalized for COVID-19 during the 3rd Wave of the Epidemic-Sao Tome and Principe

Rationale: In the literature, some risk factors for severity and mortality from COVID-19 have been indicated. However, these factors can change, depending on the characteristics of the population and health services. In this sense, longitudinal studies can be useful for understanding local realities and subsidizing health actions based on these realities. Objective: To analyze the risk factors for severity and death in hospitalized patients with COVID-19. Methods: A retrospective cohort of patients with COVID-19 hospitalized from August 1 to October 16, 2021 (3rd wave of the pandemic), notified by the Department of Epidemiological Surveillance of Sao Tome and Principe. We employed measures of strength of associations for the analysis of exposure risk factors. Results: We analyzed 110 hospitalized patients (31.8% severe-critical and 68.2% non-severe). The risk factors for severe forms of COVID-19 were: being aged ≥60 years (RR = 3.3), being male (RR = 2), having comorbidities (RR = 2) and the risk increases to 10-fold for multicomorbidities, with emphasis on obesity, neoplasia, skin-muscle-surgical infection, dementia and to some degree CVD. 62.9% of patients with severe forms of the disease were not vaccinated. Risk factors for death among hospitalized and severe/critical cases, respectively, were having comorbidities (RR = 8 and 2.4) multicomorbidities (RR = 10 and 2.8 for those with 2 comorbidities and RR = 33.3 and 4 for those with 3 or 4 comorbidities), especially diabetes, dementia, neoplasia, cutaneous-muscular infection, and obesity. Although CVD was not associated with risk factors for death, these were the most frequently found among the severely hospitalized and deaths. In addition, important risk factors associated with death were not using corticoids (RR = 3.3, 230-fold risk) and not using anticoagulants-heparin (RR = 1.3, 30% risk) more compared to the severe cases that did use them. Most of the patients who died (63.2%) were not vaccinated. Moreover, having only 1 dose of the vaccine was a risk factor 1.9 times more for death among all hospitalized patients, but in the severe cases, there was no association between the variable vaccination and death. Among those hospitalized with 2 doses, it was a 0.5-fold protective factor among those hospitalized. The Delta variant of Sarscov-2 was the one found among severe cases and deaths investigated by genetic sequencing, with more exuberant clinical features compared to the other 2 previous vaccinations. Conclusion: Being elderly, male and presenting comorbidities, mainly multicomorbidities were the main characteristics associated with severity of COVID-19. On the other hand, comorbidities, and even worse, multicomorbidities, hospitalization for respiratory failure, lowered level of consciousness, no use of corticoid and no use of anticoagulation in critically ill patients, and not having at least 2 doses of vaccine for covid-19, were characteristics associated with death by COVID-19. These results will help inform healthcare providers so that the best interventions can be implemented to improve outcomes for patients with COVID-19. Public health interventions must be carefully tailored and implemented in these susceptible groups to reduce the risk of mortality in patients with COVID-19 and then the risk of major complications. Intensive and regular follow-up is needed to detect early occurrences of clinical conditions.

Effect of vaccination status on CORADS and computed tomography severity score in hospitalized COVID-19 patients:A retrospective study

BACKGROUND The coronavirus disease 2019(COVID-19)pandemic is continuing.The disease most commonly affects the lungs.Since the beginning of the pandemic thorax computed tomography(CT)has been an indispensable imaging method for diagnosis and follow-up.The disease is tried to be controlled with vaccines.Vaccination reduces the possibility of a severe course of the disease.AIM The aim of this study is to investigate whether the vaccination status of patients hospitalized due to COVID-19 has an effect on the CT severity score(CT-SS)and CORADS score obtained during hospitalization.METHODS The files of patients hospitalized between April 1,2021 and April 1,2022 due to COVID-19 were retrospectively reviewed.A total of 224 patients who were older than 18 years of age,whose vaccination status was accessible,whose severe acute respiratory syndrome coronavirus 2 polymerase chain reaction result was positive,and who had a Thorax CT scan during hospitalization were included in the study.RESULTS Among the patients included in the study,52.2%were female and the mean age was 61.85 years.The patients applied to the hospital on the average 7th day of their complaints.While 63 patients were unvaccinated(Group 1),20 were vaccinated with a single dose of CoronaVac(Group 2),24 with a single dose of BioNTech(Group 3),38 with 2 doses of CoronaVac(Group 4),40 with 2 doses of BioNTech(Group 5),and 39 with 3 doses of vaccine(2 doses of CoronaVac followed by a single dose of BioNTech,Group 6).CT-SS ranged from 5 to 23,with a mean of 12.17.RESULTS CT-SS mean of the groups were determined as 14.17,13.35,11.58,10.87,11.28,10.85,respectively.Accordingly,as a result of the comparisons between the groups,the CT-SS levels of the unvaccinated patients found to be significantly higher than the other groups.As the vaccination rates increased,the rate of typical COVID-19 findings on CT was found to be significantly lower.CONCLUSION Increased vaccination rates in COVID-19 patients reduce the probability of typical COVID-19 symptoms in the lungs.It also reduces the risk of severe disease and decreases CT Severity Scores.This may lead to a loss of importance of Thorax CT in the diagnosis of COVID-19 pneumonia as the end of the pandemic approaches.

Red cell distribution width: A predictor of the severity of hypertriglyceridemia-induced acute pancreatitis

BACKGROUND Compared with patients with other causes of acute pancreatitis,those with hypertriglyceridemia-induced acute pancreatitis(HTG-AP)are more likely to develop persistent organ failure(POF).Therefore,recognizing the individuals at risk of developing POF early in the HTG-AP process is a vital for improving outcomes.Bedside index for severity in acute pancreatitis(BISAP),a simple parameter that is obtained 24 h after admission,is an ideal index to predict HTG-AP severity;however,the suboptimal sensitivity limits its clinical application.Hence,current clinical scoring systems and biochemical parameters are not sufficient for predicting HTG-AP severity.AIM To elucidate the early predictive value of red cell distribution width(RDW)for POF in HTG-AP.METHODS In total,102 patients with HTG-AP were retrospectively enrolled.Demographic and clinical data,including RDW,were collected from all patients on admission.RESULTS Based on the Revised Atlanta Classification,37(33%)of 102 patients with HTG-AP were diagnosed with POF.On admission,RDW was significantly higher in patients with HTG-AP and POF than in those without POF(14.4%vs 12.5%,P<0.001).The receiver operating characteristic curve demonstrated a good discrim-inative power of RDW for POF with a cutoff of 13.1%,where the area under the curve(AUC),sensitivity,and specificity were 0.85,82.4%,and 77.9%,respectively.When the RDW was≥13.1%and one point was added to the original BISAP to obtain a new BISAP score,we achieved a higher AUC,sensitivity,and specificity of 0.89,91.2%,and 67.6%,respectively.CONCLUSION RDW is a promising predictor of POF in patients with HTG-AP,and the addition of RDW can promote the sensitivity of BISAP.

Radial growth in Qinghai spruce is most sensitive to severe drought in the Qilian Mountains of Northwest China

Global warming and frequent extreme drought events lead to tree death and extensive forest decline,but the underlying mechanism is not clear.In drought years,cambial development is more sensitive to climate change,but in different phenological stages,the response rela-tionship is nonlinear.Therefore,the dynamic relationship between tree radial growth and climatic/environmental fac-tors needs to be studied.We thus continuously monitored radial growth of Qinghai spruce(Picea crassifolia Kom.)and environmental factors from January 2021 to November 2022 using point dendrometers and portable meteorological weather stations in the central area of the Qilian Mountains.The relationship and stability between the radial growth of Qinghai spruce and environmental factors were compared for different levels of drought in 2021 and 2022.The year 2022 had higher temperatures and less precipitation and was drier than 2021.Compared with 2021,the growing period in 2022 for Qinghai spruce was 10 days shorter,maximum growth rate(Grmax)was 4.5μm·d^(-1) slower,and the initiation of growth was 6 days later.Growth of Qinghai spruce was always restricted by drought,and the stem radial increment(SRI)was more sensitive to precipitation and air relative humidity.Seasonal changes in cumulative radial growth were divided into four phenological stages according to the time of growth onset,cessation,and maximum growth rate(Grmax)of Qinghai spruce.Stability responses of SRI to climate change were stronger in Stage 3 and Stage 4 of 2021 and stronger in Stage 1(initiation growth stage)and Stage 3 of 2022.The results provide important information on the growth of the trees in response to drought and for specific managing forests as the climate warms.

Superplasticity of fine-grained Mg-10Li alloy prepared by severe plastic deformation and understanding its deformation mechanisms

The superplastic behavior and associated deformation mechanisms of a fine-grained Mg-10.1 Li-0.8Al-0.6Zn alloy(LAZ1011)with a grain size of 3.2μm,primarily composed of the BCCβphase and a small amount of the HCPαphase,were examined in a temperature range of 473 K to 623 K.The microstructural refinement of this alloy was achieved by employing high-ratio differential speed rolling.The best superplasticity was achieved at 523 K and at strain rates of 10^(-4)-5×10^(-4)s^(-1),where tensile elongations of 550±600%were obtained.During the heating and holding stage of the tensile samples prior to tensile loading,a significant increase in grain size was observed at temperatures above 573 K.Therefore,it was important to consider this effect when analyzing and understanding the superplastic deformation behavior and mechanisms.In the investigated strain rate range,the superplastic flow at low strain rates was governed by lattice diffusion-controlled grain boundary sliding,while at high strain rates,lattice diffusion-controlled dislocation climb creep was the rate-controlling deformation mechanism.It was concluded that solute drag creep is unlikely to occur.During the late stages of deformation at 523 K,it was observed that grain boundary sliding led to the agglomeration of theαphase,resulting in significant strain hardening.Deformation mechanism maps were constructed forβ-Mg-Li alloys in the form of 2D and 3D formats as a function of strain rate,stress,temperature,and grain size,using the constitutive equations for various deformation mechanisms derived based on the data of the current tests.

Management of Severely Malnourished Children Aged 6 - 59 Months Hospitalized in the Pediatric Ward of Kayanza Hospital/Burundi

The implementation of WHO guidelines has significantly reduced hospital mortality due to severe malnutrition. Nevertheless, severe acute malnutrition in children under five remains a major public health problem in all low-income countries and is little studied. The study aimed to assess the nutritional status and quality of management of severe acute malnutrition in children aged 06 to 59 months hospitalized in the pediatric ward of Kayanza Hospital. This is a prospective study with descriptive and analytical aims over 6 months from February 10 to July 9, 2023. All children aged 6 to 59 months admitted for severe acute malnutrition are included in this study. The results show that the most affected age group is 12 to 23 months (42.55%). Marasmus is the most common clinical form, at 70.2%. Housewives and mothers from rural areas are the most affected, with rates ranging from 69.14% to 91.49%. The study shows that the reasons for consultation are respectively: edema (29.78%), diarrhea (26.59%), vomiting (19.14%), and fever (14.89%). Dehydration and hypothermia were the main complications observed in 45.74% and 22.34% respectively. Medical treatment was provided by antibiotics (44.68%), artesunate (31.91%), and resomal (21.27%). Nutritional treatment consisted of F75 100% milk and F100 100% milk. Pathologies observed were: anemia 38.29%, malaria 37.23%, urinary tract infection 12.7%, measles 11.7%. The national protocol for integrated management of acute malnutrition (PCIMA) was followed. This study shows that 72.3% of children were successfully treated, with 9.5% dropping out and dying, and 8.5% not responding. We found that malnutrition remains a public health problem, affecting mostly children aged 12 to 24 months. The main complications or pathologies associated with malnutrition are diarrhea, malaria, and fever, and the majority of children suffer from marasmus.