با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
ردیف | عنوان | نوع |
---|---|---|
1 |
Identifying neuronal correlates of dying and resuscitation in a model of reversible brain anoxia
شناسایی همبستگی های عصبی در حال مرگ و احیا در مدل بی هوشی مغز برگشت پذیر-2020 We developed a new rodent model of reversible brain anoxia and performed continuous electrocorticographic
(ECoG) and intracellular recordings of neocortical neurons to identify in real-time the cellular and network
dynamics that successively emerge throughout the dying-to-recovery process. Along with a global decrease in
ECoG amplitude, deprivation of oxygen supply resulted in an early surge of beta-gamma activities, accompanied
by rhythmic membrane depolarizations and regular firing in pyramidal neurons. ECoG and intracellular signals
were then dominated by low-frequency activities which progressively declined towards isoelectric levels.
Cortical neurons during the isoelectric state underwent a massive membrane potential depolarizing shift, captured
in the ECoG as a large amplitude triphasic wave known as the “wave-of-death” (WoD). This neuronal
anoxic depolarization, associated with a block of action potentials and a loss of cell integrative properties, could
however be reversed if brain re-oxygenation was rapidly restored (within 2–3.5 min). The subsequent slow
repolarization of neocortical neurons resulted in a second identifiable ECoG wave we termed “wave-of-resuscitation”
since it inaugurated the progressive regaining of pre-anoxic synaptic and firing activities. These
results demonstrate that the WoD is not a biomarker of an irremediable death and unveil the cellular correlates
of a novel ECoG wave that may be predictive of a successful recovery. The identification of real-time biomarkers
of onset and termination of cell anoxic insult could benefit research on interventional strategies to optimize
resuscitation procedures. Keywords: Brain anoxia | Dying | Resuscitation | Near-death experience | Neocortex | Neuronal excitability |
مقاله انگلیسی |
2 |
Computerized data mining analysis of keywords as indicators of the concepts in AHA-BLS guideline updates
تجزیه و تحلیل داده کاوی رایانه ای از کلمات کلیدی به عنوان شاخص های مفاهیم در به روزرسانی های راهنمای AHA-BLS-2019 Introduction: Cardiopulmonary resuscitation (CPR) guidelines have been updated every 5 years since 2000. Significant
changes have been made in each update, and every time a guideline is changed, the instructors of each
country that ratify the American Heart Association (AHA) must review the contents of the revised guideline to
understand the changes made in the concept of CPR. The purpose of this study was to use a computerized data
mining method to identify and characterize the changes in the key concepts of the AHA-Basic Life Support
(BLS) updates between 2000 and 2015.
Methods: We analyzed the guidelines of the AHA-BLS provider manual of 2000, 2005, 2010, and 2015 using a
computerized data mining method and attempted to identify the changes in keywords along with changes in
the guideline.
Results: In particular, the 2000 guideline has focused on the detailed BLS technique of an individual health care
provider, whereas the 2005 and 2010 guidelines have focused on changing the ratio of chest compressions and
breathing and changing the BLS sequence, respectively. In the most recent 2015 guideline, the CPR team was
the central topic. We observed that as the guidelines were updated over the years, keywords related to CPR
and automated external defibrillators (AED) associated with co-occurrence network continued to appear.
Conclusions: Analysis revealed that keywords related to CPR and AED associatedwith the co-occurrence network
continued to appear. We believe that the results of this study will ultimately contribute to optimizing AHAs educational
strategies for health care providers. Keywords: American Heart Association | Basic Life Support | Cardiopulmonary resuscitation | Guideline | Data mining analys |
مقاله انگلیسی |
3 |
A machine learning approach for predicting urine output after fluid administration
یک روش یادگیری ماشین برای پیش بینی خروجی ادرار پس از تجویز مایعات-2019 Background and objective: To develop a machine learning model to predict urine output (UO) in sepsis patients after fluid resuscitation. Methods: We identified sepsis patients in the Multiparameter Intelligent Monitoring in Intensive Care-III v1.4 database according to the Sepsis-3 criteria. We focused on two outcomes: whether the UO decreased after fluid administration and whether oliguria (defined as UO less than the threshold of 0.5 mL/kg/h) de- veloped. A gradient tree-based machine learning model implemented with an eXtreme Gradient Boosting algorithm was used to integrate relevant physiological parameters for predicting the aforementioned out- comes. A confusion matrix was computed. Results: A total of 232,929 events in 19,275 patients were included. Using decreased UO as the outcome measure, the optimal model achieved an area under the curve (AUC) of 0.86; for predicting oliguria, most models achieved an AUC greater than 0.86, and the highest sensitivity was 92.2% when the model was applied to patients with baseline oliguria. Conclusions: Machine learning could help clinicians evaluate fluid status in sepsis patients after fluid administration, thus preventing fluid overload-related complications. Keywords: Sepsis | Prediction | Machine learning | Electronic health records | Clinical decision support | Fluid resuscitation |
مقاله انگلیسی |
4 |
Outcome prediction of out-of-hospital cardiac arrest with presumed cardiac aetiology using an advanced machine learning technique
پیش بینی نتیجه ایست قلبی خارج از بیمارستان با اتیولوژی قلب فرضی با استفاده از یک روش پیشرفته یادگیری ماشین-2019 Background: Outcome prediction for patients with out-of-hospital cardiac arrest (OHCA) has the possibility to detect patients who could have been
potentially saved. Advanced machine learning techniques have recently been developed and employed for clinical studies. In this study, we aimed to
establish a prognostication model for OHCA with presumed cardiac aetiology using an advanced machine learning technique.
Methods and Results: Cohort data from a prospective multi-centre cohort study for OHCA patients transported by an ambulance in the Kanto area of
Japan between January 2012 and March 2013 (SOS-KANTO 2012 study) were analysed in this study. Of 16,452 patients, data for OHCA patients aged
18 years with presumed cardiac aetiology were retrieved, and were divided into two groups (training set: n = 5718, between January 1, 2012 and
December 12, 2012; test set: n = 1608, between January 1, 2013 and March 31, 2013). Of 421 variables observed during prehospital and emergency
department settings, 35 prehospital variables, or 35 prehospital and 18 in-hospital variables, were used for outcome prediction of 1-year survival using a
random forest method. In validation using the test set, prognostication models trained with 35 variables, or 53 variables for 1-year survival showed area
under the receiver operating characteristics curve (AUC) values of 0.943 (95% CI [0.930, 0.955]) and 0.958 (95% CI [0.948, 0.969]), respectively.
Conclusions: The advanced machine learning technique showed favourable prediction capability for 1-year survival of OHCA with presumed cardiac
aetiology. These models can be useful for detecting patients who could have been potentially saved. Keywords: Out-of-hospital cardiac arrest |Resuscitation | Outcome prediction | Machine learning |
مقاله انگلیسی |
5 |
Perioperative Management of Pregnant Women With Idiopathic Pulmonary Arterial Hypertension: An Observational Case Series Study From China
مدیریت بعد از عمل زنان باردار مبتلا به ایدیوپاتیک ریوی فشار خون شریانی: مطالعه موردی مورد بررسی از چین-2018 Objectives: The mortality of pregnant women with idiopathic pulmonary arterial hypertension (PAH) is very high. There are limited data on the
management of idiopathic PAH during pregnancy. The authors aimed to examine systematically the characteristics of parturient women with
idiopathic PAH, to explore the adverse effects of idiopathic PAH on pregnancy outcomes, and to report the multidisciplinary perioperative
management strategy from the largest comprehensive cardiac hospital in China.
Design: Observational case series study.
Setting: Tertiary referral acute care hospital in Beijing, China.
Participants: The cases of 17 consecutive pregnant idiopathic PAH patients undergoing abortion or parturition at Anzhen Hospital were
reviewed retrospectively.
Interventions: Preoperative characteristics, anesthesia method, intensive care management, PAH-specific therapy, and maternal and neonatal
outcomes were analyzed in this case series study.
Measures and Main Results: Maternal and neonatal outcomes were the main measures. The mean ages of the 17 parturient women with
idiopathic PAH were 28.3 7 5.4 years, and the mean systolic pulmonary arterial pressure was 97.9 7 18.6 mmHg. Fifteen patients (88.2%)
received PAH-specific therapy before delivery, including sildenafil, iloprost, and treprostinil. All except 1 parturient received epidural anesthesia
for surgery due to an emergency Caesarean section. Three patients experienced pulmonary hypertension crisis that necessitated conversion to
general anesthesia. Ten parturients underwent Caesarean delivery at a median gestational age of 31 weeks. Three patients developed acute
pulmonary hypertensive crisis intraoperatively. Two patients underwent cardiopulmonary resuscitation and extracorporeal membrane
oxygenation support. The maternal mortality was 17.6% (3/17). Of the 10 delivered neonates, 9 (90.0%) survived.
Conclusions: The maternal mortality of the idiopathic PAH parturient was high in this case series from China. The authors applied epidural
anesthesia, early management with multidisciplinary approaches, PAH-specific therapy, avoidance of oxytocin, and timely delivery or pregnancy
termination to improve maternal and neonatal outcomes.
Key Words: idiopathic pulmonary arterial hypertension; outcome; pregnancy |
مقاله انگلیسی |
6 |
Toxic shock syndrome – the seven Rs of management and treatment
سندرم شوک سمی - هفت روایت از مدیریت و درمان-2017 Staphylococcal and streptococcal toxic shock syndrome (TSS) are associated with
significant morbidity and mortality. There has been considerable progress in understanding
the pathophysiology and delineating optimal management and treatment. This article reviews
the management of TSS, outlining the ‘Seven Rs of Managing and Treating TSS’: Recognition,
Resuscitation, Removal of source of infection, Rational choice of antibiotics, Role of adjunctive
treatment (clindamycin and intravenous immunoglobulin), Review of progress and Reduce risk
of secondary cases in close contacts.
KEYWORDS : Staphylococcus | aureus | Streptococcus | pyogenes | Superantigen | Clindamycin | Intravenous | immunoglobulin |Contact prophylaxis |
مقاله انگلیسی |