Basic Vital Signs Research Articles

Curcumin-mediated enhancement of lung barrier function in rats with high-altitude-associated acute lung injury via inhibition of inflammatory response

BackgroundExposure to a hypobaric hypoxic environment at high altitudes can lead to lung injury. In this study, we aimed to determine whether curcumin (Cur) could improve lung barrier function and protect against high-altitude-associated acute lung injury.MethodsTwo hundred healthy rats were randomly divided into standard control, high-altitude control (HC), salidroside (40 mg/kg, positive control), and Cur (200 mg/kg) groups. Each group was further divided into five subgroups. Basic vital signs, lung injury histopathology, routine blood parameters, plasma lactate level, and arterial blood gas indicators were evaluated. Protein and inflammatory factor (tumor necrosis factor α (TNF-α), interleukin [IL]-1β, IL-6, and IL-10) concentrations in bronchoalveolar lavage fluid (BALF) were determined using the bicinchoninic acid method and enzyme-linked immunosorbent assay, respectively. Inflammation-related and lung barrier function-related proteins were analyzed using immunoblotting.ResultsCur improved blood routine indicators such as hemoglobin and hematocrit and reduced the BALF protein content and TNF-α, IL-1β, and IL-6 levels compared with those in the HC group. It increased IL-10 levels and reduced pulmonary capillary congestion, alveolar hemorrhage, and the degree of pulmonary interstitial edema. It increased oxygen partial pressure, oxygen saturation, carbonic acid hydrogen radical, and base excess levels, and the expression of zonula occludens 1, occludin, claudin-4, and reduced carbon dioxide partial pressure, plasma lactic acid, and the expression of phospho-nuclear factor kappa.ConclusionsExposure to a high-altitude environment for 48 h resulted in severe lung injury in rats. Cur improved lung barrier function and alleviated acute lung injury in rats at high altitudes.

Effectiveness of Remimazolam Benzenesulfonate Combined with Alfentanil in Elderly Patients Undergoing Painless Gastroenteroscopy

Objective: To observe the effects of remimazolam benzenesulfonate combined with alfentanil during painless gastroenteroscopy in elderly patients. Methods: This study analyzes patients aged 60–85 years old undergoing painless gastroenteroscopy. A total of 140 patients, examined between February 2023 and February 2024, voluntarily participated and were randomly divided into an experimental group and a control group. The control group received alfentanil combined with propofol for anesthesia, while the experimental group received alfentanil combined with remimazolam benzenesulfonate. The relevant indices of both groups were separately analyzed. Results: Patients in the experimental group had a shorter awakening time, a faster discharge rate (P < 0.05), and a shorter examination duration; however, the difference in examination time between the two groups was not statistically significant (P > 0.05). Before anesthesia, there was no significant difference in the basic information and vital signs of the two groups (P > 0.05). Two minutes after anesthesia, both groups showed a decline in vital signs, but the vital signs of the experimental group remained more stable after the procedure, with the group’s indices showing improvement over the control group (P < 0.05). Additionally, the incidence of adverse reactions in the experimental group was lower than in the control group (P < 0.05). Conclusion: In painless gastroenteroscopy for elderly patients, anesthesia using a combination of remimazolam benzenesulfonate and alfentanil improves anesthesia effectiveness, hastens patient recovery, enhances the stability of vital signs, and effectively controls adverse reactions, thereby improving patient comfort.

Malignant tumor increases the risk of all causes in-hospital mortality in critically ill patients with ischemic stroke: analysis of the MIMIC-IV database.

Ischemic stroke (IS) and malignant tumor (MT) have high morbidity and mortality rates worldwide, and several associations exist between them. This study aimed to determine the effect of MT on hospital mortality in patients with IS. Based on their MT status, participants with IS in the Medical Information Mart for Intensive Care IV (MIMIC-IV) were divided into two groups. The primary outcome was in-hospital all causes mortality. Kaplan-Meier survival analysis was performed to evaluate the intergroup in-hospital mortality, and three Cox regression models were used to determine the association between MT and in-hospital mortality. A total of 1605 participants (749 males and 856 females) were included in the study. The mean age was 72.030 ± 15.463 years. Of these, 257 (16%) patients died in the hospital. Kaplan-Meier analysis showed that the MT group had a significantly lower possibility of in-hospital survival than the non-MT group. In the unadjusted model, in-hospital mortality among MT patients had a higher odds ratio (OR) of 1.905 (95% CI, 1.320-2.748; P < 0.001) than the non-MT group. After adjusting for basic information, vital signs, and laboratory data, MT was also associated with increased in-hospital mortality (OR = 1.844, 95% CI: 1.255-2.708; P = 0.002). Among the patients with IS, the risk of all causes in-hospital mortality was higher for MT than for patients non-MT. This finding can assist clinicians in more accurately assessing prognosis and making informed treatment decisions.

Chinese medicine Jianpi Antai formula improves pregnancy outcomes of in vitro fertilization-embryo transfer in infertile women.

To evaluate the efficacy and safety of Chinese medicine Jianpi Antai formula in infertile women undergoing in vitro fertilization-embryo transfer (IVF-ET). A total of 300 infertile women who underwent 2 frozen embryo transfer procedures at the Reproductive Medicine Center, Sir Run Run Shaw Hospital were included in the study. The participants were randomly divided into study group and control group. The study group received routine medication plus the Jianpi Antai formula during the period of embryo transfer, while the control group received routine medication only. The general condition, embryo implantation rate, clinical pregnancy rate, live birth rate, and the blood routine and liver and kidney function were evaluated and compared between two groups. There were 277 cases who completed the study, including 134 in the study group and 143 in the control group. The embryo implantation rate (68.7% vs. 55.9%), the clinical pregnancy rate (56.7% vs. 44.8%) and the live birth rate (50.7% vs. 37.8%) in the study group were all higher than those in the control group (all P<0.05). Subgroup analysis revealed that in patients of advanced age (≥35 years) and those with decreased ovarian reserve function (anti-Müllerian hormone <1.68 ng/mL), the embryo implantation rate, clinical pregnancy rate, and live birth rate in the study group were all higher than those in the control group (all P<0.05). During the follow-up period, there were no abnormalities in the basic vital signs of both groups, and no adverse events were reported. Jianpi Antai formula can safely improve the embryo implantation rate in infertile women undergoing IVF-ET, reduce the embryo miscarriage rate, increase the live birth rate as well as improve the clinical outcomes.

Application of intranasal dexmedetomidine in magnetic resonance imaging of preterm infants: The ED50, efficacy and safety analysis.

Infants undergoing magnetic resonance imaging (MRI) often require pharmacological sedation. Dexmedetomidine serves as a novel sedative agent that induces a unique unconsciousness similar to natural sleep, and therefore has currently been used as the first choice for sedation in infants and young children. To determine the 50% effective dose (ED50) and 95% confidence interval (95%CI) of intranasal dexmedetomidine for MRI in preterm and term infants, and to observe the incidence of adverse events. To explore whether there were differences in ED50 and 95%CI, heart rate (HR) and blood oxygen saturation (SpO2), the induction time and wake-up time and the incidence of adverse events between the 2 groups, so as to provide guidance for clinical safe medication for the meanwhile. A total of 68 infants were prospectively recruited for MRI examination under drug sedation (1 week ≤ age ≤ 23 weeks or weight ≤ 5kg). The children were divided into 2 groups according to whether they had preterm birth experience (Preterm group, Atterm group). The Dixon up-and-down method was used to explore ED50. The basic vital signs of the 2 groups were recorded, and the heart rate and SpO2 were recorded every 5 minutes until the infants were discharged from the hospital. The induction time, wake-up time and adverse events were recorded. The ED50 (95%CI) of intranasal dexmedetomidine in the Preterm group and the Atterm group were 2.23 (2.03-2.66) μg/kg and 2.64 (2.49-2.83) μg/kg, respectively (P < .05). the wake-up time was longer in Preterm group (98.00min) than in Atterm group (81.00 min) (P < .05), the incidence of bradycardia in Preterm group was 3/33, which was higher than that in Atterm group (1/35). There was no difference in the induction time between the 2 groups (P > .05), and there was no significant difference in other adverse events. Intranasal dexmedetomidine can be safely used for sedation in preterm infants undergoing MRI. Compared with term infants, preterm infants have a lower dose of dexmedetomidine, a higher incidence of bradycardia, and a longer weak-up time.

Constructing a predictive model for the death risk of patients with septic shock based on supervised machine learning algorithms

To construct and validate the best predictive model for 28-day death risk in patients with septic shock based on different supervised machine learning algorithms. The patients with septic shock meeting the Sepsis-3 criteria were selected from Medical Information Mart for Intensive Care-IV v2.0 (MIMIC-IV v2.0). According to the principle of random allocation, 70% of these patients were used as the training set, and 30% as the validation set. Relevant predictive variables were extracted from three aspects: demographic characteristics and basic vital signs, serum indicators within 24 hours of intensive care unit (ICU) admission and complications possibly affecting indicators, functional scoring and advanced life support. The predictive efficacy of models constructed using five mainstream machine learning algorithms including decision tree classification and regression tree (CART), random forest (RF), support vector machine (SVM), linear regression (LR), and super learner [SL; combined CART, RF and extreme gradient boosting (XGBoost)] for 28-day death in patients with septic shock was compared, and the best algorithm model was selected. The optimal predictive variables were determined by intersecting the results from LASSO regression, RF, and XGBoost algorithms, and a predictive model was constructed. The predictive efficacy of the model was validated by drawing receiver operator characteristic curve (ROC curve), the accuracy of the model was assessed using calibration curves, and the practicality of the model was verified through decision curve analysis (DCA). A total of 3 295 patients with septic shock were included, with 2 164 surviving and 1 131 dying within 28 days, resulting in a mortality of 34.32%. Of these, 2 307 were in the training set (with 792 deaths within 28 days, a mortality of 34.33%), and 988 in the validation set (with 339 deaths within 28 days, a mortality of 34.31%). Five machine learning models were established based on the training set data. After including variables at three aspects, the area under the ROC curve (AUC) of RF, SVM, and LR machine learning algorithm models for predicting 28-day death in septic shock patients in the validation set was 0.823 [95% confidence interval (95%CI) was 0.795-0.849], 0.823 (95%CI was 0.796-0.849), and 0.810 (95%CI was 0.782-0.838), respectively, which were higher than that of the CART algorithm model (AUC = 0.750, 95%CI was 0.717-0.782) and SL algorithm model (AUC = 0.756, 95%CI was 0.724-0.789). Thus above three algorithm models were determined to be the best algorithm models. After integrating variables from three aspects, 16 optimal predictive variables were identified through intersection by LASSO regression, RF, and XGBoost algorithms, including the highest pH value, the highest albumin (Alb), the highest body temperature, the lowest lactic acid (Lac), the highest Lac, the highest serum creatinine (SCr), the highest Ca2+, the lowest hemoglobin (Hb), the lowest white blood cell count (WBC), age, simplified acute physiology score III (SAPS III), the highest WBC, acute physiology score III (APS III), the lowest Na+, body mass index (BMI), and the shortest activated partial thromboplastin time (APTT) within 24 hours of ICU admission. ROC curve analysis showed that the Logistic regression model constructed with above 16 optimal predictive variables was the best predictive model, with an AUC of 0.806 (95%CI was 0.778-0.835) in the validation set. The calibration curve and DCA curve showed that this model had high accuracy and the highest net benefit could reach 0.3, which was significantly outperforming traditional models based on single functional score [APS III score, SAPS III score, and sequential organ failure assessment (SOFA) score] with AUC (95%CI) of 0.746 (0.715-0.778), 0.765 (0.734-0.796), and 0.625 (0.589-0.661), respectively. The Logistic regression model, constructed using 16 optimal predictive variables including pH value, Alb, body temperature, Lac, SCr, Ca2+, Hb, WBC, SAPS III score, APS III score, Na+, BMI, and APTT, is identified as the best predictive model for the 28-day death risk in patients with septic shock. Its performance is stable, with high discriminative ability and accuracy.

Documentation Completeness and Nurses' Perceptions of a Novel Electronic App for Medical Resuscitation in the Emergency Room: Mixed Methods Approach.

Complete documentation of critical care events in the accident and emergency department (AED) is essential. Due to the fast-paced and complex nature of resuscitation cases, missing data is a common issue during emergency situations. This study aimed to evaluate the impact of a tablet-based resuscitation record on documentation completeness during medical resuscitations and nurses' perceptions of the use of the tablet app. A mixed methods approach was adopted. To collect quantitative data, randomized retrospective reviews of paper-based resuscitation records before implementation of the tablet (Pre-App Paper; n=176), paper-based resuscitation records after implementation of the tablet (Post-App Paper; n=176), and electronic tablet-based resuscitation records (Post-App Electronic; n=176) using a documentation completeness checklist were conducted. The checklist was validated by 4 experts in the emergency medicine field. The content validity index (CVI) was calculated using the scale CVI (S-CVI). The universal agreement S-CVI was 0.822, and the average S-CVI was 0.939. The checklist consisted of the following 5 domains: basic information, vital signs, procedures, investigations, and medications. To collect qualitative data, nurses' perceptions of the app for electronic resuscitation documentation were obtained using individual interviews. Reporting of the qualitative data was guided by Consolidated Criteria for Reporting Qualitative Studies (COREQ) to enhance rigor. A significantly higher documentation rate in all 5 domains (ie, basic information, vital signs, procedures, investigations, and medications) was present with Post-App Electronic than with Post-App Paper, but there were no significant differences in the 5 domains between Pre-App Paper and Post-App Paper. The qualitative analysis resulted in main categories of "advantages of tablet-based documentation of resuscitation records," "challenges with tablet-based documentation of resuscitation records," and "areas for improvement of tablet-based resuscitation records." This study demonstrated that higher documentation completion rates are achieved with electronic tablet-based resuscitation records than with traditional paper records. During the transition period, the nurse documenters faced general problems with resuscitation documentation such as multitasking and unique challenges such as software updates and a need to familiarize themselves with the app's layout. Automation should be considered during future app development to improve documentation and redistribute more time for patient care. Nurses should continue to provide feedback on the app's usability and functionality during app refinement to ensure a successful transition and future development of electronic documentation records.

Modified Harborview Risk Score accurately predicts mortality for patients with ruptured abdominal aortic aneurysm

ObjectiveThe modified Harborview Risk Score (HRS) is a simple measure initially derived from a single institutional dataset used to predict ruptured abdominal aortic aneurysm (rAAA) repair survival preoperatively using basic labs and vital signs collected upon presentation. However, validation of this widely applicable scoring system has not been performed. This study aims to validate this scoring system using a large multi-institutional database. MethodsAll patients who underwent repair of an rAAA from 2011 to 2018 in the National Surgical Quality Improvement Program (NSQIP) and at a single academic medical center were included. The modified HRS was calculated by assigning 1 point for each of the following: age >76 years, creatinine >2 mg/dL, international normalized ratio >1.8, and any systolic blood pressure less than 70 mmHg. Assessment of the prediction model was then completed. Using a primary outcome measure of 30-day mortality, the receiver operating characteristic area under the curve was calculated. The discrimination between datasets was compared using a Delong test. Mortality rates for each score were compared between datasets using the Pearson χ2 test. Comparative analysis for patients with a score of 4 was limited due to a small sample size. ResultsA total of 1536 patients were identified using NSQIP, and 163 patients were assessed in the institutional dataset. There were 518 patients with a score of 0 (455 NSQIP, 63 institutional), 676 patients with a score of 1 (617 NSQIP, 59 institutional), 391 patients with a score of 2 (364 NSQIP, 27 institutional), 106 with a score of 3 (93 NSQIP, 13 institutional), and 8 patients with a score of 4 (7 NSQIP, 1 institutional). No difference was found in the receiver operating characteristic area under the curves between datasets (P = .78). Thirty-day mortality was 10% NSQIP vs 22% institutional for a score of 0; 28% NSQIP vs 36% institutional for a score of 1; 41% NSQIP vs 44% institutional for a score of 2; 45% NSQIP vs 69% institutional for a score of 3; and 57% NSQIP vs 100% institutional for a score of 4. Score 0 was the only score with a significant mortality rate difference between datasets (P = .01). ConclusionsThe modified HRS is confirmed to be broadly applicable as a clinical decision-making tool for patients presenting with rAAAs. Therefore, this easily applicable model should be applied for all patients presenting with rAAAs to assist with provider and patient decision-making prior to proceeding with repair.

Creating a Pathway to Health-Care Professions for Historically Marginalized Students.

Developing a diverse talent pool starting at the high-school level, while students are making future education and career decisions, should be a national priority, given the need to build a diverse health-care workforce. This article describes a 6-week immersive simulation-based summer program to introduce 20 junior high-school students (13-15 years old) to the range of health professions. Because precollege students typically receive limited exposure to clinical settings, high-fidelity simulation is an excellent surrogate for providing realistic experiences in health care. Students heard lectures on health careers, earned basic life support certification, learned to perform basic vital signs measurement, practiced the management of acute asthma, and received an introduction to the daily activities in the life of a nurse and an anesthesiologist. They researched, developed, and presented public service announcement videos about a health-care issue affecting their community. Participants reported being excited about health careers. Future programs will focus on longitudinal support and mentoring, essential for mitigating the higher rates of attrition from health professions among minoritized individuals.

Deep Learning-Based Early Warning Score for Predicting Clinical Deterioration in General Ward Cancer Patients.

Cancer patients who are admitted to hospitals are at high risk of short-term deterioration due to treatment-related or cancer-specific complications. A rapid response system (RRS) is initiated when patients who are deteriorating or at risk of deteriorating are identified. This study was conducted to develop a deep learning-based early warning score (EWS) for cancer patients (Can-EWS) using delta values in vital signs. A retrospective cohort study was conducted on all oncology patients who were admitted to the general ward between 2016 and 2020. The data were divided into a training set (January 2016-December 2019) and a held-out test set (January 2020-December 2020). The primary outcome was clinical deterioration, defined as the composite of in-hospital cardiac arrest (IHCA) and unexpected intensive care unit (ICU) transfer. During the study period, 19,739 cancer patients were admitted to the general wards and eligible for this study. Clinical deterioration occurred in 894 cases. IHCA and unexpected ICU transfer prevalence was 1.77 per 1000 admissions and 43.45 per 1000 admissions, respectively. We developed two models: Can-EWS V1, which used input vectors of the original five input variables, and Can-EWS V2, which used input vectors of 10 variables (including an additional five delta variables). The cross-validation performance of the clinical deterioration for Can-EWS V2 (AUROC, 0.946; 95% confidence interval [CI], 0.943-0.948) was higher than that for MEWS of 5 (AUROC, 0.589; 95% CI, 0.587-0.560; p < 0.001) and Can-EWS V1 (AUROC, 0.927; 95% CI, 0.924-0.931). As a virtual prognostic study, additional validation was performed on held-out test data. The AUROC and 95% CI were 0.588 (95% CI, 0.588-0.589), 0.890 (95% CI, 0.888-0.891), and 0.898 (95% CI, 0.897-0.899), for MEWS of 5, Can-EWS V1, and the deployed model Can-EWS V2, respectively. Can-EWS V2 outperformed other approaches for specificities, positive predictive values, negative predictive values, and the number of false alarms per day at the same sensitivity level on the held-out test data. We have developed and validated a deep learning-based EWS for cancer patients using the original values and differences between consecutive measurements of basic vital signs. The Can-EWS has acceptable discriminatory power and sensitivity, with extremely decreased false alarms compared with MEWS.

Outcome Evaluation of Burn Injury Management: A Study of Selective Traditional Home Remedies.

Background Clinicians classify burns as epidermal, partial thickness (superficial and deep), or full thickness, according to the depth of tissue damage. Although skin is considered the largest organ in the human body, studies investigating burns, their types, and their management has revealed that the background knowledge of burn aid the community possesses remains unsatisfactory. Thus, in this study, we aimed to evaluate the effect of various traditional home remedies, taking into account the type of burns and the nature of the remedies usedfrom a cosmetic point of view. Materials and methods This is an original retrospective study conducted at Dr. Soliman Fakeeh Hospital in Jeddah from June through December 2022. Using the Vancouver Scar Scale (VSS), eligible patients who met our inclusion criteria were invited to participate in the study after a review of their patient history, an assessment of basic vital signs, and a physical examination. Results Fifty-two participants met our inclusion criteria and successfully completed the study. A total of 80 wounds of varying severity in various locations were evaluated. Participants were divided into three categories according to VSS scores indicating good, intermediate, or poor healing. None of the eight cases treated with water resulted in poor healing. However, tomato paste resulted in poor healing for six cases (60%) but moderate and good healing for two cases (20%). Conclusion The safest and most effective initial management for burns among all the reviewed remedies was the application of cool running water, followed by seeking medical attention for evaluation and proper treatment, whereas tomato paste had a markedly poor effect.

Establishment of a Rat Model of Capillary Leakage Syndrome Induced by Cardiopulmonary Resuscitation After Cardiac Arrest.

Cardiopulmonary resuscitation (CPR) after cardiac arrest (CA) is one of the main causes of capillary leakage syndrome (CLS). This study aimed to establish a stable CLS model following the CA and cardiopulmonary resuscitation (CA-CPR) model in Sprague-Dawley (SD) rats. We conducted a prospective, randomized, animal model study. All adult male SD rats were randomly divided into a normal group (group N), a sham operation group (group S), and a cardiopulmonary resuscitation group (group T). The SD rats of the three groups were all inserted with 24-G needles through their left femoral arteries and right femoral veins. In group S and group T, the endotracheal tube was intubated. In group T, CA induced by asphyxia (AACA) was caused by vecuronium bromide with the endotracheal tube obstructed for 8 min, and the rats were resuscitated with manual chest compression and mechanical ventilation. Preresuscitation and postresuscitation measurements, including basic vital signs (BVS), blood gas analysis (BG), routine complete blood count (CBC), wet-to-dry ratio of tissues (W/D), and the HE staining results after 6 h were evaluated. In group T, the success rate of the CA-CPR model was 60% (18/30), and CLS occurred in 26.6% (8/30) of the rats. There were no significant differences in the baseline characteristics, including BVS, BG, and CBC, among the three groups (P>0.05). Compared with pre-asphyxia, there were significant differences in BVS, CBC, and BG, including temperature, oxygen saturation (SpO2), mean arterial pressure (MAP), central venous pressure (CVP), white blood cell count (WBC), hemoglobin, hematocrit, pH, pCO2, pO2, SO2, lactate (Lac), base excess (BE), and Na+ (P<0.05) after the return of spontaneous circulation (ROSC) in group T. At 6 h after ROSC in group T and at 6 h after surgery in groups N and S, there were significant differences in temperature, heart rate (HR), respiratory rate (RR), SpO2, MAP, CVP, WBC, pH, pCO2, Na+, and K+ among the three groups (P<0.05). Compared with the other two groups, the rats in group T showed a significantly increased W/D weight ratio (P<0.05). The HE-stained sections showed consistent severe lesions in the lung, small intestine, and brain tissues of the rats at 6 h after ROSC following AACA. The CA-CPR model in SD rats induced by asphyxia could reproduce CLS with good stability and reproducibility.

Design of Basic Vital Signs Measurement Tool And Dehydration Early Detection in Human Body

A tool has been made to monitor vital signs such as heart rate, oxygen saturation in the blood, and body temperature based on a microcontroller which is based on dehydration conditions where the body loses more fluid than the amount of fluid it enters. Parameters for carrying out this detection include heart rate, blood oxygen saturation (SpO2), body temperature and urine color. The targets of this research are (a) making a prototype, (b) programming the system with the help of the Arduino IDE, and (c) measuring system performance. The research method starts from making a prototype and measuring system performance. The results of measuring the performance of the tool show that the error for measuring heart rate is 1.28%, measuring blood oxygen saturation (SpO2) is 0.51%, and measuring body temperature is 1.729%. However, for the dehydration detection test from 5 test samples, the results showed a success percentage of 60% with an average error of 40%. Overall the tool can function well

Liver Enzyme Correlated with C-Reactive Protein as a Biomarker of Metabolic Syndrome in Elderly Patients

Metabolic syndrome is a multifactorial disease with various risk factors that arise from insulin resistance associated with obesity, diabetes, hypertension, and dyslipidemias. Objective: To investigate the association between C-Reactive Protein and various risk factors related to CVS and metabolic health in elderly adults. Increased levels of various liver enzymes were found to be indicative of the progression of metabolic syndrome. Methods: In this study, total of 150 individuals with diagnosed metabolic syndrome were included, and they were compared to an equal number of control cases. The participants were selected from individuals visiting the Pathology and Molecular Biology Laboratories in Karachi for lab investigations. before collecting blood samples, basic vital signs, blood pressure, height, and waist circumference measurements were recorded to determine the participants' body mass index. The blood samples were then taken to assess liver enzyme levels and CRP. Results: The study examined and compared the levels of inflammatory marker and liver enzymes in both cases of metabolic syndrome (MetS) and the control group. The findings revealed a significant association (p-value of 0.001) between metabolic syndrome and elevated levels of liver enzymes and CRP. Specifically, the suspected cases of metabolic syndrome showed strong correlations with alanine aminotransferase, gamma-glutamyl transferase, and aspartate aminotransferase. However, alkaline phosphatase did not show substantial differences between the two groups. Conclusions: Our research revealed correlation between metabolic syndrome, liver enzymes and CRP. These findings indicate that high levels of liver enzymes and CRP can be indicative of cardiovascular functional abnormalities in elderly patients with metabolic syndrome.

One hundred cases of primary spontaneous pneumomediastinum: leukocytosis is common, pleural effusions and age over 40 are rare.

Primary spontaneous pneumomediastinum (PSPM) is a benign condition, but it can be difficult to discriminate from Boerhaave syndrome. The diagnostic difficulty is attributable to a shared constellation of history, signs, and symptoms combined with a poor understanding of the basic vital signs, labs, and diagnostic findings characterizing PSPM. These challenges likely contribute to high resource utilization for diagnosis and management of a benign process. Patients aged 18 years or older with PSPM were identified from our radiology department's database. A retrospective chart review was performed. Exactly 100 patients with PSPM were identified between March 2001 and November 2019. Demographics and histories correlated well with prior studies: mean age (25 years); male predominance (70%); association with cough (34%), asthma (27%), retching or emesis (24%), tobacco abuse (11%), and physical activity (11%); acute chest pain (75%), and dyspnea (57%) as the first and second most frequent symptoms and subcutaneous emphysema (33%) as the most common sign. We provide the first robust data on presenting vital signs and laboratory values of PSPM, showing that tachycardia (31%) and leukocytosis (30%) were common. No pleural effusion was found in the 66 patients who underwent computed tomography (CT) of the chest. We provide the first data on inter-hospital transfer rates (27%). 79% of transfers were due to concern for esophageal perforation. Most patients were admitted (57%), with an average length of stay (LOS) of 2.3 days, and 25% received antibiotics. PSPM patients frequently present in their twenties with chest pain, subcutaneous emphysema, tachycardia, and leukocytosis. Approximately 25% have a history of retching or emesis and it is this population that must be discriminated from those with Boerhaave syndrome. An esophagram is rarely indicated and observation alone is appropriate in patients under age 40 with a known precipitating event or risk factors for PSPM (e.g., asthma, smoking) if they have no history of retching or emesis. Fever, pleural effusion, and age over 40 are rare in PSPM and should raise concern for esophageal perforation in a patient with a history of retching, emesis, or both.

Integrating Structured and Unstructured EHR Data for Predicting Mortality by Machine Learning and Latent Dirichlet Allocation Method.

An ICU is a critical care unit that provides advanced medical support and continuous monitoring for patients with severe illnesses or injuries. Predicting the mortality rate of ICU patients can not only improve patient outcomes, but also optimize resource allocation. Many studies have attempted to create scoring systems and models that predict the mortality of ICU patients using large amounts of structured clinical data. However, unstructured clinical data recorded during patient admission, such as notes made by physicians, is often overlooked. This study used the MIMIC-III database to predict mortality in ICU patients. In the first part of the study, only eight structured variables were used, including the six basic vital signs, the GCS, and the patient's age at admission. In the second part, unstructured predictor variables were extracted from the initial diagnosis made by physicians when the patients were admitted to the hospital and analyzed using Latent Dirichlet Allocation techniques. The structured and unstructured data were combined using machine learning methods to create a mortality risk prediction model for ICU patients. The results showed that combining structured and unstructured data improved the accuracy of the prediction of clinical outcomes in ICU patients over time. The model achieved an AUROC of 0.88, indicating accurate prediction of patient vital status. Additionally, the model was able to predict patient clinical outcomes over time, successfully identifying important variables. This study demonstrated that a small number of easily collectible structured variables, combined with unstructured data and analyzed using LDA topic modeling, can significantly improve the predictive performance of a mortality risk prediction model for ICU patients. These results suggest that initial clinical observations and diagnoses of ICU patients contain valuable information that can aid ICU medical and nursing staff in making important clinical decisions.

Natural Language Processing Applications for Computer-Aided Diagnosis in Oncology.

In the era of big data, text-based medical data, such as electronic health records (EHR) and electronic medical records (EMR), are growing rapidly. EHR and EMR are collected from patients to record their basic information, lab tests, vital signs, clinical notes, and reports. EHR and EMR contain the helpful information to assist oncologists in computer-aided diagnosis and decision making. However, it is time consuming for doctors to extract the valuable information they need and analyze the information from the EHR and EMR data. Recently, more and more research works have applied natural language processing (NLP) techniques, i.e., rule-based, machine learning-based, and deep learning-based techniques, on the EHR and EMR data for computer-aided diagnosis in oncology. The objective of this review is to narratively review the recent progress in the area of NLP applications for computer-aided diagnosis in oncology. Moreover, we intend to reduce the research gap between artificial intelligence (AI) experts and clinical specialists to design better NLP applications. We originally identified 295 articles from the three electronic databases: PubMed, Google Scholar, and ACL Anthology; then, we removed the duplicated papers and manually screened the irrelevant papers based on the content of the abstract; finally, we included a total of 23 articles after the screening process of the literature review. Furthermore, we provided an in-depth analysis and categorized these studies into seven cancer types: breast cancer, lung cancer, liver cancer, prostate cancer, pancreatic cancer, colorectal cancer, and brain tumors. Additionally, we identified the current limitations of NLP applications on supporting the clinical practices and we suggest some promising future research directions in this paper.

AUTOMATION IN EMERGENCY MEDICINE - OUR FUTURE OR THREAT?

Automation in the 21st century is a rapidly evolving phenomenon that leverages advanced technologies, artif i cial intelligence, robotics to enhance the func-tioning of our world and simplify daily lives. The fi rst signs of this progress can also be observed in emergency medicine. The use of drones, which has made it possible to deliver equipment more quickly and safely to remote and hard-to-reach areas, has become an invaluable tool in response to crisis situations. The concept of self-service ambulances, capable of checking a patient’s basic vital signs and transporting them to a hospital, holds the promise of revolutionizing fi rst aid. The use of computer technology, particularly artif i cial intelligence, enables rapid and precise analysis of medical data, signif i cantly supporting the process of patient triage and diagnosis process. Robotic assistants for medical personnel not only have the potential to improve the quality of care but also allow for the performance of precise medical procedures, which can be crucial for critically ill patients. The ongoing automation of emergency medicine is undeniable. Modern technologies open up new perspectives in emergency medicine in terms of both the speed and ef f i ciency of interventions and the qual-ity of care provided to patients. However, to fully harness the potential of these solutions, it is necessary to adequately prepare medical personnel, society, and consider the ethical, legal, and moral aspects associated with their application. This paper analyzes the impact of automation on emergency medicine, with a focus on its potential benef i ts, challenges, and threats to both patients and medical personnel.

Comparison of the Efficacy and Safety of Adamgammadex with Sugammadex for Reversal of Rocuronium-Induced Neuromuscular Block: Results of a Phase II Clinical Trial

This current phase II clinical trial was to compare the effect and safety of adamgammadex, a new cyclodextrin-based selective relaxant binding agent, with sugammadex to reverse rocuronium-induced neuromuscular block. Patients were randomised to receive adamgammadex (4 or 6 mg kg-1) or sugammadex (2 mg kg-1, as a positive control group) at the reappearance of the second twitch (T2) in response to TOF stimulation. The standard safety data were collected. The 4 mg kg-1 (n = 16) and 6 mg kg-1 (n = 20) adamgammadex- and 2 mg kg-1 (n = 20) sugammadex-induced recovery time of TOF ratio to 0.9 were 2.3, 1.6, and 1.5 min, respectively (p = 0.49). The 4 mg kg -1 adamgammadex-induced median recovery time was longer than that of 2 mg kg-1 sugammadex (p = 0.01), and there was no difference between the 6 mg kg -1 adamgammadex group and 2 mg kg-1 sugammadex group (p = 0.32). Then, the number of patients who experienced adverse events (AEs) was 6, 11, and 14 for adamgammadex at 4, 6 mg kg-1 and sugammadex at 2 mg kg-1, respectively. The treatment emergent AEs that occurred more than twice were detailed as follows: incision site pain, hypotension, emesis, fever, throat pain, blood bilirubin increase, abnormal T-wave of ECG, dizziness, incision site swelling, postoperative fever, expectoration, and nausea. For drug-related AEs, the increased urine acetone bodies and first-degree atrioventricular block were observed in two patients from sugammadex group. Then, the previously reported AEs were not observed in this study, including anaphylaxis, haemorrhage, recurarization, abnormal basic vital signs, or lengthened QRS intervals and QT intervals. Adamgammadex was found to be effective for reversal of rocuronium-induced neuromuscular block as sugammadex.

AdipoRon Inhibits Neuroinflammation Induced by Deep Hypothermic Circulatory Arrest Involving the AMPK/NF-κB Pathway in Rats.

Deep hypothermic circulatory arrest (DHCA) can induce systemic inflammatory response syndrome, including neuroinflammation. Finding suitable compounds is necessary for attenuating neuroinflammation and avoiding cerebral complications following DHCA. In the present study, we established DHCA rat models and monitored the vital signs during the surgical process. After surgery, we found significantly increased proinflammatory cytokines (IL-6, IL-1β, and TNF-α) in DHCA rats. Quantitative proteomics analysis was performed for exploring the differentially expressed proteins in hippocampus of DHCA rats and the data showed the adiponectin receptor 1 protein was upregulated. More importantly, administration of AdipoRon, a small-molecule adiponectin receptor agonist, could improve the basic vital signs and attenuate the increased IL-6, IL-1β, and TNF-α in DHCA rats. Furthermore, AdipoRon inhibits the activation of microglia (M1 state) and promotes their transition to an anti-inflammatory state, via promoting the phosphorylation of adenosine monophosphate-activated protein kinase (AMPK), and downregulating nuclear factor kappa B (NF-κB) in DHCA rats. Consistently, we used LPS-treated BV2 cells to mimic the neuroinflammatory condition and found that AdipoRon dose-dependently decreased cytokines, along with increased phosphorylation of AMPK and downregulated NF-κB. In conclusion, our present data supported that AdipoRon inhibited DHCA-induced neuroinflammation via activating the hippocampal AMPK/NF-κB pathway.