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Acidosis, Metabolic

Metabolic Acidosis is a condition characterized by an imbalance in the body's acid-base balance, resulting in an abnormal increase in acidity.
This can occur due to a variety of underlying causes, such as kidney dysfunction, diabetes, or excessive production of certain acids.
Symptoms may include nausea, fatigue, and rapid breathing.
Accurate diagnosis and effective management of Metabolic Acidosis is crucial for maintaining overall health and preventing complications.
Researchers can explore the latest literature, pre-prints, and patents using AI-powered tools like PubCompare.ai to optimize their research protocols and advance their understanding of this complex metabolic disorder.

Most cited protocols related to «Acidosis, Metabolic»

1
Cardiorespiratory Fitness Assessment Protocol
Height and body weight were measured to the nearest 0.5 cm and 0.1 kg, respectively, with participant's wearing no shoes and light clothes. The exercise test was performed during daytime by walking and running on a treadmill using a modified Balke protocol [22] (link). Four minutes of warm-up were performed with the treadmill speed set at 4.8 km·h–1 and inclination set at 4%. For participants who were older than 55 years or were obese, the speed was set at 3.8 km·h–1. The inclination was then increased each 60 s by 2%, up to a 20% inclination. If the participant was still able to continue, the speed was further increased by 0.5 km·h–1 until exhaustion. Gas exchange and ventilatory variables were measured continuously as the subjects breathed into a Hans Rudolph two-way breathing mask (2700 series; Hans Rudolph Inc., Shawnee, KS, USA). During the last part of the test, the subject's effort was largely encouraged by the technician until voluntary termination. The rating of perceived exertion was obtained using the Borg Scale6–20[23] (link). A capillary blood sample was taken 60 s after termination of the exercise test and analyzed for blood lactate concentration using hemolyzed blood (Lactate Pro; KDK Corporation, Kyoto, Japan; or ABL 800; Radiometer Medical, Copenhagen, Denmark).
The gas analyzers used were daily volume- and gas calibrated corrected for barometric pressure, temperature and humidity. A detailed descriptions regarding measurement accuracy between gas analyzers is given elsewhere [14] . The gas-exchange variables were reported as 30 s averages. HR was recorded each minute using a Polar Sports Watch (Kempele, Finland) or 12-lead ECG. The highest VO2max during 30 s stage was used, and the highest RER measured before or corresponding to the last 30 s stage was reported. A plateau in VO2 was defined as any two 30-sec VO2 values in which the second was not higher than the first, provided increase in ventilation at maximal effort. Participants who did not exhibit an increase in ventilation despite achievement of a plateau were not accepted. This to ensure that the subject had reached the respiratory compensation point caused by metabolic acidosis.
The different end criteria used to study the impact on VO2max were VO2 leveling off, RERmax ≥1.0, 1.10, and 1.15, blood lactate concentration ≥6.0 and 8.0 mmol•L–1, Borg Scale6–20 rating, and HRmax ≥95% of the age-predicted HRmax (220– age) compared with symptom-limiting termination of the test.
Edvardsen E., Hem E, & Anderssen S.A. (2014). End Criteria for Reaching Maximal Oxygen Uptake Must Be Strict and Adjusted to Sex and Age: A Cross-Sectional Study. PLoS ONE, 9(1), e85276.
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Publication 2014
Acidosis, Metabolic BLOOD Body Weight Capillaries Electrocardiography, 12-Lead Exercise Tests Humidity Lactates Light Obesity Pressure Respiratory Rate
2
NAFLD Treatment in Children Protocol

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Publication 2008
Acidosis, Metabolic Biopsy Child Diabetes Mellitus Diagnosis Ethics Committees, Research HIV Infections Kidney Failure Legal Guardians Liver Liver Cirrhosis Liver Diseases Metformin Non-alcoholic Fatty Liver Disease Nonalcoholic Steatohepatitis Operative Surgical Procedures Parent Parenteral Nutrition Pharmaceutical Preparations Placebos Short Bowel Syndrome Vitamin E Youth
3
Hyperoxia Impact on Post-Cardiac Arrest Outcomes
Categorical variables were compared using the chi-square test. Continuous variables were compared using student t-test or Wilcoxon rank-sum test, based on the distribution of the data. We used Spearman’s correlation coefficient (r) to assess the relationship between 1- and 6-hour PaO2, and the corresponding FiO2 and SaO2. We used multivariable logistic regression analysis to identify what patient and management characteristics were associated with hyperoxia (see Supplemental Methods).
For the primary outcome, we calculated relative risk (RR) using multivariable generalized linear regression with a log link33 (link) to test if exposure to hyperoxia during the initial six hours after ROSC was an independent predictor of poor neurologic function at hospital discharge. We a priori defined hyperoxia as PaO2 > 300 mmHg on one or more ABG analyses, based on previously described definition for hyperoxia.6 (link), 7 (link), 10 (link), 13 (link)A priori, we selected the following candidate variables for the regression model on the grounds that they were previously demonstrated to predict outcome in post–cardiac arrest patients: (1) age (decile); (2) initial cardiac rhythm [asystole or pulseless electrical activity (PEA) vs. ventricular fibrillation/pulseless ventricular tachycardia (VF/VT)];34 (link) (3) metabolic acidosis (defined as one or more recorded base deficit ≤ -6 during the initial six hours after ROSC, based on previously published literature);35 (link) (4) arterial hypotension (mean arterial pressure < 70 mmHg during the initial six hours after ROSC);21 (link) (5) pre-arrest comorbidities (i.e., Charlson comorbidities index);36 (link) (6) prolonged duration of CPR (CPR duration > 20 min);37 (link) and (7) location of cardiac arrest (in- vs. out-of-hospital).38 (link)–41 (link) Backward elimination with a criterion of p < 0.05 for retention in the model was used. Statistical interactions and collinearity were assessed. Goodness of fit of the model was evaluated with the deviance test. This analysis was repeated for both secondary outcomes. For the main analyses listwise deletion was used for missing co-variables. We also report results using multiple imputation for missing co-variables. These models used robust standard error and took into account the random effects at the institution (i.e. site of enrollment) level.
We performed several additional pre-planned sensitivity analyses for the primary outcome. First, we entered additional covariates beyond those pre-specified into a multivariable generalized linear regression model with a log link. Second, we assessed whether cardiac arrest location (pre-hospital or in-hospital) had different results. Finally, we performed a sensitivity analysis limited to only patients who survived to hospital discharge (detailed description of sensitivity analyses is discussed in Supplemental Methods).
We also examined the association between PaO2 and outcome across different thresholds to define hyperoxia (i.e. PaO2 > 100, 150, 200, 250, 300, 350, and 400 mmHg on one or more ABG analyses). We entered each threshold into a multivariable generalized linear regression model with a log link and calculated relative risks with 95% confidence intervals (CI) for poor neurological outcome adjusting for candidate variables retained in the original model. We graphed the relative risks with 95% CI and inspected the graph to assess if there was a threshold signal for neurological outcome over increasing PaO2 cut points.
To reflect the duration of hyperoxia exposure during the initial six hours after ROSC, we used the first PaO2 measurement to represent the PaO2 exposure during the time from ROSC to the first ABG measurement. We then calculated the time intervals between ABG measurements and inferred that the PaO2 remained constant at the level observed in the earlier measurement until the time point of the subsequent measurement (i.e. last value carried forward). Similar methodology to estimate PaO2 exposure has been used previously.16 (link) We then added up the total time patients had exposure to hyperoxia during the initial six hours after ROSC. To test the impact of duration of hyperoxia exposure, we entered duration of exposure as a continuous variable (calibrated for one hour) into a multivariable generalized linear regression model with a log link adjusting for the candidate variables retained in the original model. Given some subjects had ABG analyses ordered by treating physicians in addition to the protocol, we adjusted the model for the total number of ABG analyses obtained during the initial six hours after ROSC, as well as time to first ABG.
Roberts B.W., Kilgannon J.H., Hunter B.R., Puskarich M.A., Pierce L., Donnino M., Leary M., Kline J.A., Jones A.E., Shapiro N.I., Abella B.S, & Trzeciak S. (2018). Association between early hyperoxia exposure after resuscitation from cardiac arrest and neurological disability: A prospective multi-center protocol-directed cohort study. Circulation, 137(20), 2114-2124.
Publication 2018
Acidosis, Metabolic Cardiac Arrest Deletion Mutation Electricity Heart Hyperoxia Hypersensitivity Nervous System Physiological Phenomena Patient Discharge Patients Physicians Retention (Psychology) Student Tachycardia, Ventricular
4
Severe Malaria in Ugandan Children
We conducted a prospective descriptive study within the Paediatric Acute Care Unit (PACU) of Mbale Regional Referral Hospital (MRRH) between 5th May 2011 and 30th April 2012. MRRH is a 470-bed hospital with 95 paediatric beds that is situated in a region of Eastern Uganda with hyper-endemic malaria transmission [8 ]. All children presenting to MRRH with acute illnesses are admitted to the PACU for 24 h, which generally receives approximately 17 000 admissions per year. All children aged 2 months to 12 years who were admitted to PACU between 8:00 am and 5:00 pm from Mondays to Fridays were screened for malaria, and all those who had both a positive P. falciparum blood film and one or more clinical or laboratory features of WHO-defined severe malaria were recruited [6 (link), 9 (link)–12 ] (Table 1).

Criteria used as indicators of severe malaria in the study

Clinical criteriaDefinition
Clinical jaundiceYellow mucous membranes noted in sufficient daylight
Respiratory distressIncreased work of breathing, manifesting as deep, fast or very slow breathing, including retractions and the use of accessory muscles
Severe anaemiaHaemoglobin < 5 g/dL
ProstrationGeneralized weakness so that the patient is unable walk or sit up without assistance
ComaUnrousable state with a corresponding Blantyre Coma Score (BCS) of ≤ 2 for which no other cause other than malaria could be identified
HaemoglobinuriaHistory of or clinician-observed red or cola-coloured urine
Multiple convulsionsMore than two grand-mal seizures during the 24-h period preceding admission
Spontaneous bleedingPhysically un-induced and irrepressible bleeding from at least 2 non-traumatized sites in a patient with severe malaria without previous history of abnormal bleeding
Laboratory criteria
 HyperlactataemiaLactate > 5 mmol/L
 Hyperparasitaemia>5% parasitized erythrocytes or > 250 000 parasites/μL
 HyperpyrexiaAxillary temperature ≥ 40.0°C
 HypoxaemiaOxygen saturation < 90%
 HypoglycaemiaWhole blood glucose concentration < 2.2 mmol/L
 Metabolic acidosisPlasma bicarbonate < 15 mmol/L
Standard case report forms (CRFs) were used to collect data on clinical, laboratory and socio-demographic factors, along with subsequent treatment and outcome. Patients displaying any of the following clinical features were recruited if their peripheral blood smears were positive for asexual forms of P. falciparum. Impaired consciousness was defined as prostration (generalized weakness so that the patient was unable walk or sit up without assistance) or coma (an unarousable state with a corresponding Blantyre Coma Scale (BCS) ≤ 2) where no other cause other than malaria could be identified. Multiple convulsions, defined as > 2 episodes in the 24 h period prior to admission. Respiratory distress was defined as increased work of breathing manifesting as deep, fast or very slow breathing, including retractions and the use of accessory muscles. Spontaneous bleeding was defined as physically un-induced and irrepressible bleeding from at least two non-traumatized sites in a patient with severe malaria without a previous history of abnormal bleeding. Haemoglobinuria was defined when the parent reported a history of, and the clinician macroscopically observed, dark red or black urine according to a score of 5 or more on the Hillman urine colour scale [13 (link)], a description that is more objective than a history of passing dark red or black urine alone that was previously used in the Democratic Republic of Congo (DRC) [14 (link), 15 (link)], Nigeria [16 (link)] and Burundi [17 (link)]. Clinical jaundice was defined as the yellowing of mucous membranes noted in sufficient daylight. Pyrexia was defined as an axillary temperature of > 37.5 °C, measured using a digital thermometer and hyperpyrexia as an axillary temperature of ≥ 40.0°C. Hypoxaemia was defined as a transcutaneous oxygen saturation (TCpO2) of < 90%, as measured using a standard pulse oximeter. Definitions of laboratory criteria included: hypoglycaemia (blood glucose < 2.2 mmol/L), metabolic acidosis (plasma bicarbonate < 15 mmol/L), severe anaemia (Hb < 5 g/dL), hyperparasitaemia (> 5% or 250 000/μL) [8 ], and hyperlactataemia (lactate ≥ 5 mmol/L). On outcomes, a severe malaria death in our study was any in-hospital fatality in a child recruited to the study.
Eligible patients with any of the above features of severity were invited to participate after informed consent was sought from the patient’s parent or guardian. Data were captured on a case report form (CRF), which was logically sequenced to capture data on: socio-demographic features, clinical features (symptoms and signs), laboratory data, treatment and outcome. Blood samples for the study were collected for parasitological microscopy as well as estimation of lactate using (ARKRAY Factory, Shiga, Japan), and random blood sugar levels using On Call Plus (ACON Laboratories, San Diego, USA). In addition, a small volume of blood was collected for additional tests including 2 mls of whole blood for a complete blood count (CBC) and 0.1 mls for a quality-controlled blood slide. At the hospital, HIV testing was routinely done as part of the Ministry of Health policy.
Olupot-Olupot P., Engoru C., Nteziyaremye J., Chebet M., Ssenyondo T., Muhindo R., Nyutu G., Macharia A.W., Uyoga S., Ndila C.M., Karamagi C., Maitland K, & Williams T.N. (2020). The clinical spectrum of severe childhood malaria in Eastern Uganda. Malaria Journal, 19, 322.
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Publication 2020
Acidosis, Metabolic Anemia Asthenia Axilla Bicarbonates BLOOD Blood Glucose Blood Volume Child Clinical Laboratory Services Clonic Seizures, Tonic Cola Comatose Consciousness Debility Erythrocytes Fever Fingers Hospital Referral Hyperlactatemia Hyperpyrexia Hypoglycemia Icterus Lactates Legal Guardians Malaria Microscopy Mucous Membrane Muscle Tissue Oxygen Saturation Parasites Parent Patients Plasma Pulse Rate Respiratory Rate Seizures Thermometers Transmission, Communicable Disease Urine Vitamin A
5
Delirium Risk Prediction Model Development and Validation
We calculated the sample size needed for the development of the model on the basis of the need for 10-15 delirious patients per risk factor plus 10% dropout. We imputed missing data for the risk factors. Values were missing in the development study for urea (0.7%), liver enzymes (3.0%), bilirubin (18.0%), calcium (4.5%), sodium (0.3%), haematocrit (0.4%), metabolic acidosis (1.0%), and acute physiology and chronic health evaluation (APACHE)-II scores (0.7%). Data for all other variables were complete. All data for the temporal validation study were complete. We decided that if a laboratory measurement was not determined we had no reason to assume that the missing variable had an abnormal value, and we imputed the mean normal value. To calculate the normal value, we selected all patients with a normal value and then calculated the mean value for this group of patients and used it for imputation. When the APACHE-II score was missing, we imputed the mean value for the delirium or non-delirium group, depending on the results of the CAM-ICU. In the external validation dataset, 6.3% of the urea values were missing and imputed. For APACHE-II, 0.6% of the scores were missing, and we imputed a mean APACHE-II score for the group in the external validation set.
We used univariate logistic regression to develop the prediction model by assessing the association between each potential prognostic determinant and the presence or absence of delirium. We excluded determinants with a P value above 0.15 in univariate analysis or with a prevalence rate below 10%. With the remaining risk factors, we used multivariate logistic regression analysis with backward elimination (excluding risk factors with P values over 0.10) to evaluate the independent associations with the occurrence of delirium. The final model therefore contains independent risk factors for delirium. We estimated the prognostic ability of the model to discriminate between patients with and without delirium by using the area under the receiver operating characteristics curve (AUROC). We used bootstrapping techniques to adjust for overfitting—that is, for overly optimistic estimates of the regression coefficients of the risk factors in the final model. Two hundred random bootstrap samples resulted in shrunken regression coefficients of the risk factors and area under the curve of the developed model.21
In both validation studies, we multiplied shrunken regression coefficients for each risk factor by the observed patients’ values. The outcome is a calculated predicted probability on which we built a new AUROC. Finally, to examine how well the model was calibrated, we calculated linear predictor values for each patient of every cohort by using the coefficients from the final development model. We used these linear predictors in a logistic regression model to test whether the prediction rule was well calibrated, resulting in a calibration slope and an intercept. A calibration slope of 1 and an intercept of 0 show a perfect calibration. Calibration plots for each cohort are available in web appendix D. We used SPSS 16.01, R statistics version 2.10.1,22 using the rms package,23 for all analyses.
van den Boogaard M., Pickkers P., Slooter A.J., Kuiper M.A., Spronk P.E., van der Voort P.H., van der Hoeven J.G., Donders R., van Achterberg T, & Schoonhoven L. (2012). Development and validation of PRE-DELIRIC (PREdiction of DELIRium in ICu patients) delirium prediction model for intensive care patients: observational multicentre study. The BMJ, 344, e420.
Publication 2012
Acidosis, Metabolic Bilirubin Calcium Delirium Enzymes Liver Optimism Patients physiology Prognostic Factors Sodium Urea Volumes, Packed Erythrocyte

Most recents protocols related to «Acidosis, Metabolic»

1
Endpoints for Acute Kidney Injury
The following endpoints were defined: in-hospital death (endpoint 1), the need for KRT (endpoint 2), and recovery of kidney function until discharge (endpoint 3). The need for KRT was fulfilled if a patient required at least one extracorporal procedure. Extracorporal therapy was initiated, if patients presented one or more of the following criteria: refractory hyperhydration including progressive dyspnea, refractory hyperkalemia of 6.5 mmol/L or above, refractory metabolic acidosis with a pH of 7.1 or below, neurological symptoms due to suspected uremia. KRT was either performed as intermittent hemodialysis, or slow extended daily dialysis (SLEDD), or continuous veno-venous hemodialysis. Recovery of kidney function was diagnosed if the last eGFR (CKD-EPI [12 (link)]) differed from the initial eGFR by no more than 10%. Data on endpoints 1 and 2 were available from all patients, and data on endpoint 3 were missing in 50 subjects.
Marahrens B., Damsch L., Lehmann R., Matyukhin I., Patschan S, & Patschan D. (2023). Increased Serum Sodium at Acute Kidney Injury Onset Predicts In-Hospital Death. Journal of Clinical Medicine Research, 15(2), 90-98.
Publication 2023
Acidosis, Metabolic Continuous Venovenous Hemodialysis Dyspnea EGFR protein, human Extended Daily Dialysis Hemodialysis Kidney Neurologic Symptoms Patient Discharge Patients Recovery of Function Therapeutics Uremia Water Intoxication
2
Fluoxetine and Etonogestrel Respiratory Effects
We investigated whether the combination of etonogestrel with a selective serotonin reuptake inhibitor, fluoxetine, induced a respiratory benefit. As the respiratory influence of fluoxetine has been shown to be variable according to its concentration in rodent newborns (28 (link), 30 (link), 31 (link)), we tested different concentrations to determine a dose that did not cause respiratory depression in OF1 mice. Preparations were superfused for 20 minutes at normal-pH with fluoxetine at 6.25 µM (n=10), 12,5 µM (n=11), 25 µM (n=10), 50 µM (n=4) and 100 µM (n=4); 12.5 µM was retained (see results). Second, preparations were exposed to either 5.10-2 µM etonogestrel alone (n=18), 12.5 µM fluoxetine alone (n=11) or both 5.10-2 µM etonogestrel and 12.5 µM fluoxetine (n=29) for 20 minutes at normal-pH, followed by 30 minutes under metabolic acidosis with the considered drug and finally 30 minutes under normal-pH free of drug (Figure 1).
Given the results obtained in OF1 mice preparations, we investigated a respiratory benefit to the combination of etonogestrel with fluoxetine in Phox2b mutants. First, preparations from 10 Phox2b mutants and 15 wildtype littermates were exposed to 12.5 µM of fluoxetine at normal-pH. At this concentration, fluoxetine decreased the fR of Phox2b mutant mice. We therefore decided to test 3.125 µM (n=14 Phox2b and n=15 wildtype littermates) and 6.25 µM (n=12 Phox2b and n=16 wildtype littermates) of fluoxetine. 3.125 µM fluoxetine was retained (see results). Second, Phox2b mutant (n=14) and wildtype littermates (n=15) preparations were co-exposed to 5.10-2 µM etonogestrel and 3.125 µM fluoxetine under normal-pH and metabolic acidosis (Figure 1), followed by a washout with normal-pH free of drug.
Casciato A., Bianchi L., Reverdy M., Joubert F., Delucenay-Clarke R., Parrot S., Ramanantsoa N., Sizun E., Matrot B., Straus C., Similowski T., Cayetanot F, & Bodineau L. (2023). Serotonin and the ventilatory effects of etonogestrel, a gonane progestin, in a murine model of congenital central hypoventilation syndrome. Frontiers in Endocrinology, 14, 1077798.
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Publication 2023
Acidosis, Metabolic etonogestrel Fluoxetine Infant, Newborn Mice, House Pharmaceutical Preparations Respiratory Depression Respiratory Rate Rodent Selective Serotonin Reuptake Inhibitors
3
Phox2b Mutant Respiratory Response

Phox2b mutant (n=23) and wildtype littermate (n=31) preparations were exposed only to the drug-free metabolic acidosis protocol to assess whether they showed a respiratory response to metabolic acidosis.
Based on previous work (14 (link), 15 (link)), to determine the respiratory influence of etonogestrel in normal-pH conditions and the respiratory response to metabolic acidosis, 39 preparations of Phox2b mutants and 38 preparations of wildtype littermates were exposed to 5.10-2 µM of etonogestrel.
Casciato A., Bianchi L., Reverdy M., Joubert F., Delucenay-Clarke R., Parrot S., Ramanantsoa N., Sizun E., Matrot B., Straus C., Similowski T., Cayetanot F, & Bodineau L. (2023). Serotonin and the ventilatory effects of etonogestrel, a gonane progestin, in a murine model of congenital central hypoventilation syndrome. Frontiers in Endocrinology, 14, 1077798.
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Publication 2023
Acidosis, Metabolic Acidosis, Respiratory etonogestrel Pharmaceutical Preparations Respiratory Rate
4
Standardized Diabetes Outcomes Reporting
Documented clinical and demographic variables included sex, age at onset of diabetes, and age and duration of diabetes at each contact. A migratory background was assigned if the patient or at least one parent was born outside of Germany.5 Body weight standard deviation score (SDS), height SDS, and body mass index (BMI) SDS were based on the German Health Interview and Examination Survey for Children and Adolescents (KiGGs) reference [8 (link)].
HbA1c values were mathematically standardized to the reference range of 4.05–6.05% (IFCC 20.8–42.6 mmol/L) of the Diabetes Control and Complications Trial applying the multiple of the mean method in order to correct for different laboratory methods [5 (link), 9 (link)]. Corrected HbA1c values were deducted from time in range (TiR) data as described previously [10 (link)]. Laboratory-measured HbA1c values and those estimated from CGM data were integrated into a combined glucose indicator (CGI) expressed in “%”, in analogy to HbA1c values as described before [5 (link)]. Acute complications—DKA and hypoglycemia—were also documented as outcomes. DKA was defined as presence of metabolic acidosis with a pH below 7.3 and/or bicarbonate levels below 15 mmol/L. Severe hypoglycemia was defined as an episode when the affected patient required assistance, hypoglycemic coma as loss of consciousness or seizure [11 (link)].
Hammersen J., Tittel S.R., Khodaverdi S., Reschke F., Flury M., Menzel U., Mönkemöller K., Meissner T., Karges B, & Holl R.W. (2023). Metabolic control during the first two years of the COVID-19 pandemic in pediatric patients with type 1 diabetes: results from the German DPV initiative. Acta Diabetologica, 60(6), 757-766.
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Publication 2023
Acidosis, Metabolic Adolescent Bicarbonates Body Weight Child Childbirth Comatose Complications of Diabetes Mellitus Diabetes Mellitus Glucose Hypoglycemia Hypoglycemic Agents Index, Body Mass Parent Patients Seizures
5
Fungal Sepsis in COVID-19 Patient
A 75-year-old woman was admitted on 13 May 2021 to a University Hospital in Rio de Janeiro, Brazil, due to an arterial ulcer in the left foot (Fontaine IV Peripheral Arterial Disease). The disease started a year before and clinical treatments were unsuccessful, leading to bone exposure. Therefore, she was being prepared for a surgical amputation. Positive polymerase chain reaction results from two nasal swabs for SARS-CoV-2 was found twice, 30 and 10 days before admission, and she was treated symptomatically at home. Remarkable data in her past pathological history are systemic arterial hypertension, stroke with resulting aphasia and past history of smoking. She was started on intravenous antimicrobials (piperacillin and tazobactam) and heart monitoring due to the atrial fibrillation. Her general condition worsened quickly, showing drowsiness with disorientation, bradycardia and hypotension, metabolic acidosis, leukocytosis and elevated C-reactive protein. At that time, a nasal swab antigen test did not detect SARS-CoV-2. Two blood samples were collected and sent to the laboratory for culture approximately 6 hours before she died due to the septic shock, 4 days after hospitalization, on 17 May 2021. Yeast-like organisms were isolated from both blood culture samples (Figure 1A).
Ramos L.S., Mokus L., Frota H.F., Santos M.V., Oliveira S.S., Oliveira M.M., Costa G.L., Alves A.L., Bernardes-Engemann A.R., Orofino-Costa R., Aor A.C., Branquinha M.H, & Santos A.L. (2023). SARS-CoV-2 Post-Infection and Sepsis by Saccharomyces cerevisiae: A Fatal Case Report—Focus on Fungal Susceptibility and Potential Virulence Attributes. Tropical Medicine and Infectious Disease, 8(2), 99.
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Publication 2023
Acidosis, Metabolic Amputation Antigens Aphasia Arteries Atrial Fibrillation BLOOD Blood Culture Bones Cerebrovascular Accident C Reactive Protein Foot Ulcer Heart High Blood Pressures Hospitalization Leukocytosis Microbicides Nose Peripheral Vascular Diseases Piperacillin Polymerase Chain Reaction SARS-CoV-2 Septic Shock Somnolence Tazobactam Woman Yeast, Dried

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More about "Acidosis, Metabolic"

Metabolic acidosis is a medical condition characterized by an imbalance in the body's acid-base equilibrium, resulting in an abnormal increase in acidity.
This can occur due to various underlying causes, such as kidney dysfunction, diabetes, or excessive production of certain acids.
Symptoms may include nausea, fatigue, and rapid breathing.
Accurate diagnosis and effective management of metabolic acidosis are crucial for maintaining overall health and preventing complications.
Researchers exploring this complex metabolic disorder can utilize advanced tools like PubCompare.ai to optimize their research protocols.
PubCompare.ai's AI-driven insights can help researchers discover the latest literature, pre-prints, and patents related to metabolic acidosis, allowing them to find the best protocols and products.
The Prismaflex and MultiFiltrate systems are commonly used for the management of metabolic acidosis, particularly in cases of kidney dysfunction.
These devices can help restore the body's acid-base balance through dialysis or hemofiltration.
Additionally, the Prisma system and ELISA (Enzyme-Linked Immunosorbent Assay) can be employed for diagnostic purposes, while the Cobas analyzer can be used to measure the levels of various metabolites and electrolytes.
Intravenous heparin may also be used in the treatment of metabolic acidosis, as it can help improve blood flow and prevent clotting.
Statistical software like SPSS (version 18.0) and Stata 11 can be utilized to analyze the data collected during research studies on metabolic acidosis, providing valuable insights into the condition and its management.