Hemodialysis (HD) therapy is an indispensable tool used in critical care management. Patients undergoing HD are at risk for intradialytic adverse events, ranging from muscle cramps to cardiac arrest. So far, there is no effective HD device–integrated algorithm to assist medical staff in response to these adverse events a step earlier during HD.We aimed to develop machine learning algorithms to predict intradialytic adverse events in an unbiased manner.Three-month dialysis and physiological time-series data were collected from all patients who underwent maintenance HD therapy at a tertiary care referral center. Dialysis data were collected automatically by HD devices, and physiological data were recorded by medical staff. Intradialytic adverse events were documented by medical staff according to patient complaints. Features extracted from the time series data sets by linear and differential analyses were used for machine learning to predict adverse events during HD.Time series dialysis data were collected during the 4-hour HD session in 108 patients who underwent maintenance HD therapy. There were a total of 4221 HD sessions, 406 of which involved at least one intradialytic adverse event. Models were built by classification algorithms and evaluated by four-fold cross-validation. The developed algorithm predicted overall intradialytic adverse events, with an area under the curve (AUC) of 0.83, sensitivity of 0.53, and specificity of 0.96. The algorithm also predicted muscle cramps, with an AUC of 0.85, and blood pressure elevation, with an AUC of 0.93. In addition, the model built based on ultrafiltration-unrelated features predicted all types of adverse events, with an AUC of 0.81, indicating that ultrafiltration-unrelated factors also contribute to the onset of adverse events.Our results demonstrated that algorithms combining linear and differential analyses with two-class classification machine learning can predict intradialytic adverse events in quasi-real time with high AUCs. Such a methodology implemented with local cloud computation and real-time optimization by personalized HD data could warn clinicians to take timely actions in advance. Hemodialysis (HD) therapy has a substantial role in critical care management . Due to The Crit-Line  monitor is a device developed to assist with fluid removal during ultrafiltration by noninvasively monitoring real-time hematocrit, oxygen saturation, and intradialytic volume status, using an optical transmission method . AlthougArtificial intelligence has been applied to HD patients to assist clinical practice, including prediction of urea clearance -19, dietThis was a retrospective observational study in a single institution. We reviewed the records of all patients who underwent maintenance HD therapy at Changhua Christian Hospital, a tertiary-care referral center in middle Taiwan, between August 2017 and October 2017. During this period, 129 patients were eligible for enrollment evaluation, and 108 patients completed the 3-month study. HD sessions were excluded for the following three reasons: (1) session interruption due to dialyzer exchange, (2) more than one interruption per session due to patient urination or defecation, and (3) inability of patients to freely express their discomfort during the session. Eventually, a total of 4221 HD sessions from 108 patients were used to build the model. Each patient received either 39 or 40 HD sessions during the 3-month study period.The Institutional Review Board of our institution approved all protocols in April 2017 before the study began, and the protocols conformed to the ethical guidelines of the Helsinki declaration. The need for informed consent was waived because of the retrospective nature of the study.Demographic information from medical records, including age, gender, and years under dialysis treatment, were included for model building. Dialysis and physiological data of the enrolled patients during the 4-hour HD session were included in the study. Physiological data were measured and recorded by medical staff every 30 to 60 minutes approximately. Dialysis data were collected from the dialysis machine automatically. Intradialytic adverse events were documented by medical staff according to physiological measurements or patient complaints, as shown in i consisted of records {Yj,k, Tk}, where j (range 1-9) is the index for the dialysis and physiological measurements, and k is the index of time when a measurement is taking. Yj,k is the value of the measurement j at time Tk. HDi also included additional time-invariant patient-specific information Yj (j=10-13), including age, gender, years under dialysis treatment, and predialytic weight , the data set HDc weight . Accordij,1 at the beginning of each HD session was excluded if the blood flow rate varied between T1 and T2. We also excluded the data point Yj,k when the blood flow rate was equal to or below zero due to dialysis interruption . An entire HD session was excluded from the analysis if the session was interrupted more than once.To avoid the artifacts at the beginning of the data due to the different procedures on how the dialysis was set up and started in each HD session, the first data point Yj,k, Tk} of an HD session was included for feature extraction if no adverse event was registered for the session. On the other hand, for the HD session with adverse events, only data preceding the first adverse event were included for feature extraction, meaning the length of HD was less than 4 hours. Because the time interval between two adjacent records and the length of HD sessions vary, regression analysis is challenging, and we need to include the temporal features of the measured variables in the analyses for classification. To this end, we derived the mean, standard deviation of the mean, and coefficient of variance, as well as the slope and R square of linear regression from the dialysis and physiological measurements {Yj,k, Tk}. We also derived the maximum, minimum, and mean of change rate (the first-order derivative), as well as the second-order derivative of venous pressure and transmembranous pressure as features for analysis. A total of 84 features {Xh} (h=1-84), including those from the raw measurements {Yj,k, Tk} and those derived from the temporal aspect of the data as described above, were extracted for each HD session .As aforementioned, the dialysis data set {YFor outcome labeling, the HD sessions with one or more than one adverse event were labeled as 1, and HD sessions with no adverse event were labeled as 0. We also randomly relabeled 4221 HD sessions regardless of their true outcome as a negative control set while kept the same 0 to 1 ratio as the experimental set. A two-class classification model was built and evaluated by four-fold cross-validation using Azure (Microsoft Inc). At least three repeats were performed by introducing different random numbers for each model building.To pinpoint which features are more important than others in predicting HD adverse events, we also selected and used key features for model building and compared the results with that by a total of 84 features. The selection of key features was performed using MATLAB   and the Institutional Review Board of Changhua Christian Hospital (CCH IRB No. 161005).As of November 2017, we enrolled 108 patients. The list of intradialytic adverse events and the number of occurrences are shown in To increase the outcome 1 to 0 ratios, wherein the session with an adverse event is labeled as 1 and the session without an adverse event is labeled as 0, we categorized the 27 adverse events listed in A two-class averaged perceptron was used for model building with a learning rate of 20 and maximal iterations of 20. For the 84-feature model, the mean area under the curve (AUC) was 0.83 (SD 0.03), with an F1 score of 0.53, sensitivity of 0.53, and specificity of 0.96 A curve bUltrafiltration rate and ultrafiltration volume are important parameters for HD. However, our models indicated that employing a single feature, such as the maximal value of ultrafiltration volume (feature 78) or the mean value of ultrafiltration rate changes (feature 77), cannot predict adverse events properly B curve hNext, the 21 features that most frequently appeared in the 20-feature combinations were selected for the evaluation. The two-class averaged perceptron model based on these top 21 performance features but skipping ultrafiltration-related features showed a mean AUC of 0.82 (SD 0.02) and F1 score of 0.45 A curve cThe 21 features were age, maximum transmembranous pressure, minimum systolic blood pressure (SBP), minimum diastolic blood pressure (DBP), minimum pulse pressure, minimum blood flow rate, mean SBP, mean venous pressure, mean transmembranous pressure, slope of linear regression of SBP, slope of linear regression of DBP, slope of linear regression of pulse pressure, slope of linear regression of pulse rate, slope of linear regression of transmembranous pressure, standard deviation of the mean of blood flow rate, R-squared of linear regression of pulse pressure, and related parameters to the second-order derivative of venous pressure  .The model, which was based on 14 ultrafiltration-related features, had a mean AUC of 0.85 (SD 0.04) and F1 score of 0.45  curve d The model, which was based on a total of 84 features, had a mean AUC of 0.93 (SD 0.02) and F1 score of 0.41 for predicting the occurrence of hypertension  curve a.As shown in To further understand the cutoff ending time point dependence of prediction accuracy, 500 HD sessions were randomly selected to compare the prediction probabilities of adverse events obtained from 84 features with cutoff ending time points of 0, 5, 10, 15, and 20 minutes before the index adverse event. As shown in end) between 3 and 3.5 hours, yet the endpoints of positive ones remained unchanged. The data set {Yj,k, Tk} at endpoint Tend was defined according to the same method used for {Yj,k, Tk} at arbitrary time Tp. Regarding the results, the mean AUC was 0.89 (SD 0.019), F1 score was 0.55, sensitivity was 0.52, and specificity was 0.97. Alternately, the AUC was 0.86 with an F1 score of 0.55 when the endpoints were assigned exactly at 3.3 hours. Compared to the original results obtained from the untruncated negative HD sessions with a duration of about 4 hours , the prediction results were better when the endpoints were set earlier. Indeed, the AUC was 0.92, with an F1 score of 0.62, sensitivity of 0.61, and specificity of 0.98, when the endpoints were randomly assigned between 2.5 and 3.5 hours for negative HD sessions.Even though none of the 84 features contained explicit time series information, the linear and differential analyses that feature extraction employed may be affected by the length of HD sessions. Therefore, we truncated the HD sessions with no adverse events (negative ones) and compared the prediction results with those from the untruncated ones. Since the average length of HD sessions with adverse events (positive ones) was 3.3 hours, negative HD sessions were truncated and randomly assigned endpoints . After excluding these two ultrafiltration-related features, we found that the remaining 21 features were sufficient for accurate prediction with good discriminating power, with a slight reduction in the AUC from 0.83 (84 features) to 0.82 (21 features). The model built by 14 ultrafiltration-related features also had a good AUC of 0.83. Therefore, instead of including all 84 features for model building, selecting the top 21 ultrafiltration-unrelated features or integrating a total of 14 ultrafiltration-related features can reduce computing load. Our results also suggest that these two clusters of features  from a total of 84 features extracted from [Yfeatures B curve eIn our study, muscle cramp was an adverse event that occurred most frequently during HD treatment. A muscle cramp is a common adverse event that happens during HD therapy, with a prevalence of 28% among all HD sessions ,34. MuscIn general, symptomatic hypotension occurs in 20% to 30% of dialysis sessions ,39-43. TAs shown in Compared with several two-class classification modules, such as Bayes point machine, boosted decision tree, and SVM, models built by two-class average perceptron had the best AUC and F1 score. We also built models by deep learning (data not shown), but the results from deep learning did not show a good AUC and F1 score, possibly due to the limited number of our HD data sets. As clinicians are now facing the new era of artificial intelligence , the intSeveral questions may be answered if the size of the HD data set is expanded in future studies. First, how early can we predict adverse events? The consistency in the predicted probabilities of adverse events using features based on different cutoff ending time points could detect about one-tenth of HD sessions with adverse events . We antiIn this study, a model of two-class classification was established to predict intradialytic adverse events in quasi-real time, with AUCs higher than 0.8. The consistency in the predicted probabilities of adverse events obtained from the features extracted in the ongoing HD process in real time could have the HD session tagged for forthcoming adverse events. Such a methodology implemented with local cloud computation could warn clinicians to take necessary actions and adjust the HD machine settings in advance.


To explore the postoperative complications of distal radius fractures and analyze the related factors that affect its prognosis.  The clinical data of 50 patients with distal radius fractures admitted to our hospital from October 2016 to September 2019 were retrospectively analyzed. All patients were followed up for 6–12 months, and their postoperative complications were recorded. Collect general patient information and related clinical data. During the follow-up process, Gartland and Werley wrist function scoring system was used to evaluate the prognosis of patients' joint function. Univariate analysis and multiple logistic regression models were used to analyze the related factors that affected the prognosis of patients. P < 0.05). Multivariate logistic analysis showed that age, AO classification, surgical method, whether it was combined with other fractures on the same side, whether it was comminuted fracture, and the time to start postoperative exercise were the independent factors affecting the prognosis of distal radius fractures (P < 0.05).  15 patients with postoperative complications were found during the follow-up period, with an incidence rate of 30.00%. Univariate analysis showed that the patient's age, cause of injury, AO classification, shortened deformity, whether it was osteoporosis, surgical method, whether it was combined with other fractures on the same side, whether it was comminuted fracture, and the time to start postoperative exercise were all related to the distal radius. The prognosis of fractures is related ( The postoperative complications of distal radius fractures are higher. The prognosis is related to the patient's age, AO classification, surgical method, whether it is combined with other fractures on the same side, whether it is comminuted fracture, and the time to start postoperative exercise. Therefore, choosing an appropriate surgical method and starting exercise in time can effectively improve the recovery of the patient's wrist function and reduce the occurrence of complications. Distal radius fracture is one of the most common fractures. It refers to a fracture within 3 cm of the articular surface of the lower end of the radius, which is mostly caused by direct or indirect violence –3. FractA retrospective analysis of the clinical data of 50 patients with distal radius fractures admitted to our hospital from October 2016 to September 2019. There were 28 males and 22 females, aged from 16 to 68 years old, with an average age of (44.68 ± 12.24) years old. Causes of injury: fall from height (8 cases), fall injury (20 cases), traffic injuries (9 cases), hit injuries (6 cases), and others (7 cases). According to the fracture AO classification, there are type A (18 cases), type B (15 cases), and type C (17 cases). The selected surgical methods are internal fixation (29 cases) and external fixation (21 cases). Inclusion criteria: all met the diagnostic criteria for distal radius fractures; all were fresh distal radius fractures within 2 weeks; all were closed fractures. Exclusion criteria: patients with old fractures, patients with severe medical diseases, patients with severe motor nerve dysfunction, patients with severe mental illness, and patients with follow-up loss.All patients were treated with open reduction and internal fixation: patients were placed in supine position and given brachial plexus anesthesia. Surgical approach was performed to stretch the flexor carpi radialis, flexor thumb, and median nerve to protect the radial artery and expose the fracture end. After fracture reduction, fracture shortening deformity, ulnar deviation angle, and palpal angle were corrected. The T-shaped plate was used for compression fixation, and anterior muscle was repaired and sutured layer by layer.All patients were followed up for 6–12 months by outpatient and telephone, and their postoperative complications were recorded. We collected general information such as the patient's gender and age, the cause of the patient's injury, AO classification, shortening deformity, whether osteoporosis is present, surgical method, whether the injured side is the dominant hand, whether it is combined with other fractures on the same side, whether it is comminuted fracture, the time from injury to operation, surgical approach, postoperative start exercise time, etc. During the follow-up, the Gartland and Werley wrist joint function scoring system was used to evaluate the prognosis of patients' joint function; subjective evaluation: no pain, 0 points; occasional pain, wrist weakness, and 2 points; occasional pain, wrist weakness, limited movement, and 4 points; persistent pain, limited movement, wrist deformity, and 6 points. Objective evaluation: dorsiflexion defect <45° and 5 points; ruler deviation defect <15° and 3 points; supination defect <50° and 2 points; palmar flexion defect <30° and 1 point; radial deviation defect <15° and 1 point; circular motion defects and 1 point; radioulnar joint pain and 1 point; pronation defects and 2 points. Complications: slight changes of arthritis, 1 points, with pain, 3 points, moderate changes, 2 points, with pain, 4 points, severe changes, 3 points, with severe pain, 5 points; median nerve complications, 1 point. 0–2 were excellent, 3–8 were good, 9–20 were fair, and ≥21 were poor. All patients were divided into excellent and good group (excellent + good) and fair and poor group (fair + poor) according to their prognosis.P < 0.05 indicates that the difference is statistically significant.The results of this experiment were statistically analyzed by SPSS 20.0 . Count data were expressed by rate, and chi-square test was used for their comparison between groups. Multivariate analysis adopts the multiple logistic regression model. During the follow-up period, the wrist function score of 50 patients with radial fracture was (7.41 ± 4.26). A total of 15 patients with postoperative complications were found, and the incidence rate was 30.00%. Among them, carpal tunnel syndrome and traumatic arthritis are the most common ones, each accounting for 6.00%, as shown in P < 0.05), as shown in Univariate analysis showed that age, cause of injury, AO classification, shortening deformity, osteoporosis, surgical method, ipsilateral other fractures, comminuted fractures, and postoperative exercise time were all related to the postoperative prognosis of distal radius fracture (P < 0.05), as shown in Tables Multivariate logistic analysis showed that age, AO classification, surgical method, whether it was combined with other fractures on the same side, whether it was comminuted fracture, and the time to start exercise after surgery were independent factors affecting the prognosis of distal radius fractures after surgery (The distal radius is located between the compact bone and the cancellous bone. Fractures in this part will cause the height of the radius and uneven articular surface, which directly affects the movement function of the wrist joint –15. The The results of this study showed that a total of 15 patients with postoperative complications were found during the follow-up period, with an incidence rate of 30.00%. Among them, carpal tunnel syndrome and traumatic arthritis are the most common ones, each accounting for 6.00%. The reason is that bleeding after a fracture and local injection of anesthetic drugs will cause the pressure in the carpal tunnel to increase and oppress the median nerve, leading to carpal tunnel syndrome. The bone mass and quality of the distal radius are low, so the fracture degree of fracture is relatively large, and the bone blood supply and repair ability are also poor, resulting in difficulty in reduction and traumatic arthritis –21.The results of this study showed that patients' age, injury causes, AO classification, shortening deformity, osteoporosis, surgical method, ipsilateral other fractures, comminuted fractures, and postoperative exercise time were all related to the postoperative prognosis of distal radius fractures. Multivariate logistic analysis showed that age, AO classification, operation method, ipsilateral other fractures, comminted fractures, and postoperative exercise time were independent factors influencing the prognosis of distal radius fractures. The reason is that the older the patient, the more serious the calcium loss in the body, the osteoporosis, and the worse the body function and recovery function, and other complications often occur after treatment, which greatly increases the complexity, thus leading to a poor prognosis –24. PatiThe postoperative complication rate of distal radius fractures is relatively high. The prognosis is related to the patient's age, AO classification, surgical method, whether it is combined with other fractures on the same side, whether it is comminuted fracture, and the time to start exercise after surgery. Therefore, choosing a suitable surgical method and starting exercise in time can effectively improve the patient's wrist function recovery and reduce the occurrence of complications.


Background: Persistent pulmonary hypertension of the newborn (PPHN) is a condition in which pulmonary vascular resistance fails to decrease after birth. PPHN leads to hypoxemia due to right-to-left shunting of the blood through the fetal circulation. This study aimed to determine the association between PPHN and prematurity in neonates admitted to the neonatal intensive care unit (NICU).Materials and methods: This study is a single-center, retrospective, and cross-sectional study. Patients diagnosed with PPHN had been selected by using a non-probability consecutive sampling technique from 2016 to 2020 at King Abdulaziz Medical City in Jeddah, Saudi Arabia. Patients with PPHN who did not admit to NICU were excluded.Results: Fifty-six patients had met the inclusion and exclusion criteria. Twenty-six neonates were born prematurely before 37 weeks of gestation, and 30 were born at 37 weeks or more. Among the study population, respiratory complications were seen in 30 patients with a rate of 53.6%. The most common complications were respiratory failure, persistent pulmonary hypertension, and cardiopulmonary arrest.Conclusion: Mortality was documented in 26 patients, with the complicated group having a rate of 73.3% compared to the uncomplicated group 15.4%. The most common complications seen in our patients were respiratory failure, persistent pulmonary hypertension, and cardiopulmonary arrest. Persistent pulmonary hypertension of the newborn (PPHN) is a condition in which pulmonary vascular resistance fails to decrease after birth . PPHN lePPHN is associated with developmental lung disorders, including but not limited to alveolar capillary dysplasia, genetic abnormalities of surfactant production, lung hypoplasia, and diaphragmatic hernias . It was This study aimed to determine the association between PPHN and prematurity in neonates admitted to the neonatal intensive care unit (NICU) at King Abdulaziz Medical City (KAMC) from January 2016 to January 2020. Our secondary objectives were to identify the risk factors and determine the incidence of PPHN.This study is a single-center, retrospective, and cross-sectional study. Patients diagnosed with PPHN had been selected by using a non-probability consecutive sampling technique from 2016 to 2020 at King Abdulaziz Medical City in Jeddah, Saudi Arabia. The study was ethically approved by the Institutional Review Board (IRB) at King Abdullah International Medical Research Center (KAIMARC), reference number IRBC/1203/21. The inclusion criteria were all patients diagnosed with PPHN and admitted to the NICU from 2016 to 2020. Based on echocardiography, PPHN was diagnosed as a chronically elevated mean pulmonary arterial pressure (mPAP) of ≥ 20 mm Hg at rest with increased pulmonary vascular resistance. Patients with PPHN who did not admit to NICU were excluded. This research commenced by reviewing the inclusion and exclusion criteria on the BestCare system , and a total of 56 patients were identified. A datasheet was constructed which had demographic variables such as gender, weeks of gestations, height, weight, type of delivery, and appearance, pulse, grimace, activity, and respiration (Apgar) score, clinical manifestations such as hypoxia, respiratory distress, pneumothorax, pulmonary hypoplasia, and cardiac findings such as mitral regurgitation, ventral septal defect, myocardial dysfunction, presence of patent ductus arteriosus, foramen ovale, right ventricular hypertrophy, and right atrial hypertrophy. Other variables from the treatment regimen that were recorded include the use of Sildenafil, inotropes, vasodilators, surfactant, nitric oxide, fractional inspired oxygen (FiO2), high-frequency ventilation (HFV), and ECMO. Patients' outcomes were also collected in the datasheet, including length of stay, readmission, respiratory complications, and mortality.The data analysis for this study was generated using IBM SPSS version 23 . For the continuous variables, the mean (standard deviation) and median (interquartile range) were reported for normally distributed data and skewed data, respectively. For the categorical variables, frequency and percentages were used. Two-tailed Student's t-test was used to compare means and chi-square test was performed for comparisons of categorical variables. A P-value of <0.05 was considered statistically significant.Fifty-six patients had met the inclusion and exclusion criteria. The study population had a gestational age ranging from 23 to 24 weeks with a mean of 25±6 weeks. Twenty-six neonates were born prematurely before 37 weeks of gestation, and 30 were born at 37 weeks or more. Thirty-eight patients (67.9%) were males and 18 (32.1%) were females. Among the study population, respiratory complications were seen in 30 patients with a rate of 53.6%. The most common complications were respiratory failure (n=8), persistent pulmonary hypertension (n=7), and cardiopulmonary arrest (n=5). Details about the different types of respiratory complications are listed in Table Mean one-minute and five-minute Apgar scores were four and seven in the respiratory complication group, respectively, compared to six and eight in the uncomplicated group. However, there was no statistically significant difference. For the gestational weeks, the complication group had a mean of 33.5 weeks compared to the uncomplicated group, which had a mean age of 36 weeks. Gender of the neonates, type of delivery, and diabetes during pregnancy had equal rates among the complicated and uncomplicated patients. For the clinical presentation of the study population, 40 patients (71.4%) had hypoxia, and 27 (48.2%) of the hypoxic patients had it during the 24 hours after birth with a mean oxygen saturation of 89±11%. Also, metabolic acidosis was seen in 18 (32.1%) patients, respiratory distress in 37 (66.1%) patients, meconium aspiration syndrome in nine (16.1%) patients, pneumonia in 12 (21.4%) patients, pulmonary hypoplasia in eight (14.3%) patients, and pneumothorax in 15 (26.8%) patients. PPHN and prematurity in neonates admitted to the neonatal intensive care unit (NICU) show no statistical significance. Cardiac findings after examination of the study population are listed in Table Down syndrome was diagnosed in four (7.1%) of the neonates. Table According to the clinical characteristics of PPHN associated with Down syndrome, 17 infants with Down syndrome (DS) did not have any structural, congenital heart diseases (CHD) but presented with PPHN. It was noticed that pulmonary hypertension (PH) was resolved in most patients, while two infants that had refractory PH benefited from patent ductus arteriosus (PDA) ligation . An autoA study was conducted to measure the prevalence in Down syndrome patients that had persistent pulmonary hypertension and congenital heart defects under the Dutch Pediatric Surveillance Unit aimed to find a correlation between CHD and PPHN with DS. It was shown that when compared to the general population, in neonatal patients with DS there was a significant 43% prevalence of CHD, while having a marked increased elevation in the incidence of PPHN (5.2%) . There wThe conducted studies and results follow our shared goals and objectives of the importance of detecting structural heart defects during the neonatal periods in DS patients, with appropriate methods such as echocardiography. The importance of early detection is well supported by the DS health care guidelines published by the American Academy of Pediatrics and the Down Syndrome Medical Interest Group of the United Kingdom and Ireland . Early dLimitationsThis retrospective study with a single-center design had a limited sample size which affected generalizability. It has an interpretive bias due to lack of information of our patients during the study duration. Also, an inherent selection bias because it was retrospective. Many patients were excluded due to insufficient data and information.PPHN is considered a significant source of postnatal morbidity and mortality. Mortality was documented in 26 patients, with the complicated group having a rate of 73.3% compared to the uncomplicated group with a rate of 15.4%. That emphasizes the importance of recognizing these complications early to improve the outcome and reduce the mortality rate. The most common complications seen in our patients were respiratory failure, persistent pulmonary hypertension, and cardiopulmonary arrest. Apgar scores, type of delivery, gender of the neonates, and diabetes during pregnancy were not statistically significant. Cardiac screening by echocardiography is crucial. Early diagnosis and management are the keys to avoiding irreversible hemodynamic consequences of the defect. The inability to treat PPHN increases the risk for postnatal complications which extremely affect the outcomes of these infants.


The COVID-19 pandemic has required clinicians to pivot to offering services via telehealth; however, it is unclear which patients (users of care) are equipped to use digital health. This is especially pertinent for adults managing chronic diseases, such as obesity, hypertension, and diabetes, which require regular follow-up, medication management, and self-monitoring.The aim of this study is to measure the trends and assess factors affecting health information technology (HIT) use among members of the US population with and without cardiovascular risk factors.We used serial cross-sectional data from the National Health Interview Survey for the years 2012-2018 to assess trends in HIT use among adults, stratified by age and cardiovascular risk factor status. We developed multivariate logistic regression models adjusted for age, sex, race, insurance status, marital status, geographic region, and perceived health status to assess the likelihood of HIT use among patients with and without cardiovascular disease risk factors.A total of 14,304 (44.6%) and 14,644 (58.7%) participants reported using HIT in 2012 and 2018, respectively. When comparing the rates of HIT use for the years 2012 and 2018, among participants without cardiovascular risk factors, the HIT use proportion increased from 51.1% to 65.8%; among those with one risk factor, it increased from 43.9% to 59%; and among those with more than one risk factor, it increased from 41.3% to 54.7%. Increasing trends in HIT use were highest among adults aged >65 years , who had more than one cardiovascular risk factor (APC 5%) and among those who did not graduate from high school (APC 8.8%). Likelihood of HIT use was significantly higher in individuals who were younger, female, and non-Hispanic White; had higher education and income; were married; and reported very good or excellent health status. In 2018, college graduates were 7.18 (95% CI 5.86-8.79), 6.25 (95% CI 5.02-7.78), or 7.80 (95% CI 5.87-10.36) times more likely to use HIT compared to adults without high school education among people with multiple cardiovascular risk factors, one cardiovascular risk factor, or no cardiovascular risk factors, respectively.Over 2012-2018, HIT use increased nationally, with greater use noted among younger and higher educated US adults. Targeted strategies are needed to engage wider age, racial, education, and socioeconomic groups by lowering barriers to HIT access and use. The COVID-19 pandemic has significantly changed ambulatory care delivery, which has likely impacted the ability of adults living with cardiovascular disease (CVD) risk factors to manage their health conditions. Factors including shortages of testing supplies, personal protective equipment, state and health system mandates, and difficulty maintaining adequate staffing led to most providers deferring elective and annual physical examinations  or adaptMost recent estimates suggest almost half of the US population report having one CVD risk factor, such as obesity, high blood pressure, or diabetes . To prevThe current environment has provided an opportunity for a digital revolution in health care, with unparalleled, rapid expansion of telehealth and telemedicine. Previous literature showed that 74% of American adults access the internet, 57% of American households have broadband connections, and 61% of adults obtain health information on the web . HoweverWe used data from the National Health Interview Survey (NHIS) from 2012 to 2018. The NHIS is an annual survey that collects health-related information on a representative sample of the noninstitutionalized population of the United States . The NatWe used HIT use questions from the years 2012-2018 for the study. Respondents were asked, “During the past 12 months, have you ever used computers for any of the following: (1) to look up health information on the Internet, (2) to fill a prescription, (3) to schedule a web-based appointment with a health care provider, (4) to communicate with a health care provider by email?” If an individual indicated use for any of these four purposes, they were considered to have used HIT in the past 12 months. Participants were classified as “Used HIT for a general purpose” if they looked up health information on the internet and as “Used HIT for a clinical purpose” if they filled a prescription on the web, scheduled a web-based appointment, or communicated with a health care provider by email.2) or obese (>30.0 kg/m2) [The CVD risk factors included in the study were self-reported diabetes, hypertension, hyperlipidemia, and obesity, as these are the most common conditions at risk for heart disease . We iden0 kg/m2) . We straWe examined a range of household-, individual-, and health-related factors expected to impact HIT use. Individual-level characteristics included race/ethnicity , insurance type , age , education  and sex . Household-level characteristics included marital status (married and unmarried), geographic region  and poverty. Poverty was determined using the poverty income ratio variable in NHIS, which measures the ratio of the annual family income divided by the household-adjusted federal poverty level in dollars, as defined by the Census Bureau for that survey year . This vaThe unit of analysis was the individual. Sampling weights (assigned by the NHIS) were used to account for uneven data collection probabilities stemming from the NHIS sample design and nonresponse. SAS, version 9.4 (SAS Institute) was used for the analyses. Sampling weights were used to obtain nationwide representative estimates and standard errors because NHIS uses a multistage probability complex sampling design that incorporates stratification, clustering, and oversampling of some subpopulations . Weighted means along with 95% confidence intervals are reported for all continuous variables.The proportion of HIT use among respondents by CVD risk status  were compared for the years 2012 and 2018 using chi-square tests. We also compared characteristics of the respondents with and without HIT use for the years 2012 and 2018. Among HIT users, the proportions using the internet for clinical use and general use were also compared for the years 2012 and 2018.Using linear trend analysis, we then compared adults by CVD risk factor status, highest level of education, and age groups of 18-25, 26-44, 45-64, and ≥65 years to examine HIT use trends over the years 2012-2018. The annual percentage changes (APCs) of HIT use were calculated for each of the age, CVD risk factor, and education groups.Independent predictors of HIT use were identified using multiple logistic regression models adjusted for age and sex for each of the risk factor groups of one CVD risk factor condition, multiple CVD risk factors, and no CVD risk factors for the years 2012 and 2018.Among a total of 58,992 respondents in the years 2012  and 2018 , males comprised 45.5% of the total respondents in both 2012 and 2018. In 2012, 69.4% of the total respondents were non-Hispanic White and 12.5% were non-Hispanic Black, while in 2018, 66.9% of the total respondents were non-Hispanic White and 12.7% were non-Hispanic Black . In 2012In 2012, 41.6% of the total weighted sample of respondents looked up health information on the internet, representing 42.3 million Americans. Of those who used HIT, 6.8% filled a prescription on the internet, less than 5% made web-based appointments with their health care provider, and 5.8% communicated with their health care provider via email. In 2018, 54.2% of the total weighted sample of respondents looked up health information on the internet, representing 60.5 million. Approximately 11% filled a prescription on the internet, approximately 16% made web-based appointments with their health care provider, and 16.5% communicated with their health care provider via email .Overall, in 2012, 44.5% of the total weighted sample of respondents reported using HIT for any one of the four purposes listed above, representing 44.5 million, and in 2018, this proportion increased to 58.6%, representing 64.7 million.Prevalence of HIT use among respondents without any CVD risk factors  was significantly higher than respondents with one CVD risk factor  or multiple CVD risk factors  in 2012. Although there was an increase in the prevalence of HIT use in 2018 among all the CVD risk groups compared to 2012, the highest use of HIT was still among respondents without any CVD risk factors  compared to respondents with one CVD risk factor  or multiple CVD risk factors . A detailed comparison of types of HIT use by respondents with and without CVD risk factors in the years 2012 and 2018 is shown in In the linear trend analysis stratified by CVD risk status, age, and education from 2012 to 2018 , the APCRespondents with none of the CVD risk factors were the highest HIT users ; howeverIn 2012, among those with multiple CVD risk factors, college graduation was associated with the highest odds of HIT use  compared to adults without high school education. This gap remained in 2018, when college graduates were over 7 times more likely to use HIT  than adults without high school education. Similar associations were seen in those with a single CVD risk factor or no CVD risk factors .Among those respondents who reported no or multiple CVD risk factors, there was no difference in the odds of HIT use by health status. However, for those with one CVD risk factor, health status was associated with HIT use; in 2012, adults who reported their health status as “fair” were 1.49 times more likely to use HIT than adults who reported their health status as “excellent” (95% CI 1.17-1.89), and in 2018, they were 1.42 times more likely to do so . Overall, the significant predictors of HIT use were similar across all the three risk factor groups. In particular, after adjusting for health and sociodemographic factors, respondents who were relatively young, non-Hispanic White, female, and more educated; had private insurance and high income; and resided in the West were significantly more likely to be HIT users .HIT use increased by 10 to 15 percentage points in American adults over 2012-2018. Overall, the proportion of respondents using HIT for general purposes was greater than the proportion of people using HIT for clinical purposes in both 2012 and 2018. HIT users were more likely to be younger, female, and non-Hispanic White; have higher education and income; be married; and report their health status as very good or excellent.The widespread, easy access to the internet for various purposes in recent times may have boosted the overall increasing trends of HIT use from 2012 to 2018 among all the risk factor groups . Our finOur results also revealed wide variation in the odds of HIT use by individual, household-related, and health-related characteristics. Similar to previous studies, women had the highest odds of HIT use compared to men ,19. For Despite a significant recent increase in HIT use in the older population in recent times, a digital divide between younger and older persons persists ,19,21. AThe variations in HIT use related to race/ethnicity also deserve further attention. People who are White were more likely to use HIT than those in other race groups. A myriad of social and economic factors have likely created this divide, including the higher income, education, lifespan, and hence overall higher affordability and accessibility of HIT for White people compared to those in other race groups . ChronicAmong the types of HIT use, we observed that a majority reported seeking web-based health information compared to other types of use. This observation is in line with other studies showing that patients are increasingly relying on the internet as their primary source of answers to health-related questions ,31,32. GOur study has several strengths. This study is the first to use nationally representative data to examine the prevalence of and factors associated with HIT use among people with and without CVD risk factors. The survey response rate is very good at 65-77%. Further, the study has a large sample size and was able to measure the trends through multiple years, from 2012-2018. To our knowledge, this study provides the first national assessment of HIT use among adults with CVD risk factors prior to the COVID-19 pandemic.The general limitations of the NHIS data apply to this study as well. Firstly, the data are self-reported, and the questions pertaining to HIT use inquire about whether participants used HIT in the past 12 months, which means the responses could be subjected to recall bias. Second, the cross-sectional nature of the data limits the possibility to establish causal pathways between factors noted in our analysis and HIT use; cross-sectional data may also increase the risk of reverse causality. Further, the data lack information on English proficiency for the years 2012-2017. This is a limitation of the analysis, as English proficiency may have affected the rate of HIT use. Last, we are unable to quantify the amount of HIT use among the respondents, as some could be daily users and some could be monthly users; this may bear weight in the health access and knowledge of HIT tools.Our study provides a pre–COVID-19 assessment of HIT use among Americans with and without CVD risk factors. We found an increasing trend of HIT use among adults with and without CVD risk factors in the United States from 2012-2018. However, wide variation exists in use among Americans with CVD risk factors, who should be regularly accessing care. This variation has likely been exacerbated during the ongoing COVID-19 pandemic. Namely, older adults, racial and ethnic minority populations, and adults with multiple CVD risk factors are at high risk of having less access to HIT. A multipronged approach that includes education initiatives, affordable access to technology, and emphasis of health systems on creating platforms that all Americans can access are needed. Future studies to address these gaps are also needed to understand best practices.


Listeria monocytogenes ST7 strain occurred in Central Italy and caused 24 confirmed clinical cases. The epidemic strain was deeply investigated using whole-genome sequencing (WGS) analysis. In the interested area, the foodborne outbreak investigation identified a meat food-producing plant contaminated by the outbreak strain, carried by pork-ready-to-eat products. In the same region, in March 2018, the epidemic strain reemerged causing one listeriosis case in a 10-month-old child. The aim of this study was to investigate the phylogeny of the epidemic and reemergent strains over time and to compare them with a closer ST7 clone, detected during the outbreak and with different pulsed-field gel electrophoresis (PFGE) profiles, in order to identify genomic features linked to the persistence and the reemergence of the outbreak. An approach combining phylogenetic analysis and genome-wide association study (GWAS) revealed that the epidemic and reemergent clones were genetically closer to the ST7 clone with different PFGE profiles and strictly associated with the pork production chain. The repeated detection of both clones was probably correlated with (i) the presence of truly persistent clones and the repeated introduction of new ones and (ii) the contribution of prophage genes in promoting the persistence of the epidemic clones. Despite that no significant genomic differences were detected between the outbreak and the reemergent strain, the two related clones detected during the outbreak can be differentiated by transcriptional factor and phage genes associated with the phage LP-114.From May 2015 to March 2016, a severe outbreak due to  Listeria monocytogenes (Lm) and characterized by high hospitalization and fatality rates. Invasive infections mainly occur among pregnant women, the elderly, and immunocompromised people causing septicemia, meningitis, and maternal–neonatal infections.Invasive listeriosis is a severe foodborne disease caused by In Europe, in the framework of 2019 data collection, 28 Member States reported 2,621 confirmed invasive cases of listeriosis with an overall case fatality of 17.6%. The figures are increasing compared to 2018 and 2017 .Clinical listeriosis cases can occur as outbreaks , due alsLm  provides the highest possible subtyping resolution improving the surveillance, outbreak investigation, source attribution, and microbial population studies of Lm .Lm strains can be also identified based on the presence of phenotypic properties, such as virulence potential, but also persistence, or growth/survival under stress conditions prevailing in food or the host, using comparative genomics studies or genome-wide association studies (GWAS). This latter approach compares a large set of genomic data and associates them to specific phenotypic traits, allowing for the identification of markers or indicators of specific phenotypes. The GWAS analysis has been successfully used for identifying Lm genetic determinants involved in biofilm formation  and involved 24 confirmed clinical cases. The epidemic strain had an uncommon pulsed-field gel electrophoresis (PFGE) profile , showing a close genetic relationship with strains characterized by a different PFGE profile  and detected in three clinical cases that occurred in 2014, 2015, and 2016, respectively. During the outbreak investigation, several environmental and food samples were collected and analyzed to detect Lm and in particular the Lm epidemic strain.From May 2015 to March 2016, a severe outbreak due to A hog head cheese collected in a retail store (RS1) tested positive for the epidemic strain leading to the identification of the pork meat food-processing plant (FPP1) producing it, as the main source of the epidemic strain . StartinThe epidemic strain reemerged in March 2018 in the same Region causing a new sporadic case of listeriosis in a 10-month-old child . Very inAlthough WGS has allowed the source attribution and the resolution of the outbreak, there are no clear aspects on the evolutionary dynamics behind the coexistence of two related strains during the outbreak and on their reemergence after 2 years.Lm (ItNRL Lm) and the National Reference Centre for Whole Genome Sequencing of microbial pathogens: database and bioinformatic analysis.The aim of this study was to investigate the phylogeny of the epidemic and reemergent strains over time and to compare them with the closer clone with different PFGE profiles. We used a combined approach including phylogeny analysis and GWAS to compare all the interested genomes in order to identify genomic features potentially linked to the persistence and the reemergence of the outbreak. The data were retrieved from the database cured by the Italian National Reference Laboratory for Lm strains collected during the outbreak (2015–2016) (n. 144)   and the (n. 144) . Among tLm database as well. Metadata of the entire dataset are summarized in Additionally, eight ST7 strains not correlated with the outbreak were randomly selected as “Outgroup” (Og) from the ItNRL In the dataset, 174 of 178 strains showed the PFGE patterns GX6A16.0183 and GX6A12.0063 linked to the outbreak (including “Epidemic Cluster—EpiCl” and “Re-emergence Cluster—ReE” of 2018); four strains showed different PFGE combined profiles  (“Cluster 2”—C2) detected in 2015–2016 and during the reemerging outbreak (ReE) in 2018 (2018.TE.6199.1.14).To be included in the outbreak, the 178 strains were screened with a real-time PCR screening assay performed and described previously by Lm strains was extracted using the Maxwell 16 Tissue DNA Purification Kit  according to the manufacturer’s protocol, and the purity of the extracts was evaluated by NanoDrop . Starting from 1 ng of input DNA, we used the Nextera XT DNA chemistry  for library preparation according to the manufacturer’s protocols. WGS was performed on the NextSeq 500 platform  with the NextSeq 500/550 Mid Output Reagent Cartridge v2 .DNA of all the 186 de novo assembly of paired-end reads was performed using SPAdes v3.11.1  and the clonal complex (CC) were deducted For verifying the relatedness among the isolates from the outbreak and those belonging to ST7 and collected during the 2000–2020 period, a core-genome MLST (cgMLST) analysis was performed. As allele-calling method, a chewBBACA pipeline  was exec2 and then utilized for the following pan genome analysis.The single-nucleotide polymorphism (SNP) analysis was conducted using the CFSAN pipeline , and theLm strains was carried out using Prokka  was used to identify genes significantly associated with the EpiCl, ReE, and C2 strains. GWAS was performed using Pyseer  which as3In order to check the confounding population stratification, a pairwise comparison was performed based on the ML tree for population structure correction. Moreover, information regarding the obtained significant genes was retrieved using BLAST.Lm genomes were manually screened using the tools “Virulence,” “Antibiotic Resistance,” “Metal and Disinfectants Resistance,” “Stress Islands,” and “Listeria Genomic Islands,” available on the BIGSdb platform.4In order to detect the absence/presence of loci encoding virulence- and persistence-associated genes, all the 186 Furthermore, PlasmidFinder  was querLm as average read quality ≥30, average coverage ≥ 20, de novo assembly seq. length between 2.7 and 3.2 Mbp, and number of contigs ≤300 , ReE strains (24 genomes), and one C2 strain (2018.TE.6199.1.14) shared the same allelic profile  from EpiCl strains. Moreover, the clinical strain isolated from the child (2018.TE.5376.1.4) in 2018 was distant two and three SNPs from two EpiCl strains detected from the FPP environment (2016.TE.10956.1.29) and salami (2016.TE.6891.1.86), respectively. Additionally, the SNP matrix showed that the ReE strain 2018.TE.5376.1.4 was distant one SNP from the clinical EpiCl strain 2016.TE.6891.1.6.Furthermore, the SNP analysis showed a close correlation between some EpiCl clinical strains and those isolated from the environment during the ReE of the outbreak. In particular, seven clinical isolates  were spread out with three SNPs on average . Meanwhile, 17 ReE isolates from environment  were distant three SNPs on average  from EpiCl strains isolated from hog head cheese, salami, pancetta, loin, and bresaola.Five outgroup strains clustered together with those belonging to C2, except for 2018.TE.6199.1.14. Meanwhile, the remaining three strains  showed to be phylogenetically distant from the genomes of the dataset.Finally, the C2 strain detected during the ReE (2018.TE.6199.1.14) showed to be distant three SNPs from EpiCl strains isolated from hog head cheese, salami, pork cheek, and environment.Lm strains was created using annotated draft assemblies. We identified a core of 2,768 coding sequences (CDSs) present in each of the sequenced isolates, while the total accessory genome was composed of an additional 333 CDSs. Identified genes included 62 soft-core genes , 46 shell-genes present in 15–95% of the genomes, and 225 genes present in <15% of the genomes. According with the phylogenetic analysis, the pan genome of all the isolates belonging to the EpiCl, including those from the ReE, showed to be very similar, as reported in A predicted pan genome of 190 p-values below these thresholds were deemed as significantly associated with the specific subpopulation  and 2.74 (EpiCl + ReE) were calculated. The pulation . MeanwhiGenome-wide association study revealed that the EpiCl + ReE and C2 subpopulations were characterized by the presence of genes encoding holin, class I SAM-dependent methyltransferase, single-stranded DNA-binding protein, pentapeptide repeat-containing protein, sugar-phosphate nucleotidyltransferase, site-specific integrase, and N-acetylmuramoyl-L-alanine amidase. Phage terminase small subunit P27 family, phage tail protein, and three hypothetical proteins, one of which was harbored by Listeria phage LP HM00113468, were also detected .Furthermore, several proteins phage-related with several functions such as DUF1642 domain-containing protein, which is harbored in Listeria phage LP-114 (NC_024392.1), phage gp6-like head-tail connector, HK97 gp10 family phage, HK97 family phage prohead protease, phage tail tape measure, phage major capsid and phage portal proteins, and 13 more hypothetical proteins, which have different functions, were significantly associated with C2 strains .No significant differences were highlighted among the EpiCl, C2, ReE, and Og strain genome .Lm strains used in this study. Moreover, two epidemic strains isolated from salami (2016.TE.5761.1.69 and 2016.TE.6891.1.72) carried also incomplete alleles in LM9005581_70010, LM9005581_70011, and LM9005581_70012 genes. No isolates showed to carry a PMSC mutation in the inlA gene encoding for a truncated internalin A protein. Moreover, in all the genomes tested prfA, plcA, hly, mpl, actA, and plcB; the adherence genes ami and lapB; and the comK prophage were found. None of the strains carried LIPI3 island, gltA, and gltB genes.Regarding virulence genes, 57 different genes, including incomplete alleles, were detected in overall 186 Six Listeria genomic islands were detected in 186 strains while LGI-2_LMOSA2269, LGI-2_LMOSA2270, LGI-2_LMOSA2271, and LGI-2_LMOSA2272 were missing in four clinical strains belonging to EpiCl and C2. Finally, 2016.TE.5761.1.69 and 2016.TE.6891.1.72 carried incomplete alleles in LGI-2_LMOSA2270.Using the functions “Metal and Disinfectants Resistance,” no disinfectant resistance genes and toxic compounds were detected except for the Og strain 2020.TE.122547.1.5 (Tn6188_qac). Meanwhile, the Stress-Survival Islet 1 (SSI-1) for tolerance to environmental stresses was identified in the 186 genomes of the dataset.Lm genomes showed to carry lincomycin- (lmo0919) and sulfonamide- (sul) intrinsic resistance genes; meanwhile, the epidemic cluster 2020.TE.3350.1.8 isolated from hog head cheese harbored also tetracycline resistance genes tetM and tetS.The 186 The replicon plasmid J1776 (CP006612) was detected in all the isolates belonging to EpiCl, ReE, and C2. This plasmid harbored a plasmid-borne cadmium resistance gene contained in transposon Tn522.Lm outbreak that occurred in Central Italy between 2015 and 2016 and reemerged in 2018. The main aim was to identify any genetic determinant involved in the coexistence of the two related clones during the outbreak and in the reemergence using different WGS approaches such as cluster analysis and GWAS. The cluster analysis, based on cgMLST and SNP analysis, was performed to define the genetic relationships between EpiCl strains and C2 and ReE strains.In this study, we explored the genome of two ST7 clones related to a severe Lm EpiCl isolates and the ReE shared the same allelic profile. Interestingly, one strain with a PFGE profile matching with C2, detected in 2018 during the reemergence, showed the same allelic profile of EpiCl and ReE strains.Despite that the allele-based method did not reveal any significant difference among the isolates related to the outbreak, the results obtained were congruent with SNP analysis showing that most of the The SNP analysis confirmed the allelic close correlation between EpiCl and ReE strains. More in detail, the cluster analysis revealed the strict correlation between the ReE clinical strains isolated from a 10-month-old child and two strains isolated from an environmental swab and a salami sample isolated during the outbreak. Moreover, the same correlation was highlighted between clinical strains, environmental strains, and food strains  detected during EpiCl and ReE.In a recent study, To date, the source for the clinical ReE strain has not yet been attributed. Phylogenetic analytical results showed the key role of the pork production chain  in keeping the epidemic clone in circulation, or the possible reintroduction along the food production chain. Those hypotheses could be supported by the pairwise distances detected among EpiCl and ReE strains.The results suggested the presence of a persistent clone over time, probably resident in a specific FPP environment and the possible reintroduction of closely related clones along the entire food chain, as already described by Moreover, the closer similarity existing between some ReE strains and the EpiCl clones isolated from a FPP that was promptly closed in 2016 suggested the presence of additional contaminated FPPs downstream in the supply chain, as suggested by The hypothesis is supported by Furthermore, the possible existence of a common ancestor between the two similar clones detected during the outbreak (EpiCl and C2) could be confirmed by the close correlation existing between the only isolate belonging to C2 detected during the reemergence and EpiCl strain isolates from hog head cheese, salami, and pork cheek.Listeria pathogenicity island 3 (LIPI-3), encoding the hemolytic and cytotoxic factor Listeriolysin S (LLS)  , was detynthesis . Consistic genes .comK prophage, LIPI2_inlII, and LIPI1, which consists of six virulence genes . This prophage is known to play a key role in the host infection . However, it was evident that all the 190 isolates harbored nfection .qac gene, encoding an efflux-pump specific for quaternary ammonium compounds and in particular associated with the export of benzalkonium chloride  and transposase Tn3 and can contribute to the adaptation of Lm in the FPP environment.Our results showed that Listeria Genomic island 2 (LGI2), LGI-2_LMOSA2310 homolog, and LGI3 (LGI-3_LmUB3PA_1685) were found in the 186 genomes analyzed. LGI2 seemed not to carry arsenic resistance genes, unlike the homolog described by Tn552 were detected, carried by the plasmid J1776, which was common to all the isolates belonging to the EpiCl, C2, and ReE strains. This plasmid was usually found in CC9 and CC121 considered as clones persistent in FPPs  as described by Furthermore, the XRE family transcriptional regulator, which is involved in stress tolerance and virulence , enricheprfA, qoxABCD (which encodes enzyme for oxidative stress response), and cggR, which encodes the central glycolytic gene regulator  and Og strains, in order to identify the genetic determinants possibly involved in the outbreak and in the reemergence of the epidemic strain.In this study, genomes of EpiCl and ReE The cgMLST analysis was not enough discriminatory to identify differences; on the contrary, the SNP analysis provided significant elements that could explain the dynamics of the reemergence. The phylogenetic analysis uncovered the different evolution path of the clones EpiCl and C2, characterized by a common ancestor.Moreover, the repeated detection of both clones suggested the strict correlation among EpiCl and C2 clones, together with ReE, all strictly linked to the pork production chain, probably correlated with the presence of truly persistent clones and the repeated introduction of new ones.In addition, the GWAS results, together with the genomic characterization, revealed the contribution of prophage genes and transcriptional factors in promoting the persistence of EpiCl, C2, and ReE clones in the FPP environment and as a consequence in finished products.Lm in the future.The combined approach  could be applied in genomic datasets including a larger number of isolates in order to correlate them and to unveil the evolutionary pathway of ST7 strains. This combined approach could help to understand if strains causing outbreak are still circulating in order to plan and carry out a more deep investigation in FPP environments and clean the environment, avoiding a possible reemergence of The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ACh and YS conceptualized and designed the study. ACh performed the bioinformatics analysis. FG, GC, MT, VA, ACo, MD, PC, and CM performed all the microbiological and molecular analyses and contributed to the interpretation of data. ACh, FG, and YS wrote the original draft. YS, SR, GM, GB, and FP supervised the work and contributed to the interpretation of data. All the authors contributed to the manuscript drafting.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Background: In the past few years, several randomized trials have clearly shown that endovascular treatment (ET) in addition to intravenous thrombolysis (IVT) is superior to IVT alone in patients with proximal cerebral arterial occlusions. However, the effectiveness of ET in pre-stroke dependent patients  is uncertain.Methods: Using our prospectively obtained stroke database, we analyzed the impact of pre-stroke dependence on the rates of poor outcome (discharge mRS 5–6), in-hospital death, infarct sizes, and symptomatic intracranial hemorrhage (SICH) in patients with distal intracranial carotid artery M1 and M2 occlusions during two time periods.Results: From 1/2008 to 10/2012, a total of 544 patients (455 without and 89 with dependence) were treated with IVT, and from 11/2012 to 12/2019 a total of 1,061 patients (919 without and 142 with dependence) received ET (with or without IVT). Irrespective of the treatment modality, the dependent patients had significantly higher rates of poor outcome , death , or SICH  than independent patients. In dependent patients, ET significantly reduced the rates of poor outcome  and led to smaller infarcts, whereas the rates of in-hospital death  or SICH  were comparable between both treatment modalities.Conclusions: Compared with IVT, ET avoids poor outcome and leads to smaller infarcts in dependent patients. However, the overall high rates of poor outcome in this patient population stress the importance to perform decisions based on a case-by-case basis. With the publication of five landmark trials in the year 2015, endovascular treatment (ET), in addition to intravenous thrombolysis (IVT), has become an established treatment for acute stroke patients with proximal large vessel occlusions . Since pTherefore, we analyzed the impact of preexisting dependency on clinical outcome at the time of discharge as well as the radiological outcome in patients with anterior circulation large vessel occlusions during two time periods using our prospectively obtained database of a high-volume stroke center in which patients with prior disabilities  are not routinely excluded from ET or IVT. During the first time period, all patients had been treated with systemic thrombolysis, whereas an endovascular therapy with stent retrievers (with or without systemic thrombolysis) was routinely used during the second time period.The study population comprised all acute stroke patients with a proximal large vessel occlusion within the anterior circulation  and who had been treated from January 2008 through December 2019. The main exclusion criteria were (1) distal occlusions beyond the M2 segment, (2) posterior circulation strokes, and (3) bilateral strokes. From January 2008 to November 2012, all patients were treated with systemic thrombolysis alone, mainly using the inclusion and exclusion criteria, as well as a drug dose for thrombolytic treatment of the NINDS study protocol. At our institution, patients can be treated up to 4.5 h after symptom onset, and there is no upper age limit for eligibility.Endovascular treatment with or without systemic thrombolysis was performed after November 2012 in all patients who presented within 6 h  of symptom onset (using no upper age limit or specific imaging exclusion criteria).In each patient, the following demographic data and stroke risk factors were collected: age, gender, arterial hypertension, diabetes, and atrial fibrillation. The time to thrombolysis (or to thrombectomy in patients without prior systemic thrombolysis) from stroke onset was also noted.Information on the pre-stroke living situation was obtained from the patients themselves, their families, their relatives, or their primary care physicians as well as outpatient medical personnel. Patients who were dependent on the daily help of others before the stroke (either coming from home or coming from a nursing home) were classified as dependent, reflecting a mRS of 3–4; all other patients were classified as independent (mRS 0–2). At our institution, bedridden patients with severe disabilities, i.e., with a mRS of 5, are usually not treated with ET. Since the exact pre-stroke mRS is often misjudged, especially in the emergency setting, dependent patients were not further subcategorized between a mRS value of 3 or 4 , 13.The protocol of our stroke registry had been approved by our local ethics committee. Because of the retrospective character of this study, the lack of treatment influence, and the clinical data having been collected as part of a national quality control program, the study was exempted from informed consent. Using our stroke database, the clinical outcomes of patients treated before December 2014 as well as a subgroup analyses have been published previously .Non-enhanced CT (NCT) and CT angiographic acquisitions before treatment were performed on a 4-row Multisection CT scanner .NCT was performed with the patient in a head holder in the transverse plane. Using the following parameters, incremental CT acquisitions of the brain were obtained: 120 kVp, 250 mA, 2-s scan time, and 5-mm section thickness.To allow visualization of the vascular tree from the distal common carotid artery to the intracranial vessels, the CT angiography covered the region from the fifth vertebral body up to the vertex. The following parameters were used: 120 kVp, 200 mAs, 4x 1-mm collimation, 5.5 mm/rotation table feed, and 0.5 s rotation time. A total of 100 ml of contrast material was injected intravenously at a flow rate of 4 ml/s using a power injector. For follow-up studies, repeated CT or MR scans were obtained after 1–3 days after treatment or immediately in case of clinical worsening.CT and MR image analyses were performed jointly by a board-certified neuroradiologist  and a stroke neurologist  on a high-resolution monitor.Since CTA source images are superior to non-contrast CT images to detect early ischemic changes, they were used to determine the Alberta Stroke Programme Early CT score (CTA-SI-ASPECTS) . The folFunctional outcome was assessed by a senior vascular neurologist who was certified for NIHSS and mRS scoring . The mRS at the time of hospital discharge was used for early clinical outcome analyses. The main outcome measures were a poor clinical outcome (discharge mRS of 5–6), in-hospital death, infarct sizes, and SICH rates.In a first step, we compared the clinical and radiological outcomes between pre-stroke dependent (mRS ≥3) and independent (mRS 0–2) patients after either IVT or ET. In a second step, we then analyzed the impact of ET on the clinical and radiological outcomes in pre-stroke dependent patients compared to IVT.t-test or, in case of abnormally distributed data, with a Mann–Whitney U-test.Continuous values were expressed as mean ± standard deviation or as median ± interquartile range (IQR). Nominal variables were expressed as count and percentages. For comparisons of categorical data, two-tailed chi-square statistics with Yates correction and univariate Fisher's exact test were used. Fisher's exact test was used when the predicted contingency table cell values were less than five. Analyses of continuous variables were performed with an unpaired Student's P in 0.05, P out 0.1) was applied to determine the independent predictors of a poor clinical outcome (mRS 5–6). The following variables were considered: age, sex, treatment modality, baseline NIHSS, baseline and follow-up SI-ASPECTS, diabetes, hypertension, atrial fibrillation, prior stroke, occlusion site, and SICH. Results are presented as odds ratios (ORs) with 95% confidence interval.A stepwise, forward, multiple-regression analysis .A value of The data that support the findings of this study are available from the corresponding author upon reasonable request.The IVT group comprised a total of 544 patients, and 1,061 patients had received ET, respectively. Among the IVT patients, 455 (84%) were previously independent and 89 (16%) were dependent before stroke. In the ET group, 919 (87%) patients were functionally independent before the current stroke, whereas 142 (13%) patients were dependent of the help of others before their stroke. p < 0.001), in-hospital death , and SICH  were significantly higher in previously dependent patients than in independent patients. The infarct sizes were comparable between both treatment groups .After either IVT or ET, a total of 568/1,605 patients (35%) had a poor outcome (mRS 5–6), and 212/1,605 (13%) died in the hospital. Overall, 68 patients (4.2%) developed a SICH using the ECASS III definition. The rates of poor outcome . The rates of in-hospital death did not differ significantly between and ET and IVT .The clinical and imaging outcome data are given in p < 0.01), whereas the SICH rates were comparable between both treatment groups .The infarct sizes were significantly smaller after ET than after IVT  were functionally dependent before their current stroke, while in the Endovascular Recanalization With Solitaire Device vs. Best Medical Therapy in Anterior Circulation Stroke Within 8 h trial, there was only one patient (0.5%), respectively , 3.Recently, several single-center studies and registries have reported the beneficial effects of ET in pre-stroke dependent patients , 11, 18.Despite the overall positive effect of ET on clinical outcome, pre-stroke dependent patients had significantly higher rates of in-hospital death than independent patients irrespective of the treatment modality in our case series. In good agreement with this finding is the fact that the mortality rates at 90 days were significantly higher in patients with a pre-stroke mRS of 2–3 (40%) than independent patients (14%) after endovascular therapy in a recent analysis of two large comprehensive stroke centers in the US . In the The overall rates of SICH were 4.1% after ET or 4.4% after IVT, which is within the magnitude of SICH rates observed in the large, randomized trials , 4. In cNotably, ET did not affect the incidence of SICH in patients with prior disabilities, stressing its safety in this patient population.In this study, we also analyzed the impact of ET on infarct sizes in dependent patients compared with IVT. Using the follow-up ASPECTS as a marker of infarct extent, we observed significantly smaller infarct sizes after ET than after IVT in dependent patients. These data corroborate previous findings, albeit mainly in independent patients, in which the final infarct volumes were significantly smaller after ET than after IVT , 22. AltThis study has certain limitations. First, the data were obtained retrospectively in a single academic center and in a non-randomized fashion. This limits the generalizability of our results, which need to be corroborated in a randomized controlled trial. Second, the causes for preexisting disability was not obtained in our study, all the more considering that dependent patients likely harbor complex comorbid conditions. Third, we used the mRS at the time of discharge to determine the clinical outcome instead of the widely accepted 90 days. In support of this approach, a reanalysis of the NINDS tPA stroke trial revealed a strong correlation between the day 7 mRS and the 90-day mRS . FinallyIn everyday clinical practice, a substantial proportion of acute stroke patients with large vessel intracranial vessel occlusions have preexisting disabilities. In these patients, ET can be performed safely and avoids a poor outcome compared with IVT. However, the high death rates in this patient population stresses the importance to perform decisions based on a case-by-case basis.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Local Ethics Committee of the Ärztekammer Bremen. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.AK, CR, and PP contributed to the study design, data acquisition, critical data review and interpretation of data, and contributed to the primary manuscript writing and tables. CR, MP, MA, HH, and AS contributed to data acquisition and interpretation of data. AK, CR, MP, MA, HH, AS, and PP contributed to the critical revision and final approval of the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


In order to mitigate the food animal sector’s role in the growing threat of antimicrobial resistance (AMR), the World Health Organization (WHO) suggests the use of lower tier antimicrobials, such as florfenicol. Florfenicol has two dosing schemes used to treat primarily bovine respiratory disease. In this study, the objective was to characterize the plasma and gastrointestinal pharmacokinetics of each dosing regimen and assess the effect of these dosing regimens on the prevalence of resistant indicator bacteria over time. Twelve steers underwent abdominal surgery to facilitate the placement of ultrafiltration probes within the lumen of the ileum and colon, as well as placement of an interstitial probe. Following surgery, cattle were dosed with either 20 mg/kg IM every 48 h of florfenicol given twice (n = 6) or a single, subcutaneous dose . Plasma, interstitial fluid, gastrointestinal ultrafiltrate, and feces were collected. Pharmacokinetic analysis demonstrated high penetration of florfenicol within the gastrointestinal tract for both the high and low dose group . There was no significant difference noted between dosing groups in proportion or persistence of phenotypically resistant bacterial isolates; however, the percent of resistant isolates was high throughout the study period. The recommendation for the use of a lower tier antimicrobial, such as florfenicol, may allow for the persistence of co-resistance for antibiotics of high regulatory concern. In the United States, the antimicrobial resistance (AMR) crisis is ever growing; more than 2.8 million antibiotic resistance infections occur each year, with more than 35,000 human mortalities . Human eEnterococcus spp. may carry plasmids with co-resistance genes to florfenicol [cfr gene (named for chloramphenicol and florfenicol resistance) also mediates resistance to both linezolid and florfenicol in methicillin-resistant staphylococci [E. coli, common resistance patterns include resistance to florfenicol, ampicillin, ceftiofur, and tetracycline [Florfenicol is a fluorinated analog of thiamphenicol and chloramphenicol . Florfenrfenicol . The cfrylococci . Thus, tacycline ,7.There are two injectable dosing regimens of florfenicol in veterinary medicine, a lower intramuscular dose (20 mg/kg IM) given twice, 48 h apart, or a single, higher subcutaneous injection (40 mg/kg SC). Each dosing regimen has its own respective withdrawal interval; 28 days following the second administration of the intramuscular injection and 38 days following the subcutaneous injection. A relationship exists between sub-therapeutic drug concentrations and the amplification of a resistant sub-population of bacteria. It is believed that when dosing lower concentrations of antimicrobials for a longer period of time, maximum proliferation of a resistant sub-population of bacteria occurs; this is compared to higher concentrations of the same antimicrobial administered once early in the disease process [E. coli and Enterococcus were utilized as marker organisms to gauge antimicrobial susceptibility and resistance patterns that could be passed along to humans.In 1996, the FDA, USDA, and CDC established the National Antimicrobial Resistance Monitoring Systems (NARMS) as a surveillance program to monitor antimicrobial susceptibility of enteric pathogens in humans, animals, and retail meats. NARMS also became involved in epidemiology studies assessing risk factors and clinical case outcomes of infectious agents with specific resistance patterns. In the animal sector, E. coli and Enterococcus and their associated studied antibiotic were utilized. The overall hypothesis for the study was the prevalence of resistant bacterial isolates would be higher and persist longer in steers administered the repeated, lower dose of florfenicol. A secondary hypothesis was that the gastrointestinal concentrations of florfenicol would be high, leading to a subsequent rise in resistant bacterial isolates.The overall objective of this study was to (1) characterize the gastrointestinal pharmacokinetics of florfenicol and (2) compare the prevalence of resistant bacterial isolates over time between the two labeled dosing regimens for florfenicol in cattle. In order to assess the human implications associated with florfenicol administration, human breakpoint concentrations for ®, Merck Animal Health) or transdermal flunixin meglumine .This study was approved by North Carolina State University’s Institutional Animal Care and Use Committee. Twelve healthy 6–7 month old steers (153.3–251.8 kg) were enrolled in the study. The study size was based on previous gastrointestinal pharmacokinetic studies in order to demonstrate differences between the two dosing regimens ,10. They®, Merck Animal Health) intramuscularly every 48 h (n = 6) twice or a single 40 mg/kg subcutaneous dose (n = 6). The steers were housed in pairs (one from each treatment group) and fed grass hay with supplemental grain and free access to water for the duration of the study. Feces and dirty shavings were removed to prevent cross-contamination by means of ingesting potential excreted drug.Twenty-four to 48 h after surgery, the steers were dosed with either 20 mg/kg florfenicol  was placed. Blood was obtained at 0, 15, and 30 min, and 2, 4, 8, 12, 24, 36, 48 h, 48 h 15 min, 48 h 30 min, 49, 50, 52, 56, 60, 72, 84, 96, 120, 144, and 168 h. The blood was spun down at 1000× g for 10 min. The plasma was transferred to cyrovials and stored at −80 °C until analysis.To facilitate blood collection, prior to surgery, an intravenous jugular catheter . This was done by connecting the probe onto a needle of the vacuum vial needle holder at the end of the tubing. The external tubing was sutured in place along the flank. Intestinal ultrafiltrate was collected by changing the tubes at the following time points: 0, 2, 4, 6, 8, 12, 24, 26, 28, 30, 32, 36, 48, 72, and 96 h post high-dose drug administration, and additionally 50, 52, 54, 56, 60, 74, 76, 78, 80, and 84 h post low-dose drug administration. The fluid was collected into cyrovials and stored at −80 °C.An ultrafiltration probe was also used to collect interstitial fluid (ISF); this probe was placed in the subcutaneous space at the withers. The free end of the probe was connected to an evacuated tube in the same fashion and sutured along the back. The evacuated tube was changed at 0, 2, 4, 8, 12, 24, 36, 48, 50, 52, 56, 60, 72, 84, 96, 120, 144, and 168 h. The fluid was transferred into cyrovials and stored at −80 °C.The samples were analyzed for florfenicol using reverse phase high-pressure liquid chromatography (HPLC) with ultraviolet (UV) absorbance detection for plasma samples and fluorescence detection for tissue fluids with methods previously used and validated in our laboratory , which h®, St. Louis, MO, USA) was used to determine the values for pharmacokinetic parameters. Plasma, ISF, and intestinal drug concentrations were plotted on linear and semi-logarithmic graphs for analysis and for visual assessment of the best model for pharmacokinetic analysis. Specific models  were determined for best fit based on visual analysis for goodness of fit and by visual inspection of residual plots for the plasma samples.For the pharmacokinetic analysis, a computer program , time to peak concentration (Tmax), area under the plasma-concentration vs. time profile (AUC), and the elimination half-life (t ½). The ISF and intestinal ultrafiltrate samples were corrected for lag time. This was determined by measuring the volume of the ultrafiltration tube and dividing by the flow rate (μL/min) for each fluid type. The average lag time per fluid type was then used to correct for the actual sample collection time.The relative drug transfer from the plasma compartment to the ISF and intestinal fluids was measured by calculation of a penetration factor. The penetration factor was determined by the ratio of AUC for the tissue fluid  to the AUC for plasma as follows:Penetration Factor = AUC Tissue Fluid or ISF/AUC Plasma.MAX, TMAX, and intestinal penetration.Individual Wilcoxon ranked sum tests were used to determine significance between dosing groups for the following pharmacokinetic parameters for all fluid types : AUC, half-life, C®, Nasco, Fort Atkinson, WI, USA) and stored on ice until delivered to the laboratory.Feces was collected manually from the rectum. Time points for feces collection were 0, 12, 24, 36, 48, 60, 72, 84, 96, 120, 144, 168, and 192 h, and then weekly post first drug administration until day 38. The samples were placed into bags  on selective media; E. coli concentrations were determined on DifcoTM MacConkey Agar  and Enterococcus concentrations were determined on DifcoTM m-Enterococcus Agar . The MacConkey Agar plates were incubated overnight at 37 °C, while the Enterococcus plates were incubated for 48 h at 37 °C. Dilutions that yielded colony counts between 30–300 were used; the three replicates were averaged to determine the quantity of both E. coli and Enterococcus at each time point. From the plates that were used to determine the quantity of E. coli and Enterococcus, eight isolates were randomly selected and were streaked for isolation onto Columbia agar with 5% sheep blood  and incubated overnight at 37 °C. After incubation, each suspected E. coli isolate was indole tested . Each isolate was then stored in a cryogenic vial containing LB Broth  supplemented with 25% glycerol . They were vortexed and frozen at −80 °C as a pure growth. Enterococcus isolates were speciated using MALDI-TOF and frozen as stated above.One gram of feces was weighed and placed into either 9 mL of EC broth  or 9 mL of Phosphate Buffered Saline  for quantification of TM, BD), MacConkey containing tetracycline , Saint Louis, MO, USA), ceftiofur hydrochloride , or ampicillin , or m-Enterococcus containing tetracycline (16 μg/mL), ceftiofur hydrochloride (8 μg/mL), or ampicillin (concentration of 16 μg/mL). Plates were incubated and counted; isolates were characterized and saved as described above. Isolates were considered resistant if they grew on their respective media containing antibiotics. This design is shown in In addition to being plated on selective agar, 100 μL from each dilution of EC and PBS was also plated onto nutrient agar  and AUC (p = 0.005) between the dosing groups, both being increased in the higher, single dose group. A non-compartmental analysis was used for the ISF and intestinal ultrafiltrate because there were insufficient samples to fit a model to these data. The geometric means and geometric CV% for selected pharmacokinetic parameters for each dosing group are shown in MAX (p = 0.01) that was higher in the low dose group.After comparing multiple one and two compartment models and a non-compartmental analysis, an extravascular, one compartment model with first order input and elimination with no lag time was the best fit for the plasma data based upon residual error plots, Akaike information criteria (AIC), and observation of observed vs. predictive plots. A comparison of the drug concentration of each fluid type per dosing group can be observed in E. coli or Enterococcus were obtained on selective media without antibiotics and nutrient agar at all time points. The average concentration (log10 CFU/g) of E. coli and Enterococcus can be observed in E. coli (0–24 h) and a decrease (0–24 h) in Enterococcus immediately after administration of florfenicol. E. coli isolates phenotypically resistant to ampicillin, ceftiofur or tetracycline. At time point zero, there was a high proportion of resistant isolates observed for both ceftiofur and tetracycline; these both then decrease. Enterococcus for ampicillin, ceftiofur or tetracycline. There was little Enterococcus growth on any of the ampicillin infused plates. There were no significant differences noted at the predetermined individual time points when comparing within or across treatment groups for either E. coli or Enterococcus spp.  and a higher, single subcutaneous dose. In addition, indicator enteric organisms, MAX and TMAX values when compared to the study conducted by Foster et al. However, in this study we observed a longer half-life (56.95 h), which was more similar to the study conducted by Kawalek et al. (51.9 h). The AUC measured in our study is higher than the values of either prior study. The reason for this difference is undetermined. The study conducted by Lobell et al. assessed florfenicol drug concentrations following a 20 mg/kg IM dose administered only once. Although the number of doses administered was different, the mean pharmacokinetic parameters were similar to our study.For the plasma pharmacokinetic drug analysis, there was a significant difference between dosing groups in the AUC and T ½, with both being increased in the higher, single dose group. The difference observed between each dose is likely related to the 2× higher dose. This is expected to increase the total drug absorbed, which is reflected in the AUC. The longer T ½ from a higher dose is likely caused by slow absorption from a higher volume of injection. Florfenicol can be irritating to tissues and a higher subcutaneous administration volume could lead to more erratic and prolonged absorption, which then could lead to a longer T ½. In pharmacokinetic terms, this is known as the “flip-flop” effect, where the rate of absorption influences the terminal slope of the plasma vs. time profile . PreviouMeasurement of drug concentrations in plasma includes both protein bound and unbound drug. Only the free concentration is microbiologically active . Free, nE. coli isolates collected from calves with diarrhea; all isolates studied were found to be resistant to at least four antimicrobials, while 77% were resistant to at least nine antimicrobials [E. coli were studied in the feces of healthy lactating dairy cattle [E. coli was observed in 40% of the isolates. In this study, the most common resistance patterns identified were as follows: florfenicol and tetracycline (35.8%); ampicillin, florfenicol and tetracycline (14%); and florfenicol, ampicillin, tetracycline, and ceftiofur (0.45%). The prevalence of these specific resistance profiles influenced the decision for this study as to which antibiotics to assess. To understand the underlying genetic association with the phenotypic resistance detected, further genetic analysis would need to be conducted.Bacterial resistance to florfenicol falls into three main categories: minimizing intracellular concentrations, modification/protection of the antibiotic target, and modification of the antibiotic. Many of the genes responsible for florfenicol are found on mobile genetic elements, allowing for an increased probability of horizontal gene transfer and transfer of florfenicol resistance genes through many genera of bacteria. The presence of florfenicol resistance genes has been documented on plasmids that convey resistance to other antimicrobial classes, allowing for the potential of co-resistance to develop. In one study, florfenicol resistance was assessed in crobials . The mosE. coli or Enterococcus using CLSI interpretations (E. coli and Enterococcus as indicator organisms because of their strong presence in the gastrointestinal tract, their importance in the acquisition and transmission of antimicrobial resistance genes, their human clinical importance, and their repeated use in surveillance systems of public health. For either indicator organism, there was no significant difference observed in the concentration obtained per dosing group; however, there were elevated concentrations observed at the respective drug residue withdrawal interval. This indicates there is a potential for resistant bacterial isolates to enter the food chain. An incidental finding in this study was the unexpected level of ceftiofur and tetracycline resistant E. coli isolates prior to the start of this study. Tetracycline resistance is prevalent among bacterial isolates from cattle [For this study, the drug concentrations that were included in the selective media were the breakpoint concentrations determined for either etations . Althougm cattle ,21. ThesE. coli and Enterococcus isolates at each time point. This was compared to the pharmacokinetic parameters. max in the ileum and colon in the high dose group compared to the low dose group. The Cmax is also higher in the ileum for the high dose group. The higher Cmax may increase selection for more resistant bacteria in the gut microflora and cross select for resistance to other antimicrobial classes, such as beta-lactams and tetracyclines.In an effort to correlate the intestinal drug concentrations with the prevalence of resistance, we calculated the proportion of isolates demonstrating phenotypic resistance compared to the concentration of all E. coli or Enterococcus. These two species were chosen as representatives of Gram-negative and Gram-positive bacterial species, respectively. They are also marker species utilized by NARMS to conduct surveillance for resistance. It has also been noted that these two species harbor and share antimicrobial resistance genes with other bacterial species [E. coli or Enterococcus. Observing the proportion of resistant isolates according to CLSI breakpoints was our primary objective. Finally, these was a relatively small sample size. It is undetermined if a larger sample size would have produced different results.There were several limitations to this study. First, the only bacterial species studied were either  species . In addiE. coli or Enterococcus isolates to ampicillin, ceftiofur, or tetracycline. We observed high concentrations of active florfenicol in the gastrointestinal tract lumen, which may select for resistant bacteria that can be shed in the feces and environment. Thus, use of this lower tier antimicrobial can select for antimicrobial resistance to other antimicrobial classes of high regulatory concern and those classified by the WHO as critically important. This suggests that there needs to be additional evaluation of the antimicrobial tiers and subsequent recommendations in order to best balance animal and public health.In conclusion, we reported both the plasma and gastrointestinal pharmacokinetics of two FDA-approved dosing regimens for florfenicol. Although some pharmacokinetic parameters were different between doses, the dosing regimen did not affect the proportion of resistant