Enhanced SARS-CoV-2 case prediction using public health data and machine learning models.

Objectives: The goal of this study is to propose and test a scalable framework for machine learning (ML) algorithms to predict near-term severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cases by incorporating and evaluating the impact of real-time dynamic public health data. Materials and Methods: Data used in this study include patient-level results, procurement, and location information of all SARS-CoV-2 tests reported in West Virginia as part of their mandatory reporting system from January 2021 to March 2022. We propose a method for incorporating and comparing widely available public health metrics inside of a ML framework, specifically a long-short-term memory network, to forecast SARS-CoV-2 cases across various feature sets. Results: Our approach provides better prediction of localized case counts and indicates the impact of the dynamic elements of the pandemic on predictions, such as the influence of the mixture of viral variants in the population and variable testing and vaccination rates during various eras of the pandemic. Discussion: Utilizing real-time public health metrics, including estimated Rt from multiple SARS-CoV-2 variants, vaccination rates, and testing information, provided a significant increase in the accuracy of the model during the Omicron and Delta period, thus providing more precise forecasting of daily case counts at the county level. This work provides insights on the influence of various features on predictive performance in rural and non-rural areas. Conclusion: Our proposed framework incorporates available public health metrics with operational data on the impact of testing, vaccination, and current viral variant mixtures in the population to provide a foundation for combining dynamic public health metrics and ML models to deliver forecasting and insights in healthcare domains. It also shows the importance of developing and deploying ML frameworks in rural settings.

Outcomes of patients with active cancers and pre-existing cardiovascular diseases infected with SARS-CoV-2.

OBJECTIVE: To determine the impact of acute SARS-CoV-2 infection on patient with concomitant active cancer and CVD. METHODS: The researchers extracted and analyzed data from the National COVID Cohort Collaborative (N3C) database between January 1, 2020, and July 22, 2022. They included only patients with acute SARS-CoV-2 infection, defined as a positive test by PCR 21 days before and 5 days after the day of index hospitalization. Active cancers were defined as last cancer drug administered within 30 days of index admission. The "Cardioonc" group consisted of patients with CVD and active cancers. The cohort was divided into four groups: (1) CVD (-), (2) CVD ( +), (3) Cardioonc (-), and (4) Cardioonc ( +), where (-) or ( +) denotes acute SARS-CoV-2 infection status. The primary outcome of the study was major adverse cardiovascular events (MACE), including acute stroke, acute heart failure, myocardial infarction, or all-cause mortality. The researchers analyzed the outcomes by different phases of the pandemic and performed competing-risk analysis for other MACE components and death as a competing event. RESULTS: The study analyzed 418,306 patients, of which 74%, 10%, 15.7%, and 0.3% had CVD (-), CVD ( +), Cardioonc (-), and Cardioonc ( +), respectively. The Cardioonc ( +) group had the highest MACE events in all four phases of the pandemic. Compared to CVD (-), the Cardioonc ( +) group had an odds ratio of 1.66 for MACE. However, during the Omicron era, there was a statistically significant increased risk for MACE in the Cardioonc ( +) group compared to CVD (-). Competing risk analysis showed that all-cause mortality was significantly higher in the Cardioonc ( +) group and limited other MACE events from occurring. When the researchers identified specific cancer types, patients with colon cancer had higher MACE. CONCLUSION: In conclusion, the study found that patients with both CVD and active cancer suffered relatively worse outcomes when they had acute SARS-CoV-2 infection during early and alpha surges in the United States. These findings highlight the need for improved management strategies and further research to better understand the impact of the virus on vulnerable populations during the COVID-19 pandemic.

Outcomes of Patients with Active Cancers and Pre-Existing Cardiovascular Diseases Infected with SARS-CoV-2.

Objective To determine the impact of acute SARS-CoV-2 infection on patient with concomitant active cancer and CVD. Methods The researchers extracted and analyzed data from the National COVID Cohort Collaborative (N3C) database between January 1, 2020, and July 22, 2022. They included only patients with acute SARS-CoV-2 infection, defined as a positive test by PCR 21 days before and 5 days after the day of index hospitalization. Active cancers were defined as last cancer drug administered within 30 days of index admission. The "Cardioonc" group consisted of patients with CVD and active cancers. The cohort was divided into four groups: (1) CVD (-), (2) CVD (+), (3) Cardioonc (-), and (4) Cardioonc (+), where (-) or (+) denotes acute SARS-CoV-2 infection status. The primary outcome of the study was major adverse cardiovascular events (MACE), including acute stroke, acute heart failure, myocardial infarction, or all-cause mortality. The researchers analyzed the outcomes by different phases of the pandemic and performed competing-risk analysis for other MACE components and death as a competing event. Results The study analyzed 418,306 patients, of which 74%, 10%, 15.7%, and 0.3% had CVD (-), CVD (+), Cardioonc (-), and Cardioonc (+), respectively. The Cardioonc (+) group had the highest MACE events in all four phases of the pandemic. Compared to CVD (-), the Cardioonc (+) group had an odds ratio of 1.66 for MACE. However, during the Omicron era, there was a statistically significant increased risk for MACE in the Cardioonc (+) group compared to CVD (-). Competing risk analysis showed that all-cause mortality was significantly higher in the Cardioonc (+) group and limited other MACE events from occurring. When the researchers identified specific cancer types, patients with colon cancer had higher MACE. Conclusion In conclusion, the study found that patients with both CVD and active cancer suffered relatively worse outcomes when they had acute SARS-CoV-2 infection during early and alpha surges in the United States. These findings highlight the need for improved management strategies and further research to better understand the impact of the virus on vulnerable populations during the COVID-19 pandemic.

Assessing the effects of therapeutic combinations on SARS-CoV-2 infected patient outcomes: A big data approach.

BACKGROUND: The COVID-19 pandemic has demonstrated the need for efficient and comprehensive, simultaneous assessment of multiple combined novel therapies for viral infection across the range of illness severity. Randomized Controlled Trials (RCT) are the gold standard by which efficacy of therapeutic agents is demonstrated. However, they rarely are designed to assess treatment combinations across all relevant subgroups. A big data approach to analyzing real-world impacts of therapies may confirm or supplement RCT evidence to further assess effectiveness of therapeutic options for rapidly evolving diseases such as COVID-19. METHODS: Gradient Boosted Decision Tree, Deep and Convolutional Neural Network classifiers were implemented and trained on the National COVID Cohort Collaborative (N3C) data repository to predict the patients' outcome of death or discharge. Models leveraged the patients' characteristics, the severity of COVID-19 at diagnosis, and the calculated proportion of days on different treatment combinations after diagnosis as features to predict the outcome. Then, the most accurate model is utilized by eXplainable Artificial Intelligence (XAI) algorithms to provide insights about the learned treatment combination impacts on the model's final outcome prediction. RESULTS: Gradient Boosted Decision Tree classifiers present the highest prediction accuracy in identifying patient outcomes with area under the receiver operator characteristic curve of 0.90 and accuracy of 0.81 for the outcomes of death or sufficient improvement to be discharged. The resulting model predicts the treatment combinations of anticoagulants and steroids are associated with the highest probability of improvement, followed by combined anticoagulants and targeted antivirals. In contrast, monotherapies of single drugs, including use of anticoagulants without steroid or antivirals are associated with poorer outcomes. CONCLUSIONS: This machine learning model by accurately predicting the mortality provides insights about the treatment combinations associated with clinical improvement in COVID-19 patients. Analysis of the model's components suggests benefit to treatment with combination of steroids, antivirals, and anticoagulant medication. The approach also provides a framework for simultaneously evaluating multiple real-world therapeutic combinations in future research studies.

Persistence and Protective Potential of SARS-CoV-2 Antibody Levels After COVID-19 Vaccination in a West Virginia Nursing Home Cohort.

Importance: West Virginia prioritized SARS-CoV-2 vaccine delivery to nursing home facilities because of increased risk of severe illness in elderly populations. However, the persistence and protective role of antibody levels remain unclear. Objective: To examine the persistence of humoral immunity after COVID-19 vaccination and the association of SARS-CoV-2 antibody levels and subsequent infection among nursing home residents and staff. Design, Setting, and Participants: In this cross-sectional study, blood samples were procured between September 13 and November 30, 2021, from vaccinated residents and staff at participating nursing home facilities in the state of West Virginia for measurement of SARS-CoV-2 antibody (anti-receptor binding domain [RBD] IgG). SARS-CoV-2 infection and vaccination history were documented during specimen collection and through query of the state SARS-CoV-2 surveillance system through January 16, 2022. Exposure: SARS-CoV-2 vaccination (with BNT162b2, messenger RNA-1273, or Ad26.COV2.S). Main Outcomes and Measures: Anti-RBD IgG levels were assessed using multivariate analysis to examine associations between time since vaccination or infection, age, sex, booster doses, and vaccine type. Antibody levels from participants who became infected after specimen collection were compared with those without infection to correlate antibody levels with subsequent infection. Results: Among 2139 SARS-CoV-2 vaccinated residents and staff from participating West Virginia nursing facilities (median [range] age, 67 [18-103] years; 1660 [78%] female; 2045 [96%] White), anti-RBD IgG antibody levels decreased with time after vaccination or infection (mean [SE] estimated coefficient, -0.025 [0.0015]; P < .001). Multivariate regression modeling of participants with (n = 608) and without (n = 1223) a known history of SARS-CoV-2 infection demonstrated significantly higher mean (SE) antibody indexes with a third (booster) vaccination (with infection: 11.250 [1.2260]; P < .001; without infection: 8.056 [0.5333]; P < .001). Antibody levels (calculated by dividing the sample signal by the mean calibrator signal) were significantly lower among participants who later experienced breakthrough infection during the Delta surge (median, 2.3; 95% CI, 1.8-2.9) compared with those without breakthrough infection (median, 5.8; 95% CI, 5.5-6.1) (P = .002); however, no difference in absorbance indexes was observed in participants with breakthrough infections occurring after specimen collection (median, 5.9; 95% CI, 3.7-11.1) compared with those without breakthrough infection during the Omicron surge (median, 5.8; 95% CI, 5.6-6.2) (P = .70). Conclusions and Relevance: In this cross-sectional study, anti-RBD IgG levels decreased after vaccination or infection. Higher antibody responses were found in individuals who received a third (booster) vaccination. Although lower antibody levels were associated with breakthrough infection during the Delta surge, no significant association was found between antibody level and infection observed during the Omicron surge. The findings of this cross-sectional study suggest that among nursing home residents, COVID-19 vaccine boosters are important and updated vaccines effective against emerging SARS-CoV-2 variants are needed.

An ordinal severity scale for COVID-19 retrospective studies using Electronic Health Record data.

Objectives: Although the World Health Organization (WHO) Clinical Progression Scale for COVID-19 is useful in prospective clinical trials, it cannot be effectively used with retrospective Electronic Health Record (EHR) datasets. Modifying the existing WHO Clinical Progression Scale, we developed an ordinal severity scale (OS) and assessed its usefulness in the analyses of COVID-19 patient outcomes using retrospective EHR data. Materials and Methods: An OS was developed to assign COVID-19 disease severity using the Observational Medical Outcomes Partnership common data model within the National COVID Cohort Collaborative (N3C) data enclave. We then evaluated usefulness of the developed OS using heterogenous EHR data from January 2020 to October 2021 submitted to N3C by 63 healthcare organizations across the United States. Principal component analysis (PCA) was employed to characterize changes in disease severity among patients during the 28-day period following COVID-19 diagnosis. Results: The data set used in this analysis consists of 2 880 456 patients. PCA of the day-to-day variation in OS levels over the totality of the 28-day period revealed contrasting patterns of variation in disease severity within the first and second 14 days and illustrated the importance of evaluation over the full 28-day period. Discussion: An OS with well-defined, robust features, based on discrete EHR data elements, is useful for assessments of COVID-19 patient outcomes, providing insights on the progression of COVID-19 disease severity over time. Conclusions: The OS provides a framework that can facilitate better understanding of the course of acute COVID-19, informing clinical decision-making and resource allocation.

Association of Vitamin D Prescribing and Clinical Outcomes in Adults Hospitalized with COVID-19.

It is unclear whether vitamin D benefits inpatients with COVID-19. Objective: To examine the relationship between vitamin D and COVID-19 outcomes. Design: Cohort study. Setting: National COVID Cohort Collaborative (N3C) database. Patients: 158,835 patients with confirmed COVID-19 and a sub-cohort with severe disease (n = 81,381) hospitalized between 1 January 2020 and 31 July 2021. Methods: We identified vitamin D prescribing using codes for vitamin D and its derivatives. We created a sub-cohort defined as having severe disease as those who required mechanical ventilation or extracorporeal membrane oxygenation (ECMO), had hospitalization >5 days, or hospitalization ending in death or hospice. Using logistic regression, we adjusted for age, sex, race, BMI, Charlson Comorbidity Index, and urban/rural residence, time period, and study site. Outcomes of interest were death or transfer to hospice, longer length of stay, and mechanical ventilation/ECMO. Results: Patients treated with vitamin D were older, had more comorbidities, and higher BMI compared with patients who did not receive vitamin D. Vitamin D treatment was associated with an increased odds of death or referral for hospice (adjusted odds ratio (AOR) 1.10: 95% CI 1.05−1.14), hospital stay >5 days (AOR 1.78: 95% CI 1.74−1.83), and increased odds of mechanical ventilation/ECMO (AOR 1.49: 95% CI 1.44−1.55). In the sub-cohort of severe COVID-19, vitamin D decreased the odds of death or hospice (AOR 0.90, 95% CI 0.86−0.94), but increased the odds of hospital stay longer >5 days (AOR 2.03, 95% CI 1.87−2.21) and mechanical ventilation/ECMO (AOR 1.16, 95% CI 1.12−1.21). Limitations: Our findings could reflect more aggressive treatment due to higher severity. Conclusion: Vitamin D treatment was associated with greater odds of extended hospitalization, mechanical ventilation/ECMO, and death or hospice referral.

Assessing the impact of social distancing measures implemented during COVID-19 pandemic on medications for opioid use disorder in West Virginia.

INTRODUCTION: This study evaluates if social distancing measures instituted during the novel coronavirus SARS-CoV-2 (COVID-19) pandemic were associated with a reduction in Medication for Opioid Use Disorder (MOUD) prescribing in West Virginia. The COVID-19 pandemic necessitated the quick implementation of public health interventions such as social distancing. This led to the use of telemedicine in the clinical setting however implementing telemedicine involves system level and infrastructure level changes within a healthcare environment. This could cause a barrier to MOUD delivery as it is often provided concomitantly with other face to face substance use and mental health services. The purpose of this study is to determine whether social distancing was associated with a reduction in MOUD prescribing in West Virginia, with the goal of adding to the knowledge of how COVID-19 and COVID-19-related mitigation strategies have impacted patients with OUD. METHODS: Prescription monitoring data were requested from the West Virginia Board of Pharmacy. We applied interrupted time series modeling to investigate MOUD prescribing practices before and after social distancing took effect. Gabapentin prescriptions were utilized as a control for comparison. RESULTS: Our study assessed state-wide buprenorphine and Suboxone prescriptions as compared to a control medication and found an increase in dosage of both medications and an increase in number of buprenorphine prescriptions, but a small decrease in buprenorphine/naloxone prescription number related to the dates of implementation of social distancing. Taken together, overall this indicates an increase in prescription number of MOUD prescriptions as well as an increase in dosage. CONCLUSION: This study suggests that social distancing measures were associated with an increase in both the number of MOUD prescriptions and the number of doses in each prescription. Significant alterations to MOUD delivery in the clinical setting were implemented in a short timeframe with the COVID-19 pandemic. Understanding the implementation of clinical measures to accommodate social distancing measures may provide benefit to transformation of future delivery of MOUD.

Predicting increases in COVID-19 incidence to identify locations for targeted testing in West Virginia: A machine learning enhanced approach.

During the COVID-19 pandemic, West Virginia developed an aggressive SARS-CoV-2 testing strategy which included utilizing pop-up mobile testing in locations anticipated to have near-term increases in SARS-CoV-2 infections. This study describes and compares two methods for predicting near-term SARS-CoV-2 incidence in West Virginia counties. The first method, Rt Only, is solely based on producing forecasts for each county using the daily instantaneous reproductive numbers, Rt. The second method, ML+Rt, is a machine learning approach that uses a Long Short-Term Memory network to predict the near-term number of cases for each county using epidemiological statistics such as Rt, county population information, and time series trends including information on major holidays, as well as leveraging statewide COVID-19 trends across counties and county population size. Both approaches used daily county-level SARS-CoV-2 incidence data provided by the West Virginia Department Health and Human Resources beginning April 2020. The methods are compared on the accuracy of near-term SARS-CoV-2 increases predictions by county over 17 weeks from January 1, 2021- April 30, 2021. Both methods performed well (correlation between forecasted number of cases and the actual number of cases week over week is 0.872 for the ML+Rt method and 0.867 for the Rt Only method) but differ in performance at various time points. Over the 17-week assessment period, the ML+Rt method outperforms the Rt Only method in identifying larger spikes. Results show that both methods perform adequately in both rural and non-rural predictions. Finally, a detailed discussion on practical issues regarding implementing forecasting models for public health action based on Rt is provided, and the potential for further development of machine learning methods that are enhanced by Rt.

Predicting increases in COVID-19 incidence to identify locations for targeted testing in West Virginia: A machine learning enhanced approach.

During the COVID-19 pandemic, West Virginia developed an aggressive SARS-CoV-2 testing strategy which included utilizing pop-up mobile testing in locations anticipated to have near-term increases in SARS-CXoV-2 infections. In this study, we describe and compare two methods for predicting near-term SARS-CoV-2 incidence in West Virginia counties. The first method, R t Only, is solely based on producing forecasts for each county using the daily instantaneous reproductive numbers, R t. The second method, ML+ R t , is a machine learning approach that uses a Long Short-Term Memory network to predict the near-term number of cases for each county using epidemiological statistics such as Rt, county population information, and time series trends including information on major holidays, as well as leveraging statewide COVID-19 trends across counties and county population size. Both approaches used daily county-level SARS-CoV-2 incidence data provided by the West Virginia Department Health and Human Resources beginning April 2020. The methods are compared on the accuracy of near-term SARS-CoV-2 increases predictions by county over 17 weeks from January 1, 2021-April 30, 2021. Both methods performed well (correlation between forecasted number of cases and the actual number of cases week over week is 0.872 for the ML+R t method and 0.867 for the R t Only method) but differ in performance at various time points. Over the 17-week assessment period, the ML+R t method outperforms the R t Only method in identifying larger spikes. We also find that both methods perform adequately in both rural and non-rural predictions. Finally, we provide a detailed discussion on practical issues regarding implementing forecasting models for public health action based on R t , and the potential for further development of machine learning methods that are enhanced by R t.

5-HT7 receptor activation rescues impaired synaptic plasticity in an autistic-like rat model induced by prenatal VPA exposure.

Autism spectrum disorder (ASD) is a severe life-long neuropsychiatric disorder. Alterations and imbalance of several neurochemical systems may be involved in ASD pathophysiology, of them, serotonergic neurotransmission dysfunction and deficiency may underlie behavioral abnormalities associated with ASD. However, the functional importance of serotonergic receptors, particularly 5HT7 receptors in ASD pathology remains poorly defined. Serotonin receptor subtype 7 (5-HT7R) plays a direct regulatory role in the development and also for the mature function of the brain, therefore, further studies are necessary to elucidate the role of these receptors in the etiology of autism. To address this issue, we combined here behavioral, electrophysiological methods to further characterize the contribution of 5-HT7Rs in the prenatal valproic acid (VPA) exposure-induced impairment in synaptic plasticity and their impact on the associated behavioral changes. This may help to unravel the underlying cellular mechanisms involved in ASD and can lead to new treatment and/or prevention therapies based on the role of the serotonergic system for autism. Findings revealed that compared to control, autistic-like offspring showed increased anxiety-like behavior, reduced social interaction, decreased locomotor activity, and impaired identification of the novel object. However, administration of 5-HT7Rs agonist, LP-211, for 7 consecutive days before testing from postnatal day 21 to 27 reversed all behavioral deficits induced by prenatal exposure to VPA in offspring. Also, both short-term depression and long-term potentiation were impaired in the autistic-like pups, but activation of 5-HT7Rs rescued the LTP impairment in the autistic-like group so that there was no significant difference between the two groups. Blockade of 5-HT7Rs caused LTP impairment following HFS in the autistic-like group. Besides, there was a significant difference in LTD induction following SB-269970 application between the control and the autistic-like groups measured at first 10 min following TPS. Moreover, both the number and the size of retrograde fast blue-labelled neurons in the raphe nuclei were reduced. Overall, these results provide for the first time, as far as we know, functional evidence for the restorative role of 5-HT7Rs activation against prenatal VPA exposure induced behavioral deficits and hippocampal synaptic plasticity impairment. Therefore, these receptors could be a potential and promising pharmacotherapy target for the treatment of autism.

Assessing the impact of a restrictive opioid prescribing law in West Virginia.

BACKGROUND: The Opioid Reduction Act (SB 273) took effect in West Virginia in June 2018. This legislation limited ongoing chronic opioid prescriptions to 30 days' supply, and first-time opioid prescriptions to 7 days' supply for surgeons and 3 days' for emergency rooms and dentists. The purpose of this study was to determine the effect of this legislation on reducing opioid prescriptions in West Virginia, with the goal of informing future similar policy efforts. METHODS: Data were requested from the state Prescription Drug Monitoring Program (PDMP) including overall number of opioid prescriptions, number of first-time opioid prescriptions, average daily morphine milligram equivalents (MME) and prescription duration (expressed as "days' supply") given to adults during the 64 week time periods before and after legislation enactment. Statistical analysis was done utilizing an autoregressive integrated moving average (ARIMA) interrupted time series analysis to assess impact of both legislation announcement and enactment while controlling secular trends and considering autocorrelation trends. Benzodiazepine prescriptions were utilized as a control. RESULTS: Our analysis demonstrates a significant decrease in overall state opioid prescribing as well as a small change in average daily MME associated with the date of the legislation's enactment when considering serial correlation in the time series and accounting for pre-intervention trends. There was no such association found with benzodiazepine prescriptions. CONCLUSION: Results of the current study suggest that SB 273 was associated with an average 22.1% decrease of overall opioid prescriptions and a small change in average daily MME relative to the date of legislative implementation in West Virginia. There was, however, no association of the legislation on first-time opioid prescriptions or days' supply of opioid medication, and all variables were trending downward prior to implementation of SB 273. The control demonstrated no relationship to the law.

Superior hypogastric plexus block as an effective treatment method for endometriosis-related chronic pelvic pain: an open-label pilot clinical trial.

We aimed to investigate the effect of superior hypogastric plexus (SHP) block on pain relief and quality of life of patients with refractory endometriosis. Sixteen patients with refractory endometriosis underwent SHP block. The outcome measures included visual analogue scale (VAS) for chronic pelvic pain, VAS for dysmenorrhoea, and VAS for dyspareunia. The short-form endometriosis health profile (EHP-5) was used to measure quality of life. All the outcome measures were evaluated at weeks 0, 1, 4, 12 and 24. The mean baseline VAS scores significantly improved after the SHP block (p < .001 for all). The mean overall EHP-5 score also significantly improved from 54.3 ± 18.2 to 24.6 ± 13.3 (p < .001). The positive effects of SHP were not diminished over time. No serious adverse effect was noticed in any of the patients. Preliminary results suggest that SHP block could be used as an effective method in pain control and improvement of quality of life in refractory endometriosis.IMPACT STATEMENTWhat is already known on this subject? Safety and efficacy of SHP block in the treatment of CPP has been revealed in earlier investigations. However, the efficacy of SHP block for pain management in patients with refractory endometriosis has not been investigated in earlier investigations.What do the results of this study add? SHP block is an effective method for pain control and improvement of quality of life in patients with refractory endometriosis. The positive effects of this treatment did not diminish over 24-weeks follow-up of the study. No serious adverse effect was noticed in any of the patients.What are the implications of these findings for clinical practice and/or further research? Preliminary results suggest that SHP block could be used safely and effectively for controlling pain and improvement of quality of life in patients with refractory endometriosis.

Effects of ultrasound guided ganglion stellate blockade on intraoperative and postoperative hemodynamic responses in laparoscopic gynecologic surgery.

INTRODUCTION: Laparoscopic gynecologic surgery is one of the most well-known procedures. Pneumoperitoneum with carbon dioxide insufflation can cause unfavorable hemodynamic effects due to catecholamine and vasopressin release. AIM: To examine the effects of stellate ganglion block on hemodynamic response and postoperative pain. MATERIAL AND METHODS: In a prospective double blinded randomized parallel study we included 40 patients with ASA physical status I and II, aged between 18 and 50 years with a gynecologic problem candidate for laparoscopic surgery under general anesthesia. The patients were randomly divided into two groups. Fifteen minutes before anesthesia induction, the patients underwent ultrasound guided stellate ganglion block with 10 ml of lidocaine 1% and the control group underwent stellate ganglion block using 10 ml of distilled water as placebo. After induction of general anesthesia, systolic and diastolic blood pressure and heart rate were recorded, especially after blowing of CO2 gas, the position change, depletion of CO2, and tracheal extubation in recovery. The postoperative pain was calculated using the visual analogue scale (VAS) at three times (0, 30, and 24 h after surgery). RESULTS: Our results showed that mean systolic and diastolic blood pressure and heart rate did not show any significant difference at the measurement times (p > 0.05), and mean VAS of patients in the two groups was significantly different for the three measurement times except 24 h after surgery (p < 0.05). CONCLUSIONS: Stellate ganglion block before laparoscopic gynecologic surgery has no significant effect on intraoperative and postoperative hemodynamic responses; however, it can decrease VAS in the early postoperative period.

Comparison of maximal Rubidium-82 activities for myocardial blood flow quantification between digital and conventional PET systems.

BACKGROUND: PET-based myocardial blood flow (MBF) quantification can be inaccurate when using high tracer activities. Our aim was to derive the maximal Rubidium-82 activity for MBF assessment using a new digital PET system and compare the results with conventional analog systems. METHODS: 1.8 GBq Rubidium-82 was injected into the cardiac insert of an anthropomorphic torso phantom. Data were acquired for 10 min using an Ingenuity TF (Philips Healthcare), Discovery 690 (D690, GE Healthcare), and digital PET prototype system (Philips Healthcare). The dynamic ranges, defined as the maximal measured activity in the reconstructed images deviating < 10% from the true present activity, were determined in all scans. RESULTS: The dynamic ranges were 312 MBq for Ingenuity TF, 650 MBq for D690, and 654 MBq for digital PET prototype. CONCLUSIONS: The maximal Rb-82 activity for MBF assessment using digital PET prototype is higher than that for its analog counterpart (Ingenuity TF), but seems comparable to the D690.

A Case of Colorectal Cancer during Pregnancy: A Brief Review of the Literature.

The incidence of colorectal cancer (CRC) during pregnancy is so rare. Herein we present a case of colorectal cancer that was missed by pregnancy all over the pregnancy period. The patient was a 37-year-old woman (gravid 4, para 2) referred with the complaints of vaginal discharge and suspicious rupture of membrane (ROM). The patient was pale and the initial physical examination revealed dilation of two fingers, effacement about 30%. She underwent emergent cesarean section which showed adhesions surrounding the uterus, the bladder, and the abdominal wall. Forty days postoperatively, the patient presented with abdominal pain in the left upper quadrant (LUQ). Imaging confirmed a mass in LUQ. Partial colectomy of transverse colon (20 cm) was performed. Postoperative histopathologic study revealed a 7 ∗ 6 ∗ 5 cm mass in transverse colon compatible to stage IIa of the Duck class (T3, N0, Mx). Adjuvant chemotherapy was applied and the patient showed improvements during 7 months followup after surgery. Colorectal cancer in pregnancy is associated with diagnostic and therapeutic challenges which mostly lead to late diagnosis in advanced stages and poor prognosis. A targeted program to improve the general population knowledge and the establishment of a national consultant and screening program particularly for women with a planned pregnancy in the high risk group might be beneficial.

Estradiol supplementation during the luteal phase in poor responder patients undergoing in vitro fertilization: a randomized clinical trial.

PURPOSE: This study was designed to evaluate the effects of adding Estradiol (E2) supplementation to progesterone (P) on improvement of pregnancy outcomes in poor responder patients who underwent in vitro fertilization (IVF). METHODS: In a prospective randomized clinical trial, 118 poor responder patients, older than 38 years without contraindications of estradiol consumption from Infertility clinic of a university hospital were randomly divided (by computerized software) into two groups. Control group (59 patients) received only P and intervention group (59 patients) received P and E2 (4 mg/d). Supplementation was done with 4 mg E2 in the luteal phase. Fertilization rate, implantation rate, biochemical and clinical pregnancy rates, abortion rate, ongoing pregnancy, multiple pregnancy and ectopic pregnancy rates were documented for those who completed the study protocol in each group (per protocol analysis) and compared between groups. RESULT: Fifty five patients in control group and 53 patients in intervention group successfully completed the study protocol. Treatment outcomes were not significantly different between two groups. CONCLUSION: For poor responder women who underwent IVF, addition of E2 to P supplementation could not significantly improve pregnancy outcomes.

Combined MRI-PET dissects dynamic changes in plant structures and functions.

Unravelling the factors determining the allocation of carbon to various plant organs is one of the great challenges of modern plant biology. Studying allocation under close to natural conditions requires non-invasive methods, which are now becoming available for measuring plants on a par with those developed for humans. By combining magnetic resonance imaging (MRI) and positron emission tomography (PET), we investigated three contrasting root/shoot systems growing in sand or soil, with respect to their structures, transport routes and the translocation dynamics of recently fixed photoassimilates labelled with the short-lived radioactive carbon isotope (11)C. Storage organs of sugar beet (Beta vulgaris) and radish plants (Raphanus sativus) were assessed using MRI, providing images of the internal structures of the organs with high spatial resolution, and while species-specific transport sectoralities, properties of assimilate allocation and unloading characteristics were measured using PET. Growth and carbon allocation within complex root systems were monitored in maize plants (Zea mays), and the results may be used to identify factors affecting root growth in natural substrates or in competition with roots of other plants. MRI-PET co-registration opens the door for non-invasive analysis of plant structures and transport processes that may change in response to genomic, developmental or environmental challenges. It is our aim to make the methods applicable for quantitative analyses of plant traits in phenotyping as well as in understanding the dynamics of key processes that are essential to plant performance.