Post-translational modifications of histones by protein methyltransferases (PMTs) and histone demethylases (KDMs) play an important role in the regulation of gene expression and transcription and are ...implicated in cancer and many other diseases. Many of these enzymes also target various nonhistone proteins impacting numerous crucial biological pathways. Given their key biological functions and implications in human diseases, there has been a growing interest in assessing these enzymes as potential therapeutic targets. Consequently, discovering and developing inhibitors of these enzymes has become a very active and fast-growing research area over the past decade. In this review, we cover the discovery, characterization, and biological application of inhibitors of PMTs and KDMs with emphasis on key advancements in the field. We also discuss challenges, opportunities, and future directions in this emerging, exciting research field.
A possible way to store both renewable energy and CO2 in chemical energy is to produce value-added chemicals and fuels starting from CO2 and green electricity. This can be done by exploiting the ...non-equilibrium properties of gaseous electrical discharges. Discharges, in addition, can be switched on and off quickly, thus being suitable to be coupled with an intermittent energy source. In this study, we have used a nanosecond pulsed discharge to dissociate CO2 and CH4 in a 1:1 mixture at atmospheric pressure, and compared our results with literature data obtained by other discharges. The main products are CO, H2, C2H2, water and solid carbon. We estimate an energy efficiency of 40% for syngas (CO and H2) production, higher if other products are also considered. Such values are among the highest compared to other discharges, and, although not very high on an absolute scale, are likely improvable via possible routes discussed in the paper and by coupling to the discharge a heterogeneous catalysis stage.
Abstract
BACKGROUND
As spine surgery becomes increasingly common in the elderly, frailty has been used to risk stratify these patients. The Hospital Frailty Risk Score (HFRS) is a novel method of ...assessing frailty using International Classification of Diseases, Tenth Revision (ICD-10) codes. However, HFRS utility has not been evaluated in spinal surgery.
OBJECTIVE
To assess the accuracy of HFRS in predicting adverse outcomes of surgical spine patients.
METHODS
Patients undergoing elective spine surgery at a single institution from 2008 to 2016 were reviewed, and those undergoing surgery for tumors, traumas, and infections were excluded. The HFRS was calculated for each patient, and rates of adverse events were calculated for low, medium, and high frailty cohorts. Predictive ability of the HFRS in a model containing other relevant variables for various outcomes was also calculated.
RESULTS
Intensive care unit (ICU) stays were more prevalent in high HFRS patients (66%) than medium (31%) or low (7%) HFRS patients. Similar results were found for nonhome discharges and 30-d readmission rates. Logistic regressions showed HFRS improved the accuracy of predicting ICU stays (area under the curve AUC = 0.87), nonhome discharges (AUC = 0.84), and total complications (AUC = 0.84). HFRS was less effective at improving predictions of 30-d readmission rates (AUC = 0.65) and emergency department visits (AUC = 0.60).
CONCLUSION
HFRS is a better predictor of length of stay (LOS), ICU stays, and nonhome discharges than readmission and may improve on modified frailty index in predicting LOS. Since ICU stays and nonhome discharges are the main drivers of cost variability in spine surgery, HFRS may be a valuable tool for cost prediction in this specialty.
We sought to predict surgical volumes for 2 common cervical spine procedures from 2020 to 2040.
Using the National Inpatient Sample from 2003–2016, nationwide estimates of anterior cervical ...diskectomy and fusion (ACDF) and posterior cervical decompression and fusion (PCDF) volumes were calculated using International Classification of Diseases, Ninth and Tenth Revision (ICD-9, ICD-10) procedure codes. With data from the U.S. Census Bureau, estimates of the U.S. population were used to create Poisson models controlling for age and sex. Age was categorized into ranges (<25 years old, 25–34, 35–44, 45–54, 55–64, 65–74, 75–84, and >85), and estimates of surgical volume for each age group were created.
From 2020–2040, increases in surgical volume from 13.3% (153,288–173,699) and 19.3% (29,620–35,335) are expected for ACDF and PCDF, respectively. For ACDF, the largest increases are expected in the 45–54 (42,077–49,827) and 75–84 (8065–14,862) age groups, whereas for PCDF, the largest increases will be seen in the 75–84 (3710–6836) age group. In accordance with an aging population, modest increases will be seen for ACDF (858–1847) and PCDF (730–1573) in the >85-year-old cohort.
As expected, large growth in cervical spine surgical volumes is likely to be seen, which could indicate a need for increased numbers of spinal neurosurgeons and orthopedic surgeons. Further studies are needed to investigate the needs of the field in light of these expected increases in volume.
Abstract
Over the years, until the present days, a persistent mistake has been found in the literature: the use of the ‘vibrational temperature’ of an emitting electronic state as somewhat ...representative of the vibrational temperature of the gas in the discharge. Such a temperature is determined by fitting the spectra measured by optical emission spectroscopy. Besides the misuse of the word
temperature
, the results of such fittings are ambiguously named ‘vibrational temperature’ and sometimes used to argue about the vibrational non-equilibrium and its variation with discharge conditions. What has this
temperature
to do with the vibrational excitation of the molecules’ ground state, i.e. of the large majority of gas components? It is well established that the connection between the vibrational population of the excited and the ground state exists through the excitation process, the collisional quenching, and the vibrational relaxation in the manifold of the excited state. Nevertheless, this is very often ignored in the literature. In this note, we discuss this subject with the example of the ‘vibrational temperature’ of the N
2
(C,
v
) manifold, showing how much all the mentioned parameters can drive to incorrect deductions from an anyway conceptually wrong measurement.
In situ quantum cascade laser (QCL) absorption spectroscopy is used to investigate the effect of admixed water in a pulsed CO2 glow discharge on the vibrational excitation of CO2 and CO and the ...conversion of CO2. Time-resolved transmittance spectra of the non-equilibrium CO2 plasma are measured with a 100 μs time resolution. A custom fitting routine is used to extract the time evolution of the gas temperature, rotational temperature and vibrational temperatures of CO2 and CO, while the CO2 conversion is determined from measured CO2 and CO number densities. Rotational Raman scattering is additionally performed in the centre of the reactor to verify measured rotational and vibrational temperatures from line-of-sight absorption spectroscopy. The plasma is operated at 6.7 mbar, with up to 10% water admixed, and is pulsed with a 5-10 ms on-off cycle, with a current of 50 mA supplied during the plasma on-time. Vibrational temperatures and CO2 conversion are not significantly affected by water admixtures below 0.5%. However, the asymmetric stretch temperature of CO2 (T3) shows considerable quenching upon admixing 10% water vapour, with the maximum elevation above the rotational temperature (Trot) decreasing from 580 ± 86 K to 230 ± 63 K. For the vibrational temperature of CO (TCO), a similar trend is measured. However, the slopes of T3 and TCO within the first few hundred μs after the start of the plasma remain unchanged, even when admixing 10% water vapour, suggesting equal excitation of the vibrational modes through e-V and V-V interactions. The conversion decreases by almost a factor of 4 when admixing 10% water. We argue that vibrational quenching of CO2 by water can explain part of the decrease. Changes in electron density and temperature and reactions between CO and OH can also play a role.
Summary The Brazilian Osteoporosis Study (BRAZOS) is the first epidemiological study carried out in a representative sample of Brazilian men and women aged 40 years or older. The prevalence of ...fragility fractures is about 15.1% in the women and 12.8% in the men. Moreover, advanced age, sedentarism, family history of hip fracture, current smoking, recurrent falls, diabetes mellitus and poor quality of life are the main clinical risk factors associated with fragility fractures. Introduction The Brazilian Osteoporosis Study (BRAZOS) is the first epidemiological study carried out in a representative sample of Brazilian men and women aged 40 years or older with the purpose of identifying the prevalence and the main clinical risk factors (CRF) associated with osteoporotic fracture in our population. Methods A total of 2,420 individuals (women, 70%) from 150 different cities in the five geographic regions in Brazil, and all different socio-economical classes were selected to participate in the present survey. Anthropometrical data as well as life habits, fracture history, food intake, physical activity, falls and quality of life were determined by individual quantitative interviews. The representative sampling was based on Brazilian National data provided by the 2000 and 2003 census. Low trauma fracture was defined as that resulting of a fall from standing height or less in individuals 50 years or older at specific skeletal sites: forearm, femur, ribs, vertebra and humerus. Sampling error was 2.2% with 95% confidence intervals. Logistic regression analysis models were designed having the fragility fracture as the dependent variable and all other parameters as the independent variable. Significance level was set as p < 0.05. Results The average of age, height and weight for men and women were 58.4 ± 12.8 and 60.1 ± 13.7 years, 1.67 ± 0.08 and 1.56 ± 0.07 m and 73.3 ± 14.7 and 64.7 ± 13.7 kg, respectively. About 15.1% of the women and 12.8% of the men reported fragility fractures. In the women, the main CRF associated with fractures were advanced age (OR = 1.6; 95% CI 1.06-2.4), family history of hip fracture (OR = 1.7; 95% CI 1.1-2.8), early menopause (OR = 1.7; 95% CI 1.02-2.9), sedentary lifestyle (OR = 1.6; 95% CI 1.02-2.7), poor quality of life (OR = 1.9; 95% CI 1.2-2.9), higher intake of phosphorus (OR = 1.9; 95% CI 1.2-2.9), diabetes mellitus (OR = 2.8; 95% CI 1.01-8.2), use of benzodiazepine drugs (OR = 2.0; 95% CI 1.1-3.6) and recurrent falls (OR = 2.4; 95% CI 1.2-5.0). In the men, the main CRF were poor quality of life (OR = 3.2; 95% CI 1.7-6.1), current smoking (OR = 3.5; 95% CI 1.28-9.77), diabetes mellitus (OR = 4.2; 95% CI 1.27-13.7) and sedentary lifestyle (OR = 6.3; 95% CI 1.1-36.1). Conclusion Our findings suggest that CRF may contribute as an important tool to identify men and women with higher risk of osteoporotic fractures and that interventions aiming at specific risk factors (quit smoking, regular physical activity, prevention of falls) may help to manage patients to reduce their risk of fracture.
Background A subset of good-grade patients with aneurysmal subarachnoid hemorrhage (aSAH) develop delayed cerebral ischemia (DCI) that may cause permanent disabilities after aSAH. However, little is ...known about the risk factors of DCI among this specific patient group. Methods and Results We obtained a multinational cohort of good-grade (Glasgow Coma Scale 13-15 on admission) patients with aSAH by pooling patient data from 4 clinical trials and 2 prospective cohort studies. We collected baseline data on lifestyle-related factors and the clinical characteristics of aSAHs. By calculating fully adjusted risk estimates for DCI and DCI-related poor outcome, we identified the most high-risk patient groups. The pooled study cohort included 1918 good-grade patients with aSAH (median age, 51 years; 64% women), of whom 21% and 7% experienced DCI and DCI-related poor outcome, respectively. Among men, patients with obesity and (body mass index ≥30 kg/m
) thick aSAH experienced most commonly DCI (33%) and DCI-related poor outcome (20%), whereas none of the normotensive or young (aged <50 years) men with low body mass index (body mass index <22.5 kg/m
) had DCI-related poor outcome. In women, the highest prevalence of DCI (28%) and DCI-related poor outcome (13%) was found in patients with preadmission hypertension and thick aSAH. Conversely, the lowest rates (11% and 2%, respectively) were observed in normotensive women with a thin aSAH. Conclusions Increasing age, thick aSAH, obesity, and preadmission hypertension are risk factors for DCI in good-grade patients with aSAH. These findings may help clinicians to consider which good-grade patients with aSAH should be monitored carefully in the intensive care unit.
The fundamental challenge in machine learning is ensuring that trained models generalize well to unseen data. We developed a general technique for ameliorating the effect of dataset shift using ...generative adversarial networks (GANs) on a dataset of 149,298 handwritten digits and dataset of 868,549 chest radiographs obtained from four academic medical centers. Efficacy was assessed by comparing area under the curve (AUC) pre- and post-adaptation. On the digit recognition task, the baseline CNN achieved an average internal test AUC of 99.87% (95% CI, 99.87-99.87%), which decreased to an average external test AUC of 91.85% (95% CI, 91.82-91.88%), with an average salvage of 35% from baseline upon adaptation. On the lung pathology classification task, the baseline CNN achieved an average internal test AUC of 78.07% (95% CI, 77.97-78.17%) and an average external test AUC of 71.43% (95% CI, 71.32-71.60%), with a salvage of 25% from baseline upon adaptation. Adversarial domain adaptation leads to improved model performance on radiographic data derived from multiple out-of-sample healthcare populations. This work can be applied to other medical imaging domains to help shape the deployment toolkit of machine learning in medicine.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
PDF
