Background There is growing evidence to implicate Staphylococcus aureus ( S. aureus) in the pathogenesis of recalcitrant chronic rhinosinusitis (CRS). Our group has demonstrated the ability of S. aureus to internalise within mast cells in nasal polyps and this may mediate disease recalcitrance. We investigated carriage of virulence genes in CRS-related S. aureus strains and its influence on the bacteria’s ability to localise and survive intracellularly. Methods S. aureus strains isolated from non-CRS controls (n=5), CRSsNP (n=4) and CRSwNP patients (n=4) were sequenced using short read paired sequencing and interrogated for carriage of virulence genes. A representative control and CRSwNP isolate were tested for intracellular survival in the LAD2 mast cell line to investigate phenotypic differences. Results Fifty percent of the CRSwNP group had deletion of the hld gene which may promote small colony variant formation or reduced enterotoxin production, and seventy-five percent expressed virulence genes associated with invasive disease. The CRSwNP isolate had a superior ability to localise intracellularly at 6 and 9 hours and showed a higher burden of S. aureus colony forming units at 24 hours. Conclusions When compared to non-CRS controls, CRS-related S. aureus strains demonstrate increased carriage of virulence genes. This appears to facilitate intracellular localisation of the bacteria conferring a survival advantage and enhancing pathogenicity. The latter may be partly due to a reduction in enterotoxin production and the acquisition of serine proteases splA and B and leukocidins E/D genes. This pathogenic S. aureus phenotype may manifest clinically with disease recalcitrance and refractoriness to antibiotics.

Objective: To develop a prediction model for labour dystocia, suitable for risk stratification at onset of labour. Design: A cohort-based registry design was employed using data from the Copenhagen Pregnancy Cohort and the Danish Medical Birth Registry. Setting: The study was conducted at Copenhagen University Hospital – Rigshospitalet, Denmark Population: Nulliparous women with a singleton pregnancy and cephalic presentation in spontaneous labour at term from 2014 to 2020. Methods: Logistic regression analysis was employed to construct the prediction model. Candidate predictors were pre-selected based on clinical reasoning and existing evidence. These were maternal age, pre-pregnancy body mass index, height, gestational age, physical activity, self-reported medical condition, WHO-5 score, and fertility treatment. Main outcome measures: The candidate predictors ability to predict labour dystocia. For model performance, we calculated the area under the receiver operating characteristics curve (AUC) for discriminative capacity and Brier score for model calibration. Results: A total of 12,445 women involving 5,525 events of labour dystocia (44%) were included. All candidate predictors were retained in the final model, which demonstrated moderate discriminative ability with AUC was 62.3% (95% CI:60.7-64.0) and Brier score of 0.24. Conclusions: Our model represents an initial advancement in the prediction of labour dystocia utilizing readily available information obtainable upon admission in active labour. As means of facilitating risk stratification the development of a user-friendly online tool for clinicians is a logical next step. Nevertheless, further model development and external testing across other populations is warranted.

Background: Previous spontaneous preterm birth (sPTB) is an important risk factor for recurrent PTB, yet consensus on the lower limit of PTB is lacking. This complicates the identification of patients at risk for recurrent PTB. Objectives: To assess the risk of recurrent preterm birth following spontaneous extreme PTB between 16 +0 - 27 +6 weeks and the association with the interpregnancy interval. Design: A nationwide retrospective cohort study. Setting: Data from the Perinatal Registry of the Netherlands. Population: Nulliparous women with a singleton pregnancy that ended in sPTB between 16 +0 and 27 +6 weeks of gestation without congenital anomalies or antenatal death between 2010-2014 and had a subsequent pregnancy in the 5 years following. Main outcome measures: Recurrent preterm birth < 37 weeks. Results: 1011 women with linked pregnancies were included. The risk of PTB < 37 weeks with prior spontaneous birth between 16 +0-19 +6, 20 +0-23 +6, and 24 +0-27 +6 weeks was respectively 19.0%, 29.5% and 27.6%. The risk of subsequent PTB < 24 weeks was 5.7%, 7.2% and 4.3%. A short interpregnancy interval of 0-3 months was associated with increased odds for recurrent PTB < 32 weeks (OR 2.3 95% CI 1.4-3.7) and PTB < 37 weeks (OR 1.8 95% CI 1.2-2.6). Conclusion: Patients with previous sPTB from 16 weeks GA onwards are at high risk for recurrent PTB and should be regarded as such in the consideration of preventive measures to prevent recurrent adverse pregnancy outcomes.

During the fabrication of a double gate TFET structure, there is a high chance of gate misalignment which may affect the sensitivity of the sensors. In this paper, we investigated various effects of gate misalignment and switching characteristics of charge plasma-based triple metal double gate vertical TFET on electrical characteristics. We observed effects on analog/RF parameters on the proposed structure such as transconductance (g m), output conductance (g ds), intrinsic gain (A VO), total gate capacitance (C GG), and cut-off frequency (f T), along with threshold voltage (V TH) and sub-threshold slope (SS). The work focuses on the investigation of these parameters in three scenarios of gate misalignment: towards the drain, towards the source, and towards the drain and source with different probable percentages of misalignment. The results are analyzed by considering SiO 2 and HfO 2 as gate oxide materials.

Threshold voltage (VTH) is a crucial parameter for the proper functioning of a transistor. In this study, we propose an alternative method, called the conductance-voltage method, to extract the threshold voltage. This method uses the change in drain current (IDS) with applied gate voltage (VGS) as the device transition from the weak accumulation to the strong accumulation mode of operation to estimate the threshold voltage. This method was then applied to pentacene-based and amorphous indium-gallium-zinc-oxide (a-IGZO)-based thin-film transistors (TFTs) in their linear region of operation using two-dimensional simulations. The results were promising, and this new method provides a valuable tool for optimizing the performance of Complementary Organic/Oxide TFTs for future applications.

Predicting precipitation δ 18O accurately is crucial for understanding water cycles, paleoclimates, and hydrological applications. Yet, forecasting its spatio-temporal distribution remains challenging due to complex climate interactions and extreme events. We developed a method combining spatio-temporal clustering and deep learning neural networks to improve multi-site, multi-year precipitation δ 18O predictions. Using a comprehensive dataset from 33 German sites (1978-2021), our model considers precipitation δ 18O and its controlling factors, including precipitation and temperature distribution. We applied the K-means++ method for classification and divided data into training and prediction sets. The CNN[1](#fn-0002) model extracted spatial features, while the Bi-LSTM model focused on temporal features. Spatio-temporal clustering using K-means++ improved forecast accuracy and reduced errors. This study highlights the potential of deep learning and clustering techniques for forecasting complex spatio-temporal data and offers insights for future research on isotope distributions.

Amphotericin B is a broad-spectrum antifungal agent that is used in the treatment of systemic fungal infections. We describe the case of a 62-year-old female patient with recent aneurysmal subarachnoid hemorrhage who was treated for suspected ventriculitis and a fungal coinfection. Instead of liposomal amphotericin B (L-AmB), 465 mg (5 mg/kg) amphotericin B deoxycholate (DOC) was inadvertently administered, leading to refractory shock with multiple organ failure and requiring mechanical ventilation. Since an overdose of amphotericin B can lead to fatal consequences and has a half-life of 15 days, plasmapheresis was started. The serum concentration decreased from 1.32 µg/mL to 0.62 µg/mL before plasmapheresis, demonstrating a mean half-life of 49 hours. After two plasmapheresis sessions, the serum concentration further dropped to 0.26 µg/mL, demonstrating a mean half-life of 17 hours. In contrast, the third plasmapheresis session had no effect on serum concentration. The patient made a full recovery, potentially facilitated by enhanced amphotericin B elimination through plasmapheresis. Positive outcomes were previously reported in two adult patients treated with plasmapheresis. However, other reports without plasmapheresis described fatal outcomes in adult patients, albeit with a twofold overdose compared to the two patients successfully treated with plasmapheresis. Moreover, plasmapheresis itself carries risks such as hypocalcemia, metabolic alkalosis, and coagulation deficits. Consequently, the role of plasmapheresis in amphotericin B overdose is still debated.

Bleeding complications after pacemaker implantation pose risks, including infection and prolonged hospital stay. A case involving intramural aortic hematoma (IMH) arising from subclavian vein access during implantation and concomitant acute pulmonary embolism (PE) is presented. In the present case, IMH probably resulted from subclavian artery vasa vasorum trauma during vein puncture and guidewire advancement, leading to IMH and hemothorax. PE possibly stemmed from a prothrombotic state caused by intervention and IMH. A conservative management with seriate CT scans was chosen due to hemodynamic stability and high surgical risk, with IMH resolution confirmed on follow-up.

INTRODUCTION. Post-operative atrial fibrillation (POAF) represents the most common arrhythmia reported in the days following surgery. We aimed to investigate the incidence of POAF in the 28 days after surgery and determine its predictors, with a specific focus on inflammation markers. METHODS. We performed a retrospective single center cohort study that included consecutive adult patients who underwent a major surgical procedure between January 2016 to January 2020. Patients were divided into four subgroups according to the type of surgery: (I) orthopedic, (II) non-thoracic non-abdominal, (III) abdominal and esophageal, or (IV) lung and cardiovascular surgery. RESULTS. Among 53,387 included patients (48.8% male, mean age 59 ± 15.9 years), POAF occurred in 570 (1.1%) with a mean latency after surgery of 3.4±2.6 days. Ninety patients died (0.17%) after a mean of 13.7±8.4 days. The 28-day arrhythmia-free survival was lower in patients undergoing lung and cardiovascular surgery ( p<0.001). Patients who developed POAF had higher levels of C-Reactive Protein (CRP) (0.70±0.03 vs. 0.40±0.01 log10 mg/dl; p<0.0001). In the multivariable Cox regression analysis, adjusting for confounding factors (age, gender, length of hospital stay, and group of surgery), CRP was an independent predictor of POAF [HR per 1 mg/dL increase in log-scale = 1.81 (95% CI 1.18–2.79); p=0.007]. Moreover, independent predictors of POAF were also age (HR/1 year increase = 1.06 (95% CI 1.04–1.08); p<0.0001), lung and cardiovascular surgery (HR 23.62; 95% CI 5.65–98.73); p<0.0001), and abdominal and esophageal surgery (HR 6.26; 95% CI 1.48–26.49; p 0.013). CONCLUSIONS. Lung and cardiovascular surgery had the highest risk of POAF in the presented cohort. CRP was an independent predictor of POAF and post-surgery inflammation status may represent a major driver in the pathophysiology.

A document by Ramin Mehrabifard. Click on the document to view its contents.

Background: Estrogen is the strongest risk factor for breast cancer, especially hormone receptor positive subtypes. Anti- Müllerian hormone (AMH) can stimulate apoptosis, reduces breast tumor growth, and also has several actions in the steroid biosynthetic pathway. Therefore, there may be a correlation between AMH and breast cancer. Aims: In this study, we evaluate the correlation between AMH levels and the estrogen receptor (ER) or progesterone receptor (PR) status of breast cancer. Methods: This retrospective study was performed at two University Hospitals between August 2018 and April 2019. Serum AMH level, ER and PR status of the tumors were extracted from medical records. Results: Totally 100 premenopausal women with biopsy proven breast cancer were included in our study. AMH level was slightly higher in patients with ER positive than those with ER negative tumors, however, the difference was not significant (3.33 ± 3.15 vs 2.71± 2.06, p-value =0.69). In categorized AMH level, however, we could not find any significant differences between ER positive and negative as well as PR positive and negative patients. Conclusion: This study shows that there might be an association between serum AMH and breast cancer subtype regarding hormone receptors. We propose larger studies to be performed in order to verify this important issue further.

Dynamic flux balance analysis (FBA) allows estimation of intracellular reaction rates using organism-specific genome scale metabolic models (GSMM) and by assuming instantaneous pseudo steady states for processes that are inherently dynamic. This technique is well-suited for industrial bioprocesses employing complex media characterized by a hierarchy of substrate uptake and product secretion. However, knowledge of exchange rates of many components of the media would be required to obtain meaningful results. Here, we performed spent media analysis using mass spectrometry (MS) coupled with liquid (LCMS) and gas chromatography (GCMS) for a fed-batch, high cell density cultivation of E. coli BL21(DE3) expressing a recombinant protein. Time course measurements thus obtained for 246 metabolites were converted to instantaneous exchange rates. These were then used as constraints for dynamic FBA using a previously reported GSMM, thus providing insights into how the flux map evolves through the process. Changes in TCA cycle fluxes correlated with the increased demand for energy during recombinant protein production. The results show how amino acids act as hubs for the synthesis of other cellular metabolites. Our results provide a deeper understanding of an industrial bioprocess and will have implications in further optimizing the process.

In this paper, Long Short-Term Memory (LSTM) networks and multilayered feedforward neural networks (FFNNs) were combined with first principles equations in a hybrid workflow to describe human embryonic kidney 293 (HEK293) culture dynamics. Experimental data of 27 extracellular state variables in 20 fed-batch HEK293 cultures were collected in a parallel high throughput 250 mL cultivation system. The adaptive moment estimation method (ADAM) with stochastic regularization and cross-validation were employed for deep learning. A total of 784 hybrid models with varying deep neural network (DNN) architectures, depths, layers sizes and node activation functions were compared. In most scenarios, hybrid LSTM models outperformed hybrid FFNN models in terms of training and testing error. Hybrid LSTM models revealed to be less sensitive to data resampling than FFNN hybrid models. As disadvantages, Hybrid LSTM models are in general more complex (higher number of parameters) and have a higher computation cost than FFNN hybrid models. The hybrid model with the highest prediction accuracy consisted in a LSTM network with 7 internal states connected in series with dynamic material balance equations. This hybrid model correctly predicted the dynamics of the 27 state variables (R 2=0.93 in the test data set), including biomass, key substrates, amino acids and metabolic by-products for around 10 cultivation days.

Grease in the normal operation of the RV reducer has a role that can not be ignored, for the variable working conditions of the RV reducer, the performance of the lubricant changes directly affect its reliable operation. Therefore, the study of the rheological properties of the grease has become the focus of the study of RV reducer performance.In this paper, SK-1A grease is taken as the research object, and its rheological characteristics under wide temperature range working conditions (-20℃~40℃) are investigated through rheological experiments, to analyze the potential influence of SK-1A grease on the performance of RV reducer.In addition, to better study the rheological properties of grease under different working conditions, the Elman neural network model was used to predict the rheological properties of grease based on the rheological experiments of grease, and the results were compared with those of the BP neural network and the RBF neural network.The prediction accuracy of the Elman neural network model was assessed using Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) in a cross-validation approach.The results show that the viscoelastic properties of SK-1A grease generally show a decreasing trend as the temperature rises in a wide temperature range, and the degree of entanglement of soap fibers decreases more obviously, but its fluidity is more stable.The results of the three neural network prediction models show that the Elman neural network model used for the prediction of grease rheological properties shows high prediction accuracy, and the model can provide a valuable theoretical reference for the accurate prediction of the rheological properties of grease affected by complex multifactor.

The use of intracytoplasmic sperm injection (ICSI) for the production of equine embryos has significantly increased over the past decade, leading to the creation of labs specific for ICSI and related in vitro fertilization techniques. However, the commitment, resources and skills necessary for developing a viable IVF lab can be underestimated or under-appreciated. The purpose of this paper is to share the difficulties our clinic encountered when developing an IVF lab.

Several countries reported mpox cases since the beginning of the 2022 worldwide outbreak. Although MPXV infection is expected to provide long-term protection against reinfection and the global epidemic is waning, new infections are still detected worldwide and cases of mpox reinfection have been reported since January 2023, putting the paradigm of protective immunity in doubt. As little is known about the viral and host mechanisms of mpox reinfection, it is critical to understand the determinants behind long-term post-infectious immune protection. This review summarizes cases of possible mpox reinfections reported up to July 2023 and highlights key concepts and knowledge about mpox to guide future research in the field.

This paper considers the initial-boundary value problem of the Caputo time-fractional three-dimensional primitive equations for oceanic and atmospheric. Referring to Cao-Titi (Ann. of Math. 245-267, 2007), we consider the results of time-fractional case and obtain the global well-posedness of time-fractional primitive equations. It is proved that the well-posedness of the time fractional atmospheric ocean equation is still valid.

This study investigates fluid flow over a rotating disk with a focus on the impact of radiative heat flow of MHD elastico-viscous nanofluid. The analysis accounts for viscous dissipation, variable thermal conductivity, heat source, and chemical reaction. The governing nonlinear coupled partial differential equations were formulated and transformed into a system of nonlinear ordinary differential equations using appropriate similarity variables. The resulting nondimensional models were subjected to the spectral local linearization method (SLLM) for analysis. The numerical solution was validated against existing literature, demonstrating good agreement. The effects of relevant parameters were presented through graphs and tables. It was observed that the elastico-viscous fluid and magnetic parameters hinder fluid profiles, while the opposite effect is observed in temperature and concentration profiles, respectively. Moreover, the thermal boundary layer thickness increases with higher Eckert number, radiation parameter, thermophoresis parameter, Brownian motion parameter, and heat source parameter values.

Climate change impacts are driving forest fires worldwide and reducing snowfall in temperate countries. Whether these impacts result in a significant alteration of winter soil respiration (Rs) rates and temperature in the postfire and the undisturbed black pine (Pinus nigra) forests remain poorly understood. A field experiment was conducted in the postfire and the undisturbed black pine forests during a winter period in Türkiye to quantify Rs rates as affected by lack of snow and snow cover. Four treatments were applied: snow-exclusion postfire (SEPF), snow postfire (SPF), snow-exclusion undisturbed forest (SEUF), and snow-undisturbed forest (SUF). The SEPF exhibited the significantly lowest mean Rs rates (0.71 µmol m-2 s-1) compared to the SPF (1.02 µmol m-2 s-1), SEUF (1.44 µmol m-2 s-1, and SUF (1.48 µmol m-2 s-1). The Rs also showed significant variations with time (p <.0001). However, treatments and time exhibited no statistically significant interaction effects (p = 0.6801). Total amounts of winter Rs (January to March) ranged from 4.92 to 5.07 Mt CO2 ha-1 in the undisturbed forest and 2.53 to 3.51 Mt CO2 ha-2 in the postfire site. The Rs showed a significantly positive relationship (p <.0001) with the soil (0.59) and air (0.46) temperatures and a significantly negative relationship (p = 0.0017) with the soil moisture (-0.20) at the 5 cm depth. In contrast, the Rs showed a negative, but not statistically significant relationship (p = 0.0932) with the soil moisture (-0.16) at the 10 cm soil depth. The combined effects of lack of snow and forest fire resulted in a significant decrease of Rs. In contrast, a warmer winter significantly increased Rs rates in the undisturbed forest, suggesting that a warmer winter could potentially accelerate soil organic carbon losses in naturally growing undisturbed forest ecosystems, thus, providing positive feed backs to climate change.

Objective: To build a computer vision model that can automatically detect sleeping position in the third trimester under real-world conditions. Design: This study used data from an ongoing observational study and a previous cross-sectional study. Setting: Participants’ homes. Sample: Pregnant participants in the third trimester and their bed partners. Methods: Real-world overnight video recordings were collected from an ongoing, Canada-wide, prospective, four-night, home sleep apnea study and controlled-setting video recordings were used from a previous study. Images were extracted from the videos and body positions were annotated. Five-fold cross validation was used to train, validate, and test a model using state-of-the-art deep convolutional neural networks. Main Outcome Measures: Precision and recall of the model for detecting thirteen pre-defined body positions. Results: The dataset contained 39 pregnant participants, 13 bed partners, 12,930 images, and 47,001 annotations. The model was trained to detect pillows, twelve sleeping positions, and a sitting position in both the pregnant person and their bed partner simultaneously. The model significantly outperformed a previous similar model for the three most commonly occurring natural sleeping positions in pregnant and non-pregnant adults, with an 82-to-89% average probability of correctly detecting them and a 15-to-19% chance of failing to detect them when any one of them is present. Conclusions: The model holds potential to solve yet unanswered research and clinical questions regarding the relationship between sleeping position and pregnancy outcomes.

Biological invasions represent an extraordinary opportunity to study evolution. This is because accidental or deliberate species introductions have taken place for centuries across large geographical scales, in natural and anthropogenic environments. Until recently however, the utility of invasions as evolutionary experiments has been hampered by the limited information on the makeup of populations that were part of earlier invasion stages. Now, developments in ancient and historical DNA technologies, as well as the quickening pace of digitization for millions of specimens that are housed in herbaria and museums globally promise to help overcome this obstacle. In this review, we first introduce the types of temporal data that can be used to study invasions, highlighting the timescale captured by each approach, and their respective limitations. We then discuss how ancient and historical specimens as well as data available from prior invasion studies can be used to answer questions on mechanisms of (mal)adaptation, rates of evolution, or community-level changes during invasions. By bridging the gap between contemporary and historical invasive populations, temporal data can help us connect pattern to process in invasion science. These data will become increasingly important if invasions are to achieve their full potential as experiments of evolution in nature.

A document by Reabal Najjar. Click on the document to view its contents.

Photothermal devices and thermoelectric cells hold great promise for energy generation but integration of the two remains a considerable challenge in real-life power supply for sensors. Here, a novel photo-thermo-electric hydrogel (PTEH-Interlocking) was constructed by synthesis of a photothermal layer on a thermoelectric hydrogel with the redox pair Fe(CN)63−/Fe(CN)64−. The smart design of using oxidation of pyrogallic acid by Fe(CN)63− to construct the photothermal layer for photo-to-heat conversion protected the redox couple of the thermogalvanic ion pair from ultraviolet damage, as well as triggered the formation of an interlocking structure at the interface of the photothermal layer and the thermoelectric hydrogel. The as-prepared PTEH-Interlocking have shown a high Seebeck coefficient and rapid heat transfer, boosting the photo-thermo-electric conversion. As a demonstration of a practical application, the PTEH-Interlocking cells is successfully used as the energy supply for a mechanical sensor.

Pulses, or irregular resource increases, and disturbances, or removal and reorganization of matter, have usually been used to explain different ecosystem level attributes, although with differing abilities to generate accurate predictions. There is clear overlap between the phe-nomena that the two concepts describe. Jentsch & White (2019) introduce the concept of the “pulse-disturbance”. Both resource pulses and disturbances can be characterized as events that transform, augment, or reduce resources, nutrients, or biomass properties, along multiple dimensions. These dimensions can be measured with a range of variables repre-senting ecological processes or biodiversity outcomes. Any given multi-dimensional pulse-disturbance event may act as a disturbance (decreases in some variable values) and simulta-neously as a pulse (increases in other variable values). Although the pulse-disturbance con-cept is insightful, alone it does not generate falsifiable predictions about outcomes. Draw-ing on the pulse-disturbance concept I present a framework for predicting the impacts of pulse-disturbance combinations by relating them to the concept of the microhabitat. Micro-habitats explain fine-scale spatial patterning, biodiversity, and are better than macroecologi-cal variables at explaining niche coexistence. The establishment microhabitat is particularly good at predicting adult plant distributions. I note that there is a similarity between the mul-ti-dimensional concept of the pulse-disturbance and the multi-dimensional description of microhabitats. I propose that pulse-disturbances can create, and correspond to, microhabi-tats, or overlap completely or partially with microhabitat requirements. Thus, a predictable aspect of pulse-disturbances is the correspondence between the microhabitats they produce, and the establishment microhabitat requirements of species in the available pool. I propose to focus on the prediction of indicator species, given that data on species’ establishment microhabitat requirements are not always available in databases. To illustrate the approach, I present two case studies of predicting plant community responses to novel or reintroduced pulse-disturbances from central Chile.