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Rapamycin, an established mTOR inhibitor in clinical practice, is widely recognized for its therapeutic efficacy. Ridaforolimus, a non-prodrug rapalog, offers improved aqueous solubility, stability, and affinity compared to rapamycin. In recent years, there has been a surge in clinical trials involving ridaforolimus. We searched PubMed for ridaforolimus over the past decade and selected clinical trials of ridaforolimus to make a summary of the research progress of ridaforolimus in clinical trials. The majority of these trials explored the application of ridaforolimus in treating various tumors, including endometrial cancer, ovarian cancer, prostate cancer, breast cancer, renal cell carcinoma, and other solid tumors. These trials employed diverse drug combinations, incorporating agents such as ponatinib, bicalutamide, dalotuzumab, MK-2206, MK-0752, and taxanes. The outcomes of these trials unveiled the diverse potential applications of ridaforolimus in disease treatment. Our review encompassed analyses of signaling pathways, ridaforolimus as a single therapeutic agent, its compatibility in combination with other drugs, and an assessment of adverse events (AEs). We conclude by recommending further research to advance our understanding of ridaforolimus’s clinical applications.

Abstract Background and Aims To construct a bleeding events prediction model of nonvalvular atrial fibrillation (NVAF) patients receiving rivaroxaban. Methods We conducted a retrospective cohort study in patients with NVAF who received rivaroxaban from June 2017 to March 2019. Demographic information and clinical characteristics were obtained from the electronic medical system. Univariate analysis was used to find the primary predictive factors of bleeding events in patients receiving rivaroxaban. Multiple analysis was conducted to screen the primary independent predictive factors selected from the univariate analysis. Finally, the independent influencing factors were applied to build a prediction model by using R software; then, a nomogram was established according to the selected variables visually, and the sensitivity and specificity of the model was evaluated. Results Twelve primary predictive factors were selected by univariate analysis from 46 variables, and multivariate analysis showed that older age, higher prothrombin time (PT) values, history of heart failure and stroke were independent risk factors of bleeding events. The area under curve (AUC) for this novel nomogram model was 0.828 (95% CI: 0.763–0.894). The mean AUC over 10‐fold stratified cross‐validation was 0.787, and subgroup analysis validation also showed a satisfied AUC. In addition, the decision curve analysis showed that the PT in combination with CHA2DS2‐VASc and HASBLED was more practical and accurate for predicting bleeding events than using CHA2DS2‐VASc and HASBLED alone. Conclusions PT in combination with CHA2DS2‐VASc and HASBLED could be considered as a more practical and accurate method for predicting bleeding events in patients taking rivaroxaban.

Beekeeping directly depends on the floral biodiversity available to honey bees. In tropical regions, where nectar and pollen resources are numerous, the botanical origin of some honey is still under discussion. A precise knowledge of plants foraged by honey bees is useful to understand and certify the botanical origin of honey. In this study, attention was paid to honey samples from the French Guiana Atlantic coast where beekeepers generally place their hives in four types of biotopes: seaside vegetation, mangrove, savannah, and secondary forest. Pollen analysis of 87 honey samples enabled the identification of major plants visited by Africanized honey bees during the dry season (approximately from July to January). Through melissopalynologic analysis, 51 pollen types were identified and classified according to their relative presence. Frequently observed pollens (with relative presence > 50%) in French Guiana kinds of honey were those from Mimosa pudica, Cocos sp., Rhyncospora sp., Avicennia germinans, Paspalum sp., Spermacoce verticillata, Tapirira guianensis, Cecropia sp., Myrtaceae sp., Mauritia flexuosa sp., Solanum sp., and Protium sp. In many honeys, only M. pudica was over-represented (relative frequency > 90%). Color and electrical conductivity in French Guiana honeys exhibit significant variations, with color ranging from 27 mm to 110 mm Pfund, and electrical conductivity ranging from 0.35 to 1.22 mS/cm.

Various honeys from French Guiana were collected and analyzed to investigate their volatile fraction composition and antioxidant activity. Volatile composition was assessed using HS-SPME/GC, GC-MS technique. Oxygenated monoterpenes like hotrienol (0.5–45.3%) were found as major molecules, followed by non terpenic compounds like phenylacetaldehyde (0.8–18.2%) or 3-hydroxy-4-phenyl-2-butanone (0.1–29.3%). Three chemical groups using statistical analysis were classified within investigated honey samples: norisoprenoids/shikimates, mevalonate and their combination. Total phenolics content (TPC) was determined by Folin-Ciocalteu method. Antioxidant activity was assessed by oxygen radical absorbance capacity (ORAC) and 2,2′-azino-bis-3-ethylbenzothiazoline-6-sulfonic acid (ABTS) assays. TPC and anti-radical activity were compared with multifloral honeys from neighboring regions, indicating the possible presence of compounds from the polyphenol family. These results are promising for further biological studies involving honeys from French Guiana.

In most popular public accessible cryptocurrency systems, the mining pool plays a key role because mining cryptocurrency with the mining pool turns the non-profitable situation into profitable for individual miners. In many recent novel blockchain consensuses, the deep learning training procedure becomes the task for miners to prove their workload. Thus, the computation power of miners will not purely be spent on the hash puzzle. In this way, the hardware and energy will support the blockchain service and deep learning training simultaneously. While the incentive of miners is to earn tokens, individual miners are motivated to join mining pools to become more competitive. In this paper, we are the first to demonstrate a mining pool solution for novel consensuses based on deep learning.The mining pool manager partitions the full searching space into subspaces, and all miners are scheduled to collaborate on the Neural architecture search (NAS) tasks in the assigned subspace. Experiments demonstrate that the performance of this type of mining pool is more competitive than that of an individual miner. Due to the uncertainty of miners' behaviors, the mining pool manager checks the standard deviation of the performance of high reward miners and prepares backup miners to ensure completion of the tasks of high reward miners.

ObjectiveThe objective of the present study was to describe and analyze the clinical characteristics of nocardiosis.Materials and methodsWe described and analyzed the clinical characteristics of nocardiosis cases from two centers over the past 5 years from the following aspects: age and sex, Nocardia species, sites of Nocardia infection, test specimens, detection methods, concurrent pathogens, symptoms, imaging features, co-conditions, drug susceptibility tests, antibiotic therapy/duration, outcomes, and follow-up.ResultsThe median age of the 19 cases was 64 years, with an interquartile range (IQR) of 56–68 years. Eight cases (42.1%) were immunocompromised [those who had been on corticosteroid use (62.5%), those who had used immunosuppressants (50.0%), or those who had suffered from chronic nephrosis (37.5%) or diabetes mellitus (DM) (25.0%)]. The plethora of comorbidities of these cases included diabetes (10.5%), chronic kidney disease (CDK) (15.8%), chronic lung disease (36.8%), and rheumatic diseases (10.5%). Cough and expectoration (73.7%) was the most common symptom of nocardiosis. The respiratory tract (89.5%) was the most common site of the clinical disease. Nearly half (9 cases, 47.3%) of these patients had concurrent infections. The most common Nocardia isolation site was the respiratory tract (73.7%). All patients were given antibiotic therapies, out of whom as many as 63.6% of patients were treated with two concurrent antimicrobial agents, 15.8% of patients were treated under monotherapy and 21.1% of patients were treated with three or more concurrent antimicrobial agents.ConclusionsAn uncommon life-threatening infection, nocardiosis, affects those patients with structural lung disease or immunosuppression. Although nocardiosis is capable of progressing into a serious and metastatic disease, early recognition and prompt treatment usually result in successful outcomes benefitting the patient.

Abstract Olanzapine is a second‐generation antipsychotic drug that is often used to treat schizophrenia and manic attacks. An increasing number of cases in recent years have shown that olanzapine is associated with vascular thromboembolic disease (VTD). Here, we reported a case of patient with history of taking aripiprazole, benzhexol, olanzapine, and sertraline for 5 years. He was admitted because of aggravated chest tightness, chest pain, and shortness of breath sustaining for 3 days. Laboratory examination and computed tomography angiography of the chest revealed new pulmonary embolus which involved the main trunk of the pulmonary artery and bilateral pulmonary arteries, with pneumonic infiltration in the left upper lobe. After thrombolytic therapy, the patient was out of danger.

Perovskite solar cells have attracted much attention due to their easy fabrication, low cost, and high photoelectric conversion efficiency. To reduce pollution, the absorption layer thickness of perovskite solar cells should be reduced. Moreover, the ultrathin layer can effectively depress the electron–hole recombination in the process of carrier transfer. However, the light absorption of the ultrathin perovskite solar cell is not satisfactory. The promising pathways to design absorption-enhanced ultrathin perovskite solar cells are plasmonic light trapping and anti-reflection coating. In this paper, we propose a design for the light absorption enhancement of ultrathin solar cells with a 100 nm perovskite layer through the integration of plasmonic structure arrays and moth-eye textured anti-reflection coatings. Due to the plasmonic scattering and the antireflection effect, an optimized light absorption enhancement of 41% has been achieved, compared with a 100 nm blank layer. In this case, a silver cylindrical array with a radius of 100 nm, a height of 120 nm, and a coverage of 12% is embedded into the rear-side hole transport layer. Inverted pyramids of the moth-eye textures with a base length of 180 nm and a depth of 125 nm are located on the front surface of the antireflection coating and further improve the perovskite light absorption. The absorbance of the 100 nm layer is dramatically raised to 72.51%, which is comparable to that of a 300 nm perovskite layer (72.86%). The simulation results pave the way for the realization of environmental-friendly and high-performance perovskite optoelectronic devices.

The advantages coming from involving quantum systems in machine learning are still not fully clear. Here, the authors propose a software/hardware co-design framework towards quantum-friendly neural networks showing quantum advantage, representing data as either random variables or numbers in unitary matrices.

Cocoa and cupuassu are evergreen Amazonian trees belonging to the genus Theobroma, with morphologically distinct fruits, including pods and beans. These beans are generally used for agri-food and cosmetics and have high fat and carbohydrates contents. The beans also contain interesting bioactive compounds, among which are polyphenols and methylxanthines thought to be responsible for various health benefits such as protective abilities against cardiovascular and neurodegenerative disorders and other metabolic disorders such as obesity and diabetes. Although these pods represent 50–80% of the whole fruit and provide a rich source of proteins, they are regularly eliminated during the cocoa and cupuassu transformation process. The purpose of this work is to provide an overview of recent research on cocoa and cupuassu pods and beans, with emphasis on their chemical composition, bioavailability, and pharmacological properties. According to the literature, pods and beans from cocoa and cupuassu are promising ecological and healthy resources.

Seismic full wave inversion (FWI) is a widely used non-linear seismic imaging method used to reconstruct subsurface velocity images, however it is time consuming, has high computational cost and depend heavily on human interaction. Recently, deep learning has accelerated it’s use in several data-driven techniques, however most deep learning techniques suffer from overfitting and stability issues. In this work, we propose an edge computing-based data-driven inversion technique based on supervised deep convolutional neural network to accurately reconstruct the subsurface velocities. Deep learning based data-driven technique depends mostly on bulk data training. In this work, we train our deep convolutional neural network (DCN) (UNet and InversionNet) on the raw seismic data and their corresponding velocity models during the training phase to learn the non-linear mapping between the seismic data and velocity models. The trained network is then used to estimate the velocity models from new input seismic data during the prediction phase. The prediction phase is performed on a resource-constrained edge device such as Raspberry Pi. Raspberry Pi provides real-time and on-device computational power to execute the inference process. In addition, we demonstrate robustness of our models to perform inversion in the presence on noise by performing both noise-aware and no-noise training and feeding the resulting trained models with noise at different signal-to-noise (SNR) ratio values. We make great efforts to achieve very feasible inference times on the Raspberry Pi for both models. Specifically, the inference times per prediction for UNet and InversionNet models on Raspberry Pi were 22 and 4 s respectively whilst inference times for both models on the GPU were 2 and 18 s which are very comparable. Finally, we have designed a user-friendly interactive graphical user interface (GUI) to automate the model execution and inversion process on the Raspberry Pi.

Abstract Background This study aimed to elucidate the effects and mechanisms of Radix Linderae (RL) extracts on a mouse model of diabetic bladder dysfunction (DBD), especially on later decompensated phase. Methods Male C57BL/6J mice were intraperitoneally injected with streptozotocin (STZ) after 4 weeks of high-fat diet (HFD) feeding. DBD mouse models (later decompensated phase) were developed by 12-weeks persistent hyperglycemia and then treated with RL extracts for 4 weeks. During administration, the fasting blood glucose (FBG) test was performed once a week. Four weeks later, oral glucose tolerance test (OGTT), voided stain on paper (VSOP), and urodynamic alteration were explored. We also performed haematoxylin and eosin (H&E) and Masson’s trichrome staining to observe the histology of the bladder. Then, the contractile responses to α, β-methylene ATP, capsaicin (CAP), KCl and carbachol were measured. Moreover, qPCR assay was performed to analyse the bladder gene expression levels of M3 receptors and TRPV1. Results The diabetic mice exhibited higher FBG, OGTT and urine production, and no substantial alteration was observed after RL treatment. Urodynamic test showed the maximum bladder capacity (MBC), residual volume (RV) and bladder compliance (BC), as well as the decrement of voided efficiency (VE) and micturition volume (MV), remarkably increased in the DBD mice. Furthermore, RL treatment significant improved urodynamic urination, with lower MBC, RV, and, BC, as well as higher VE and MV, as compared with the model groups. The wall thickness of the bladder and the ratio of smooth muscle/collagen remarkably increased, and RL could effectively attenuate the pathological change. The response of bladder strips to the stimulus was also reduced in the DBD mice, and RL treatment markedly increased the contraction. Furthermore, the gene expression levels of M3 receptors and TRPV1 were down-regulated in the bladders of the diabetic mice, whereas RL treatment retrieved those gene expression levels. Conclusions RL extracts can improve the bladder voiding functions of the DBD model mice in later decompensated phase, and underlying mechanisms was associated with mediating the gene expression of M3 receptors and TRPV1 in the bladder instead of improving blood sugar levels.

Psoriasis is a complex inflammatory disease resulting from the activation of T helper (Th) 1 and Th17 cells. Recent evidence suggests that abnormal activation of Toll-like receptors (TLRs) 7, 8 and 9 contributes to the initiation and maintenance of psoriasis. We have evaluated the effects of TLR antagonists on the gene expression profile in an IL-23-induced skin inflammation model in mice. Psoriasis-like skin lesions were induced in C57BL/6 mice by intradermal injection of IL-23 in the dorsum. Two TLR antagonists were compared: IMO-3100, an antagonist of TLRs 7 and 9, and IMO-8400, an antagonist of TLRs 7, 8 and 9, both of which previously have been shown to reduce epidermal hyperplasia in this model. Skin gene expression profiles of IL-23-induced inflammation were compared with or without TLR antagonist treatment. IL-23 injection resulted in alteration of 5100 gene probes (fold change ≥ 2, FDR < 0.05) including IL-17 pathways that are up-regulated in psoriasis vulgaris. Targeting TLRs 7, 8 and 9 with IMO-8400 resulted in modulation of more than 2300 mRNAs while targeting TLRs 7 and 9 with IMO-3100 resulted in modulation of more than 1900 mRNAs. Both agents strongly decreased IL-17A expression (>12-fold reduction), normalized IL-17 induced genes such as beta-defensin and CXCL1, and normalized aberrant expression of keratin 16 (indicating epidermal hyperplasia). These results suggest that IL-23-driven inflammation in mouse skin may be dependent on signaling mediated by TLRs 7, 8, and 9 and that these receptors represent novel therapeutic targets in psoriasis vulgaris and other diseases with similar pathophysiology.