Background: Androgen deprivation therapy (ADT) suppresses the production of androgen, and ADT is broadly used for intermediate or higher risk disease including advanced and metastatic cancer. ADT is associated with numerous adverse effects derived from the pharmacological properties. Previous meta-analysis on fracture risk among ADT users possessed limited data without further subgroup analysis. Risk estimation of updated real-world evidence on ADT-related fracture remains unknown. Objectives: To assess the risk of fracture and fracture requiring hospitalization associated with ADT among prostate cancer population on different disease conditions, treatment regimen, dosage level, fracture sites. Methods: The Cochrane Library, PubMed, and Embase databases were systematically screened for eligible cohort studies published from inception to March 2020. Two authors independently reviewed all the included studies. The risks of any fracture and of fracture requiring hospitalization were assessed using a random-effects model, following by leave-one-out, stratified, and sensitivity analyses. The Grading of Recommendations Assessments, Development and Evaluations (GRADE) system was used to grade the certainty of evidence. Results: Sixteen eligible studies were included, and total population was 519,168 men. ADT use is associated with increasing fracture risk (OR, 1.39; 95% CI, 1.26–1.52) and fracture requiring hospitalization (OR, 1.55; 95% CI, 1.29–1.88). Stratified analysis revealed that high-dose ADT results in an elevated risk of fracture with little statistical heterogeneity, whereas sensitivity analysis restricted to adjust for additional factors indicated increased fracture risks for patients with unknown stage prostate cancer or with no restriction on age with minimal heterogeneity. The GRADE level of evidence was moderate for any fracture and low for fracture requiring hospitalization. Conclusion: Cumulative evidence supports the association of elevated fracture risk with ADT among patients with prostate cancer, including those with different disease conditions, treatment regimens, dose levels, and fracture sites. Further prospective trials with intact information on potential risk factors on fracture under ADT use are warranted to identify the risky population.
The aim of the study was to investigate whether the 7-valent pneumococcal conjugate vaccine (PCV7) alters common risk factors of nasopharyngeal carriage by Streptococcus pneumoniae in children.From July 2005 through December 2010, we performed a cross-sectional study investigating risk factors associated with pneumococcal carriage in children. Parents of participating children completed questionnaires including whether or not the children received PCV7 vaccination.Among 9705 children, 20.2% of them received at least 1 dose of the PCV7 vaccine. Multivariate logistic regression models identified older age, having 1 sibling in a family, history of acute otitis media and household exposure to smoking as independent risk factors for pneumococcal carriage in the unvaccinated group, but not associated with pneumococcal carriage in the vaccinated group. The number of siblings ≥2 in a family, history of upper respiratory tract infection and child-care attendance were strong factors associated with pneumococcal carriage in children, regardless of vaccination. In vaccinated group, breast-feeding was associated with increased nonvaccine type pneumococcal carriage, mainly in children with upper respiratory tract infection.PCV7 decreased the association between pneumococcal carriage and older age, 1 sibling in a family, history of acute otitis media and household exposure to smoking, but increased the association between pneumococcal carriage and breast-feeding.
We reported two cases with community-acquired pneumonia caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) who returned from Wuhan, China in January, 2020. The reported cases highlight non-specific clinical presentations of 2019 novel coronavirus disease (COVID-19) as well as the importance of rapid laboratory-based diagnosis.
Cystoscopic onabotulinumtoxinA (onaBoNTA) intradetrusor injection is an efficient and durable modality for treating sensory bladder disorders. However, the inconvenience of using the cystoscopic technique and anesthesia, and the adverse effects of direct needle injection (e.g., haematuria, pain, and infections) have motivated researchers and clinicians to develop diverse injection-free procedures to improve accessibility and prevent adverse effects. However, determining suitable approaches to transfer onaBoNTA, a large molecular and hydrophilic protein, through the impermeable urothelium to reach therapeutic efficacy remains an unmet medical need. Researchers have provided potential solutions in three categories: To disrupt the barrier of the urothelium (e.g., protamine sulfate), to increase the permeability of the urothelium (e.g., electromotive drug delivery and low-energy shock wave), and to create a carrier for transportation (e.g., liposomes, thermosensitive hydrogel, and hyaluronan-phosphatidylethanolamine). Thus far, most of these novel administration techniques have not been well established in their long-term efficacy; therefore, additional clinical trials are warranted to validate the therapeutic efficacy and durability of these techniques. Finally, researchers may make progress with new combinations or biomaterials to change clinical practices in the future.
Rechargeable aqueous batteries are the leading candidate to meet the surging demand for safe and low-cost energy storage systems. To achieve their development, the optimization of aqueous electrolyte for wide electrochemical stability window (ESW) and stable electrolyte/electrode interfaces is crucial. However, the selection of salts and solvents is critical to dictate the ultimate performance of Zn-ion batteries. Conventional approach requires trial-and-error optimization experiments, which is time-consuming and laborious. Herein, we show an integrated workflow that combines robotics and machine learning to accelerate the optimization of all temperature stable aqueous electrolyte with programmable ESW, ionic conductivity, and electrolyte/electrode interface stability. First, an automated pipetting robot is commanded to prepare 557 electrolyte mixtures using 2 salts (ZnCl 2 and Zn(OTf) 2 ) and 2 solvents (water and methanol). After equilibration/stabilization at 30 °C, the solubility of the electrolyte mixtures is evaluated to train a support-vector machine classifier. Next, through active learning loops with data augmentation, 60 electrolyte solutions are prepared/evaluated, establishing an artificial neural network prediction model. The achieved model is capable of two main functions: (1) predicting the electrochemical performance of an electrolyte solution based on its formulation, and (2) conducting multi-objective optimization on electrolytes to identify stable electrolytes with high Coulombic efficiency, rate performance, and interfacial stability. The model-suggested electrolyte with wide ESW, high conductivity, and stable electrolyte/electrode interfaces deliver ultrahigh cyclic stability in Zn||Zn symmetric cells, as well as outstanding capacity retention, fast charge/discharge capability, and excellent efficiency in full cells. The fusion of robotics-accelerated experimentation, machine intelligence, and simulation tools facility the discovery of electrolytes that involve time-intensive experiments and multi-dimensional design spaces.
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