Modeling of relationships between pharmacokinetics and blockade of agonist-induced elevation of intraurethral pressure and mean arterial pressure in conscious dogs treated with alpha(1)-adrenoceptor antagonists.

Fiduxosin is a new alpha(1)-adrenoceptor antagonist targeted for the treatment of symptomatic benign prostatic hyperplasia. The purpose of this study was to determine and compare the potencies of the alpha(1)-adrenoceptor antagonists terazosin, doxazosin, tamsulosin, and fiduxosin, based on relationships between plasma drug concentrations and blockade of phenylephrine (PE)-induced intraurethral (IUP) and mean arterial pressure (MAP) responses after single oral dosing in conscious male beagle dogs. Magnitude of blockade and plasma concentrations were evaluated at selected time points over 24 h. All drugs produced dose-dependent antagonism of PE-induced IUP and MAP responses. When IUP and MAP blockade effects were plotted against drug plasma concentrations, direct relationships were observed that were well described by the sigmoidal maximal effect model. IUP IC(50) values for terazosin, doxazosin, tamsulosin, and fiduxosin were 48.6, 48.7, 0.42, and 261 ng/ml, respectively. MAP IC(50) values were 12.2, 13.8, 1.07, and 1904 ng/ml, respectively. Uroselectivity index values, defined as MAP IC(50)/IUP IC(50), were 0.25, 0.28, 2.6, and 7.3, respectively. These results extend previous observations with terazosin in this model, showing that doxazosin exhibits a uroselectivity index comparable to terazosin, consistent with the lack of alpha(1)-adrenoceptor subtype selectivity or uroselectivity of these drugs. Tamsulosin, an alpha(1a)-/alpha(1d)-subtype selective agent, had an index value approximately 10-fold greater than the nonselective drugs. Based on its pharmacokinetic profile and a relative uroselectivity 29-fold greater than the nonselective drugs, fiduxosin is expected to exhibit greater selectivity for urethral compared with vascular alpha(1)-adrenoceptors in human and should be a novel, long-acting, uroselective alpha(1)-adrenoceptor antagonist.     

Short-term impact of COVID-19 on quality of life,perceived stress,and serious psychological distress in an adult population in the midwest United States

Quality of Life Research - This study aimed to investigate changes over time in quality of life, perceived stress, and serious psychological distress for individuals diagnosed with COVID-19 in an...     

Clinical Concept-Based Radiology Reports Classification Pipeline for Lung Carcinoma

Rising incidence and mortality of cancer have led to an incremental amount of research in the field. To learn from preexisting data, it has become important to capture maximum information related to disease type, stage, treatment, and outcomes. Medical imaging reports are rich in this kind of information but are only present as free text. The extraction of information from such unstructured text reports is labor-intensive. The use of Natural Language Processing (NLP) tools to extract information from radiology reports can make it less time-consuming as well as more effective. In this study, we have developed and compared different models for the classification of lung carcinoma reports using clinical concepts. This study was approved by the institutional ethics committee as a retrospective study with a waiver of informed consent. A clinical concept-based classification pipeline for lung carcinoma radiology reports was developed using rule-based as well as machine learning models and compared. The machine learning models used were XGBoost and two more deep learning model architectures with bidirectional long short-term neural networks. A corpus consisting of 1700 radiology reports including computed tomography (CT) and positron emission tomography/computed tomography (PET/CT) reports were used for development and testing. Five hundred one radiology reports from MIMIC-III Clinical Database version 1.4 was used for external validation. The pipeline achieved an overall F1 score of 0.94 on the internal set and 0.74 on external validation with the rule-based algorithm using expert input giving the best performance. Among the machine learning models, the Bi-LSTM_dropout model performed better than the ML model using XGBoost and the Bi-LSTM_simple model on internal set, whereas on external validation, the Bi-LSTM_simple model performed relatively better than other 2. This pipeline can be used for clinical concept-based classification of radiology reports related to lung carcinoma from a huge corpus and also for automated annotation of these reports.

     

Enhanced electron transfer mediated detection of hydrogen peroxide using a silver nanoparticle–reduced graphene oxide–polyaniline fabricated electrochemical sensor

The current study aims at the development of an electrochemical sensor based on a silver nanoparticle–reduced graphene oxide–polyaniline (AgNPs–rGO–PANI) nanocomposite for the sensitive and selective detection of hydrogen peroxide (H₂O₂). The nanocomposite was fabricated by simple in situ synthesis of PANI at the surface of rGO sheet which was followed by stirring with AEC biosynthesized AgNPs to form a nanocomposite. The AgNPs, GO, rGO, PANI, rGO–PANI, and AgNPs–rGO–PANI nanocomposite and their interaction were studied by UV-vis, FTIR, XRD, SEM, EDX and XPS analysis. AgNPs–rGO–PANI nanocomposite was loaded (0.5 mg cm⁻²) on a glassy carbon electrode (GCE) where the active surface area was maintained at 0.2 cm² for investigation of the electrochemical properties. It was found that AgNPs–rGO–PANI–GCE had high sensitivity towards the reduction of H₂O₂ than AgNPs–rGO which occurred at −0.4 V vs. SCE due to the presence of PANI (AgNPs have direct electronic interaction with N atom of the PANI backbone) which enhanced the rate of transfer of electron during the electrochemical reduction of H₂O₂. The calibration plots of H₂O₂ electrochemical detection was established in the range of 0.01 μM to 1000 μM (R² = 0.99) with a detection limit of 50 nM, the response time of about 5 s at a signal-to-noise ratio (S/N = 3). The sensitivity was calculated as 14.7 μA mM⁻¹ cm⁻² which indicated a significant potential as a non-enzymatic H₂O₂ sensor.

The current study aims at the development of an electrochemical sensor based on a silver nanoparticle–reduced graphene oxide–polyaniline (AgNPs–rGO–PANI) nanocomposite for the sensitive and selective detection of hydrogen peroxide (H₂O₂).