Moxibustion for hypertension: a systematic review

<p>Abstract</p> <p>Background</p> <p>Moxibustion is a traditional East Asian medical therapy that uses the heat generated by burning herbal preparations containing <it>Artemisia vulgaris </it>to stimulate acupuncture points. The aim of this review was to evaluate previously published clinical evidence for the use of moxibustion as a treatment for hypertension.</p> <p>Methods</p> <p>We searched 15 databases without language restrictions from their respective dates of inception until March 2010. We included randomized controlled trials (RCTs) comparing moxibustion to either antihypertensive drugs or no treatment. The risk of bias was assessed for each RCT.</p> <p>Results</p> <p>During the course of our search, we identified 519 relevant articles. A total of 4 RCTs met all the inclusion criteria, two of which failed to report favorable effects of moxibustion on blood pressure (BP) compared to the control (antihypertensive drug treatment alone). However, a third RCT showed significant effects of moxibustion as an adjunct treatment to antihypertensive drug therapy for lowering BP compared to antihypertensive drug therapy alone. The fourth RCT included in this review addressed the immediate BP-lowering effects of moxibustion compared to no treatment. None of the included RCTs reported the sequence generation, allocation concealment and evaluator blinding.</p> <p>Conclusion</p> <p>There is insufficient evidence to suggest that moxibustion is an effective treatment for hypertension. Rigorously designed trials are warranted to answer the many remaining questions.</p

Does the Inclusion of Spatio-Temporal Features Improve Bus Travel Time Predictions? A Deep Learning-Based Modelling Approach

With the abundance of public transportation in highly urbanized areas, it is common for passengers to make inefficient or flawed transport decisions due to a lack of information. The exact arrival time of a bus is an example of such information that can aid passengers in making better decisions. The purpose of this study is to provide a method for predicting path-based bus travel time, thereby assisting accurate bus arrival and departure time predictions at each bus stop. Specifically, we develop a Geo-conv Long Short-term Memory (LSTM) model that (1) extracts subsequent spatial features through a 1D Convolution Neural Network (CNN) for the entire bus travel sequence and (2) captures the temporal dependencies between subsequences through the LSTM network. Additionally, this study utilizes additional variables that affect two components of bus travel time (dwelling time and transit time) to precisely predict travel time. The constructed model is then evaluated by the practical application to two bus lines operating in Seoul, Korea. The results show that our model outperforms three other baseline models. Two bus lines with different types of operation show different model performance patterns that are dependent on travel distance. Interestingly, we find that the variable related to the link of the stop location appears to play an important role in predicting bus travel time. We believe that these novel findings will contribute to the literature on transportation and, in particular, on deep learning-based travel time prediction

Does the Inclusion of Spatio-Temporal Features Improve Bus Travel Time Predictions? A Deep Learning-Based Modelling Approach

With the abundance of public transportation in highly urbanized areas, it is common for passengers to make inefficient or flawed transport decisions due to a lack of information. The exact arrival time of a bus is an example of such information that can aid passengers in making better decisions. The purpose of this study is to provide a method for predicting path-based bus travel time, thereby assisting accurate bus arrival and departure time predictions at each bus stop. Specifically, we develop a Geo-conv Long Short-term Memory (LSTM) model that (1) extracts subsequent spatial features through a 1D Convolution Neural Network (CNN) for the entire bus travel sequence and (2) captures the temporal dependencies between subsequences through the LSTM network. Additionally, this study utilizes additional variables that affect two components of bus travel time (dwelling time and transit time) to precisely predict travel time. The constructed model is then evaluated by the practical application to two bus lines operating in Seoul, Korea. The results show that our model outperforms three other baseline models. Two bus lines with different types of operation show different model performance patterns that are dependent on travel distance. Interestingly, we find that the variable related to the link of the stop location appears to play an important role in predicting bus travel time. We believe that these novel findings will contribute to the literature on transportation and, in particular, on deep learning-based travel time prediction

Contralateral acupuncture versus ipsilateral acupuncture in the rehabilitation of post-stroke hemiplegic patients: a systematic review

Abstract Background Contralateral acupuncture (CAT) involves inserting needles in the meridian on the side opposite the disease location and is often used in post-stroke rehabilitation. The aim of this systematic review is to summarize and critically evaluate the evidence for and against the effectiveness of CAT for post-stroke rehabilitation as compared to ipsilateral acupuncture (IAT). Methods Seventeen databases were searched from their inceptions through June 2010. Prospective clinical trials were included if CAT was tested as the sole treatment or as an adjunct to other treatments for post-stroke rehabilitation and compared to IAT. Results Eight randomized clinical trials (RCTs) met our inclusion criteria. Four of them reported favorable effects of CAT compared to IAT for at least one outcome. A meta-analysis showed superior effects of CAT compared to IAT on recovery rate (n = 361; risk ratio (RR), 1.12; 95% confidence intervals (CIs), 1.04 to 1.22, P = 0.005). Subgroup analysis also showed favorable effects of using CAT on patients with cerebral infarction (n = 261; RR, 1.15; 95% CIs, 1.04 to 1.27, P = 0.006). Further analysis including patients with cerebral infarction and intracranial hemorrhage, however, failed to show these advantages (n = 100; RR, 1.11; 95% CIs, 0.85 to 1.46, P = 0.43). Conclusion The results of our systematic review and meta-analysis suggest that there is limited evidence for CAT being superior to IAT in the treatment of cerebral infarction. The total number of RCTs included in our analysis was low, however, and the RCTs included had a high risk of bias. Future RCTs appear to be warranted.</p

Assessments of the In Vitro and In Vivo Linker Stability and Catabolic Fate for the Ortho Hydroxy-Protected Aryl Sulfate Linker by Immuno-Affinity Capture Liquid Chromatography Quadrupole Time-of-Flight Mass Spectrometric Assay

Antibody–drug conjugate (ADC) linkers play an important role in determining the safety and efficacy of ADC. The Ortho Hydroxy-Protected Aryl Sulfate (OHPAS) linker is a newly developed linker in the form of a di-aryl sulfate structure consisting of phenolic payload and self-immolative group (SIG). In this study, using two bioanalytical approaches (namely “bottom-up” and “middle-up” approaches) via the liquid chromatography-quadrupole time-of-flight mass spectrometric (LC-qTOF-MS) method, in vitro and in vivo linker stability experiments were conducted for the OHPAS linker. For comparison, the valine-citrulline-p-aminobenzyloxycarbonyl (VC-PABC) linker was also evaluated under the same experimental conditions. In addition, the catabolite identification experiments at the subunit intact protein level were simultaneously performed to evaluate the catabolic fate of ADCs. As a result, the OHPAS linker was stable in the in vitro mouse/human plasma as well as in vivo pharmacokinetic studies in mice, whereas the VC-PABC linker was relatively unstable in mice in vitro and in vivo. This is because the VC-PABC linker was sensitive to a hydrolytic enzyme called carboxylesterase 1c (Ces1c) in mouse plasma. In conclusion, the OHPAS linker appears to be a good linker for ADC, and further experiments would be warranted to demonstrate the efficacy and toxicity related to the OHPAS linker

ITC-6102RO, a novel B7-H3 antibody-drug conjugate, exhibits potent therapeutic effects against B7-H3 expressing solid tumors

Abstract Background The B7-H3 protein, encoded by the CD276 gene, is a member of the B7 family of proteins and a transmembrane glycoprotein. It is highly expressed in various solid tumors, such as lung and breast cancer, and has been associated with limited expression in normal tissues and poor clinical outcomes across different malignancies. Additionally, B7-H3 plays a crucial role in anticancer immune responses. Antibody-drug conjugates (ADCs) are a promising therapeutic modality, utilizing antibodies targeting tumor antigens to selectively and effectively deliver potent cytotoxic agents to tumors. Methods In this study, we demonstrate the potential of a novel B7-H3-targeting ADC, ITC-6102RO, for B7-H3-targeted therapy. ITC-6102RO was developed and conjugated with dHBD, a soluble derivative of pyrrolobenzodiazepine (PBD), using Ortho Hydroxy-Protected Aryl Sulfate (OHPAS) linkers with high biostability. We assessed the cytotoxicity and internalization of ITC-6102RO in B7-H3 overexpressing cell lines in vitro and evaluated its anticancer efficacy and mode of action in B7-H3 overexpressing cell-derived and patient-derived xenograft models in vivo. Results ITC-6102RO inhibited cell viability in B7-H3-positive lung and breast cancer cell lines, inducing cell cycle arrest in the S phase, DNA damage, and apoptosis in vitro. The binding activity and selectivity of ITC-6102RO with B7-H3 were comparable to those of the unconjugated anti-B7-H3 antibody. Furthermore, ITC-6102RO proved effective in B7-H3-positive JIMT-1 subcutaneously xenografted mice and exhibited a potent antitumor effect on B7-H3-positive lung cancer patient-derived xenograft (PDX) models. The mode of action, including S phase arrest and DNA damage induced by dHBD, was confirmed in JIMT-1 tumor tissues. Conclusions Our preclinical data indicate that ITC-6102RO is a promising therapeutic agent for B7-H3-targeted therapy. Moreover, we anticipate that OHPAS linkers will serve as a valuable platform for developing novel ADCs targeting a wide range of targets