Acute and preventive treatment of menstrual migraine: a meta-analysis.

BACKGROUND AND OBJECTIVES: About a quarter of migraine cases among women have menstrual migraine (MM), which is usually more severe, longer lasting, and less responsive to treatment than typical migraine. Randomized controlled trials (RCTs) have evaluated the efficacy of several medication in the acute and preventive treatment of MM; this meta-analysis compared the effectiveness of these treatments. METHODS: We conducted systematic searches in the Cochrane Central Register of Controlled Trials, MEDLINE, and Embase databases. The primary outcomes of acute treatment trials were pain relief at 2 and 24 h after treatment compared with placebo or another treatment. The three endpoints we checked for studying MM prevention were: no recurrence of headaches each month, a 50% reduction in monthly migraine days from baseline, and a decrease in the mean number of headache days per month. RESULTS: Out of 342 studies, 26 RCTs met the criteria. Triptans, combined with or without other analgesics, were superior to placebo in providing pain relief in the acute treatment and prevention of MM. Among the treatments, sumatriptan and lasmiditan demonstrated superior pain relief at 2 h (OR: 4.62) and 24 h (OR: 4.81). Frovatriptan exhibited effectiveness in preventing headache recurrence, whereas galcanezumab and erenumab displayed significant preventive benefits in reducing headache days per month. CONCLUSION: Sumatriptan and lasmiditan are effective first-line treatments for acute MM. For prevention, frovatriptan may be the more effective of triptans. Compared with triptans, CGRP monoclonal antibodies, here including erenumab and galcanezumab, are more effective in reducing headache days, and therefore, in preventing MM.

Cancer classification: Mutual information, target network and strategies of therapy.

BACKGROUND: Cancer therapy is a challenging research area because side effects often occur in chemo and radiation therapy. We intend to study a multi-targets and multi-components design that will provide synergistic results to improve efficiency of cancer therapy. METHODS: We have developed a general methodology, AMFES (Adaptive Multiple FEature Selection), for ranking and selecting important cancer biomarkers based on SVM (Support Vector Machine) classification. In particular, we exemplify this method by three datasets: a prostate cancer (three stages), a breast cancer (four subtypes), and another prostate cancer (normal vs. cancerous). Moreover, we have computed the target networks of these biomarkers as the signatures of the cancers with additional information (mutual information between biomarkers of the network). Then, we proposed a robust framework for synergistic therapy design approach which includes varies existing mechanisms. RESULTS: These methodologies were applied to three GEO datasets: GSE18655 (three prostate stages), GSE19536 (4 subtypes breast cancers) and GSE21036 (prostate cancer cells and normal cells) shown in. We selected 96 biomarkers for first prostate cancer dataset (three prostate stages), 72 for breast cancer (luminal A vs. luminal B), 68 for breast cancer (basal-like vs. normal-like), and 22 for another prostate cancer (cancerous vs. normal. In addition, we obtained statistically significant results of mutual information, which demonstrate that the dependencies among these biomarkers can be positive or negative. CONCLUSIONS: We proposed an efficient feature ranking and selection scheme, AMFES, to select an important subset from a large number of features for any cancer dataset. Thus, we obtained the signatures of these cancers by building their target networks. Finally, we proposed a robust framework of synergistic therapy for cancer patients. Our framework is not only supported by real GEO datasets but also aim to a multi-targets/multi-components drug design tool, which improves the traditional single target/single component analysis methods. This framework builds a computational foundation which can provide a clear classification of cancers and lead to an efficient cancer therapy.

Efficient population utilization strategy for particle swarm optimizer.

The particle swarm optimizer (PSO) is a population-based optimization technique that can be applied to a wide range of problems. This paper presents a variation on the traditional PSO algorithm, called the efficient population utilization strategy for PSO (EPUS-PSO), adopting a population manager to significantly improve the efficiency of PSO. This is achieved by using variable particles in swarms to enhance the searching ability and drive particles more efficiently. Moreover, sharing principals are constructed to stop particles from falling into the local minimum and make the global optimal solution easier found by particles. Experiments were conducted on unimodal and multimodal test functions such as Quadric, Griewanks, Rastrigin, Ackley, and Weierstrass, with and without coordinate rotation. The results show good performance of the EPUS-PSO in solving most benchmark problems as compared to other recent variants of the PSO.