Aim To determine the prevalence of undiagnosed Fabry Disease (FD) in Western Australian (WA) patients undergoing dialysis. Background FD is a multisystem X-linked lysosomal storage disease caused by deficient activity of alpha-galactosidase-A (α-GAL-A). Affected individuals are at risk of developing small-fibre neuropathy, rash, progressive kidney disease, hypertrophic cardiomyopathy and ischaemic stroke. Diagnosis is often delayed by years or even decades. Screening high risk population such as dialysis patients may identify patients with undiagnosed Fabry disease. Methods A cross-sectional study was undertaken of all adult patients receiving dialysis in WA, without previously known FD. After informed consent they were screened for α-GAL-A activity by dried blood spot samples. Low or inconclusive activity were repeated via Centogene in Rostock, Germany with GLA genetic analysis. Ethics approval was granted by Royal Perth Hospital Human Research Ethic Committee REG 14–136; site-specific approval was granted from appropriate authorities; ANZ Clinical Trials Registry U1111–1163-7629. Results Between February 2015 & September 2017, α-GAL-A activity was performed on 526 patients at 16 dialysis sites. Twenty-nine patients had initial low α-GAL-A; repeat testing & GLA genotyping showed no confirmed FD cases. The causes of false positive rates were thought to be secondary to impaired protein synthesis due to patient malnutrition and chronic inflammation, which is common among dialysis patients, in addition to poor sampling handling. Conclusion Analysis of this dialysis population has shown a prevalence of 0% undiagnosed FD. False positives results may occur through impaired protein synthesis and sample handling.

The classical Multi-Dimensional Scaling (MDS) is an important method for data dimension reduction. Nonlinear variants have been developed to improve its performance. One of them is the MDS with Radial Basis Functions (RBF). A key issue that has not been well addressed in MDS-RBF is the effective selection of its centers. This paper treats this selection problem as a multi-task learning problem, which leads us to employ the (2, 1)-norm to regularize the original MDS-RBF objective function. We then study its two reformulations: Diagonal and spectral reformulations. Both can be effectively solved through an iterative block-majorization method. Numerical experiments show that the regularized models can improve the original model significantly.

Regularized Multidimensional Scaling with Radial basis function (RMDS) is a nonlinear variant of classical Multi-Dimensional Scaling (cMDS). A key issue that has been addressed in RMDS is the effective selection of centers of the radial basis functions that plays a very important role in reducing the dimension preserving the structure of the data in higher dimensional space. RMDS uses data in unsupervised settings that means RMDS does not use any prior information of the dataset. This article is concerned on the supervised setting. Here we have incorporated the class information of some members of data to the RMDS model. The class separability term improved the method RMDS significantly and also outperforms other discriminant analysis methods such as Linear discriminant analysis (LDA) which is documented through numerical experiments.

Background: Pregnancy-related illnesses are commonly treated by herbal medicines in our country as well as around the world. Objectives: The purpose of this study was to find out how common herbal use is among Bangladeshi pregnant women, what factors influence it, and how it affects pregnancy outcomes. Methods: Random sampling was done among women who gave birth between July and September 2021 in the maternity ward of an NGO-based clinic and were requested to participate in the face-to-face questionnaire-based survey.Results: 275 women (71.80%) out of 383 used herbs during their pregnancy. Only 27.42% of women who used herbs informed their doctors, and 91.03% of users reported no side effects. Most users thought that herbs were safer than allopathic medications (71.8%). The ground behind the choosing herb was suggestion from family members or self-medication (34.73% and 31.83%, respectively). Ginger (Zingiber officinale Roscoe) (73.10%), lemon (Citrus limon L. Burm. F) (71.27%), black seed (Nigella sativa) (66.55%), mustard oil (Brassica Juncea Mane Kancor) (65.45%), and prune (Prunus domestica) (41.45%) were the most widely utilized herbs. The majority of women used herbs on a daily basis. There were statistically significant differences in several socio-demographic characteristics and pregnancy outcomes between herb users and non-users. Conclusions: The usage of herbs throughout pregnancy is quite prevalent amid Bangladeshi womenfolk, according to this study. Herbs appear to be safe when used often during pregnancy. Furthermore, physicians or medical practitioners have to play a vital role in ensuring the safe usage of herbs among pregnant women.