Escherichia coli O157:H7 Resistan Antibiotik Pada Daging Kebab yang Dijual di Sekitar Kampus IPB Dramaga Bogor

Kebab merupakan salah satu makanan siap saji atau ready to eat (RTE) yang populer di seluruh dunia. Escherichia coli (E. coli) O157:H7 banyak dihubungkan dengan kejadian outbreak foodborne disease pada kebab. Kontaminasi E. coli O157:H7 resistan antibiotik pada kebab dapat menimbulkan masalah kesehatan serius. Penelitian ini bertujuan mengidentifikasi E. coli O157:H7 resistan antibiotik yang diisolasi dari daging kebab yang dijual di sekitar Kampus IPB Dramaga Bogor. Total 43 sampel daging kebab diambil dari seluruh pedagang kebab di sekitar Kampus IPB Dramaga dalam radius 2 km dari batas terluar Kampus. Isolasi dan identifikasi E. coli mengacu pada Standar Nasional Indonesia (SNI) 2897:2008 dari Badan Standardisasi Nasional tentang Metode Pengujian Cemaran Mikroba dalam Daging, Telur, dan Susu, serta Hasil Olahannya. Uji serotyping E. coli O157:H7 menggunakan uji Serologis. Uji resistansi E. coli O157:H7 mengacu pada standar Clinical Laboratory Standards Institute (CLSI) dan dilakukan terhadap 10 jenis antibiotik menggunakan metode Kirby-Bauer disk diffusion. Data yang diperoleh dianalisis secara deskriptif. Hasil penelitian menunjukkan enam isolat positif E. coli O157:H7 (31.6%; 6/19) yang resistan terhadap ampisilin, amoksisilin-asam klavulanat, sefotaksim, gentamisin, siprofloksasin, enrofloksasin, kolistin sulfat dengan satu isolat termasuk multidrug resistant (MDR). Semua isolat E. coli O157:H7 masih sensitif terhadap trimethoprim-sulfametoksasol, oksitetrasiklin, dan kloramfenikol.

Unsupervised learning of anomalous diffusion data an anomaly detection approach

The characterization of diffusion processes is a keystone in our understanding of a variety of physical phenomena. Many of these deviate from Brownian motion, giving rise to anomalous diffusion. Various theoretical models exists nowadays to describe such processes, but their application to experimental setups is often challenging, due to the stochastic nature of the phenomena and the difficulty to harness reliable data. The latter often consists on short and noisy trajectories, which are hard to characterize with usual statistical approaches. In recent years, we have witnessed an impressive effort to bridge theory and experiments by means of supervised machine learning techniques, with astonishing results. In this work, we explore the use of unsupervised methods in anomalous diffusion data. We show that the main diffusion characteristics can be learnt without the need of any labelling of the data. We use such method to discriminate between anomalous diffusion models and extract their physical parameters. Moreover, we explore the feasibility of finding novel types of diffusion, in this case represented by compositions of existing diffusion models. At last, we showcase the use of the method in experimental data and demonstrate its advantages for cases where supervised learning is not applicable.

Efek Sinergis Kombinasi Ekstrak Rimpang Kunyit (Curcuma domestica Val.) dan Ampisilin terhadap Escherichia coli secara in vitro

Escherichia coli (E.coli) pada kondisi tertentu dapat diobati dengan ampisilin. Namun ampisilin saat ini mulai ditinggalkan karena tingginya kasus E.coli yang resisten ampisilin. Salah satu cara untuk mengatasi masalah resistensi ini adalah dengan mengkombinasi ekstrak rimpang kunyit (ERK) dan ampisilin. Tujuan penelitian ini adalah untuk mengetahui efek antimikroba dan efek sinergis kombinasi ERK (Curcuma domestica Val.) dan ampisilin terhadap E.coli secara in vitro. Penelitian ini menggunakan desain uji esksperimental laboratorik sungguhan secara in vitro dengan metode agar diffusion. Data yang diperoleh disajikan dalam bentuk tabel dan dinalisis secara deskriptif. Dari hasil penelitian didapatkan bahwa ERK (Curcuma domestica Val.) konsentrasi 165, 330, 660 µg/ml tidak didapatkan zona inhibisi terhadap E.coli. Persentasi peningkatan zona inhibisi pada MHA-ERK-ampisilin 165, 330, 660 µg/ml : 24,11%; 38,79% ; 47,39% lebih besar dibanding ampisilin tunggal. Simpulan dari penelitian ini adalah ekstrak rimpang kunyit (Curcuma domestica Val.) 165 µg/ml, 330 µg/ml, dan 660 µg/ml tidak memiliki efek antimikroba terhadap Escherichia coli secara in vitro sedangkan kombinasi ekstrak rimpang kunyit (Curcuma domestica Val.) dan ampisilin memiliki efek sinergis terhadap Escherichia coli secara in vitro.

Reducing Susceptibility Distortion Related Image Blurring in Diffusion MRI EPI Data

Diffusion magnetic resonance imaging (MRI) is an increasingly popular technique in basic and clinical neuroscience. One promising application is to combine diffusion MRI with myelin maps from complementary MRI techniques such as multi-parameter mapping (MPM) to produce g-ratio maps that represent the relative myelination of axons and predict their conduction velocity. Statistical Parametric Mapping (SPM) can process both diffusion data and MPMs, making SPM the only widely accessible software that contains all the processing steps required to perform group analyses of g-ratio data in a common space. However, limitations have been identified in its method for reducing susceptibility-related distortion in diffusion data. More generally, susceptibility-related image distortion is often corrected by combining reverse phase-encoded images (blip-up and blip-down) using the arithmetic mean (AM), however, this can lead to blurred images. In this study we sought to (1) improve the susceptibility-related distortion correction for diffusion MRI data in SPM; (2) deploy an alternative approach to the AM to reduce image blurring in diffusion MRI data when combining blip-up and blip-down EPI data after susceptibility-related distortion correction; and (3) assess the benefits of these changes for g-ratio mapping. We found that the new processing pipeline, called consecutive Hyperelastic Susceptibility Artefact Correction (HySCO) improved distortion correction when compared to the standard approach in the ACID toolbox for SPM. Moreover, using a weighted average (WA) method to combine the distortion corrected data from each phase-encoding polarity achieved greater overlap of diffusion and more anatomically faithful structural white matter probability maps derived from minimally distorted multi-parameter maps as compared to the AM. Third, we showed that the consecutive HySCO WA performed better than the AM method when combined with multi-parameter maps to perform g-ratio mapping. These improvements mean that researchers can conveniently access a wide range of diffusion-related analysis methods within one framework because they are now available within the open-source ACID toolbox as part of SPM, which can be easily combined with other SPM toolboxes, such as the hMRI toolbox, to facilitate computation of myelin biomarkers that are necessary for g-ratio mapping.

Conductivity Tensor Imaging of the Human Brain Using Water Mapping Techniques

Conductivity tensor imaging (CTI) has been recently proposed to map the conductivity tensor in 3D using magnetic resonance imaging (MRI) at the frequency range of the brain at rest, i.e., low-frequencies. Conventional CTI mapping methods process the trans-receiver phase of the MRI signal using the MR electric properties tomography (MR-EPT) technique, which in turn involves the application of the Laplace operator. This results in CTI maps with a low signal-to-noise ratio (SNR), artifacts at tissue boundaries and a limited spatial resolution. In order to improve on these aspects, a methodology independent from the MR-EPT method is proposed. This relies on the strong assumption for which electrical conductivity is univocally pre-determined by water concentration. In particular, CTI maps are calculated by combining high-frequency conductivity derived from water maps and multi b-value diffusion tensor imaging (DTI) data. Following the implementation of a pipeline to optimize the pre-processing of diffusion data and the fitting routine of a multi-compartment diffusivity model, reconstructed conductivity images were evaluated in terms of the achieved spatial resolution in five healthy subjects scanned at rest. We found that the pre-processing of diffusion data and the optimization of the fitting procedure improve the quality of conductivity maps. We achieve reproducible measurements across healthy participants and, in particular, we report conductivity values across subjects of 0.55 ± 0.01Sm, 0.3 ± 0.01Sm and 2.15 ± 0.02Sm for gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF), respectively. By attaining an actual spatial resolution of the conductivity tensor close to 1 mm in-plane isotropic, partial volume effects are reduced leading to good discrimination of tissues with similar conductivity values, such as GM and WM. The application of the proposed framework may contribute to a better definition of the head tissue compartments in electroencephalograpy/magnetoencephalography (EEG/MEG) source imaging and be used as biomarker for assessing conductivity changes in pathological conditions, such as stroke and brain tumors.

Reducing susceptibility distortion related image blurring in diffusion MRI EPI data

Diffusion magnetic resonance imaging (MRI) is an increasingly popular technique in basic and clinical neuroscience. One promising application is to combine diffusion MRI with myelin maps from complementary MRI techniques such as multi-parameter mapping (MPM) to produce g-ratio maps that represent the relative myelination of axons and predict their conduction velocity. Statistical Parametric Mapping (SPM) can process both diffusion data and MPMs, making SPM the only widely accessible software that contains all the processing steps required to perform group analyses of g-ratio data in a common space. However, limitations have been identified in its method for reducing susceptibility-related distortion in diffusion data. More generally, susceptibility-related image distortion is often corrected by combining oppositely phase-encoded images (blip-up and blip-down) using the arithmetic mean (AM), however, this can lead to blurred images. In this study we sought to (1) improve the susceptibility-related distortion correction for diffusion MRI data in SPM; (2) deploy an alternative approach to the AM to reduce image blurring in diffusion MRI data when combining blip-up and blip-down EPI data after susceptibility-related distortion correction; and (3) assess the benefits of these changes for g-ratio mapping. We found that the new processing pipeline, called consecutive HySCO, improved distortion correction when compared to the standard approach in the ACID toolbox for SPM. Moreover, using a weighted average (WA) method to combine the distortion corrected data from each phase-encoding polarity achieved greater overlap of diffusion and more anatomically faithful structural white matter probability maps derived from minimally distorted multi-parameter maps as compared to the AM. Third, we showed that the consecutive HySCO WA performed better than the AM method when combined with multi-parameter maps to perform g-ratio mapping. These improvements mean that researchers can conveniently access a wide range of diffusion-related analysis methods within one framework because they are now available within the open-source ACID toolbox as part of SPM, which can be easily combined with other SPM toolboxes, such as the hMRI toolbox, to facilitate computation of myelin biomarkers that are necessary for g-ratio mapping.

Image segmentation of plexiform neurofibromas from a deep neural network using multiple b-value diffusion data

We assessed the accuracy of semi-automated tumor volume maps of plexiform neurofibroma (PN) generated by a deep neural network, compared to manual segmentation using diffusion weighted imaging (DWI) data. NF1 Patients were recruited from a phase II clinical trial for the treatment of PN. Multiple b-value DWI was imaged over the largest PN. All DWI datasets were registered and intensity normalized prior to segmentation with a multi-spectral neural network classifier (MSNN). Manual volumes of PN were performed on 3D-T2 images registered to diffusion images and compared to MSNN volumes with the Sørensen-Dice coefficient. Intravoxel incoherent motion (IVIM) parameters were calculated from resulting volumes. 35 MRI scans were included from 14 subjects. Sørensen-Dice coefficient between the semi-automated and manual segmentation was 0.77 ± 0.016. Perfusion fraction (f) was significantly higher for tumor versus normal tissue (0.47 ± 0.42 vs. 0.30 ± 0.22, p = 0.02), similarly, true diffusion (D) was significantly higher for PN tumor versus normal (0.0018 ± 0.0003 vs. 0.0012 ± 0.0002, p < 0.0001). By contrast, the pseudodiffusion coefficient (D*) was significantly lower for PN tumor versus normal (0.024 ± 0.01 vs. 0.031 ± 0.005, p < 0.0001). Volumes generated by a neural network from multiple diffusion data on PNs demonstrated good correlation with manual volumes. IVIM analysis of multiple b-value diffusion data demonstrates significant differences between PN and normal tissue.