Clinical pharmacy services for tuberculosis management: a systematic review.

Objective: This study aims to systematically review the content and potential effects of clinical pharmacy services in tuberculosis (TB) care management. Methods: Searches were performed in PubMed, Embase, Cochrane, Scopus, and Web of Science databases following Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Study characteristics and outcomes were extracted, and clinical pharmacy service components were characterized using the Descriptive Elements of Pharmacist Intervention Characterization Tool. Results: Twenty articles were included for full-text assessment, of which 10 fulfilled inclusion criteria, comprising 1,168 patients (N = 39 to 258 per study). These articles included five prospective cohort studies, two case-control studies, two quasi-experimental studies, and one cross-sectional study. Intervention foci within clinical pharmacy services were medication adherence (50%), medication safety (40%), education to patients/caregivers regarding needs/beliefs (30%), optimizing medication/therapy effectiveness (30%), emphasizing HRQoL (10%), and drug selections (10%). The three most frequently applied interventions were drug information/patient counseling (80%), adverse drug reaction monitoring (50%), and drug use evaluation (20%). Based on the World Health Organization (WHO) outcome classification, treatment success ranged from 72% to 93%, with higher cure outcomes (53%-86%) than treatment completion (7%-19%). Other outcomes, including isoniazid metabolites, medication counts, sputum conversion, adherence/compliance, knowledge, and quality of life, were better in the intervention group than those in comparator groups, and/or they improved over time. Risk of bias analysis indicated that the included studies were not comparable to a randomized clinical trial. Conclusion: Clinical pharmacy services as single or composite interventions potentially improve TB outcomes, but its evidence is still inconsistent and limited due to the lack of randomized controlled studies using the WHO outcome classification. Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=199028, identifier CRD42020199028.

Health-economic evaluation of meniscus tear treatments: a systematic review.

PURPOSE: To evaluate the overall evidence of published health-economic evaluation studies on meniscus tear treatment. METHODS: Our systematic review focuses on health-economic evaluation studies of meniscus tear treatment interventions found in PubMed and Embase databases. A qualitative, descriptive approach was used to analyze the studies' results and systematically report them following PRISMA guidelines. The health-economic evaluation method for each included study was categorized following one of the four approaches: partial economic evaluation (PEE), cost-effectiveness analysis (CEA), cost-benefit analysis (CBA), or cost-utility analysis (CUA). The quality of each included study was assessed using the Consensus on Health Economic Criteria (CHEC) list. Comparisons of input variables and outcomes were made, if applicable. RESULTS: Sixteen studies were included; of these, six studies performed PEE, seven studies CUA, two studies CEA, and one study combined CBA, CUA, and CEA. The following economic comparisons were analyzed and showed the respective comparative outcomes: (1) meniscus repair was more cost-effective than arthroscopic partial meniscectomy (meniscectomy) for reparable meniscus tear; (2) non-operative treatment or physical therapy was less costly than meniscectomy for degenerative meniscus tear; (3) physical therapy with delayed meniscectomy was more cost-effective than early meniscectomy for meniscus tear with knee osteoarthritis; (4) meniscectomy without physical therapy was less costly than meniscectomy with physical therapy; (5) meniscectomy was more cost-effective than either meniscus allograft transplantation or meniscus scaffold procedure; (6) the conventional arthroscopic instrument cost was lower than laser-assisted arthroscopy in meniscectomy procedures. CONCLUSION: Results from this review suggest that meniscus repair is the most cost-effective intervention for reparable meniscus tears. Physical therapy followed by delayed meniscectomy is the most cost-effective intervention for degenerative meniscus tears. Meniscus scaffold should be avoided, especially when implemented on a large scale. LEVEL OF EVIDENCE: Systematic review of level IV studies.

Multiclass tumor identification using combined texture and statistical features.

Early detection and diagnosis of brain tumors are essential for early intervention and eventually successful treatment plans leading to either a full recovery or an increase in the patient lifespan. However, diagnosis of brain tumors is not an easy task since it requires highly skilled professionals, making this procedure both costly and time-consuming. The diagnosis process relying on MR images gets even harder in the presence of similar objects in terms of their density, size, and shape. No matter how skilled professionals are, their task is still prone to human error. The main aim of this work is to propose a system that can automatically classify and diagnose glioma brain tumors into one of the four tumor types: (1) necrosis, (2) edema, (3) enhancing, and (4) non-enhancing. In this paper, we propose a combined texture discrete wavelet transform (DWT) and statistical features based on the first- and second-order features for the accurate classification and diagnosis of multiclass glioma tumors. Four well-known classifiers, namely, support vector machines (SVM), random forest (RF), multilayer perceptron (MLP), and naïve Bayes (NB), are used for classification. The BraTS 2018 dataset is used for the experiments, and with the combined DWT and statistical features, the RF classifier achieved the highest average accuracy whether for separated modalities or combined modalities. The highest average accuracy of 89.59% and 90.28% for HGG and LGG, respectively, was reported in this paper. It has also been observed that the proposed method outperforms similar existing methods reported in the extant literature.

Diabetic Retinopathy Detection from Fundus Images of the Eye Using Hybrid Deep Learning Features.

Diabetic Retinopathy (DR) is a medical condition present in patients suffering from long-term diabetes. If a diagnosis is not carried out at an early stage, it can lead to vision impairment. High blood sugar in diabetic patients is the main source of DR. This affects the blood vessels within the retina. Manual detection of DR is a difficult task since it can affect the retina, causing structural changes such as Microaneurysms (MAs), Exudates (EXs), Hemorrhages (HMs), and extra blood vessel growth. In this work, a hybrid technique for the detection and classification of Diabetic Retinopathy in fundus images of the eye is proposed. Transfer learning (TL) is used on pre-trained Convolutional Neural Network (CNN) models to extract features that are combined to generate a hybrid feature vector. This feature vector is passed on to various classifiers for binary and multiclass classification of fundus images. System performance is measured using various metrics and results are compared with recent approaches for DR detection. The proposed method provides significant performance improvement in DR detection for fundus images. For binary classification, the proposed modified method achieved the highest accuracy of 97.8% and 89.29% for multiclass classification.

Glioma Tumors' Classification Using Deep-Neural-Network-Based Features with SVM Classifier.

The complexity of brain tissue requires skillful technicians and expert medical doctors to manually analyze and diagnose Glioma brain tumors using multiple Magnetic Resonance (MR) images with multiple modalities. Unfortunately, manual diagnosis suffers from its lengthy process, as well as elevated cost. With this type of cancerous disease, early detection will increase the chances of suitable medical procedures leading to either a full recovery or the prolongation of the patient's life. This has increased the efforts to automate the detection and diagnosis process without human intervention, allowing the detection of multiple types of tumors from MR images. This research paper proposes a multi-class Glioma tumor classification technique using the proposed deep-learning-based features with the Support Vector Machine (SVM) classifier. A deep convolution neural network is used to extract features of the MR images, which are then fed to an SVM classifier. With the proposed technique, a 96.19% accuracy was achieved for the HGG Glioma type while considering the FLAIR modality and a 95.46% for the LGG Glioma tumor type while considering the T2 modality for the classification of four Glioma classes (Edema, Necrosis, Enhancing, and Non-enhancing). The accuracies achieved using the proposed method were higher than those reported by similar methods in the extant literature using the same BraTS dataset. In addition, the accuracy results obtained in this work are better than those achieved by the GoogleNet and LeNet pre-trained models on the same dataset.

Recent Advances in Classification of Brain Tumor from MR Images - State of the Art Review from 2017 to 2021.

BACKGROUND: The task of identifying a tumor in the brain is a complex problem that requires sophisticated skills and inference mechanisms to accurately locate the tumor region. The complex nature of the brain tissue makes the problem of locating, segmenting, and ultimately classifying Magnetic Resonance (MR) images a complex problem. The aim of this review paper is to consolidate the details of the most relevant and recent approaches proposed in this domain for the binary and multi-class classification of brain tumors using brain MR images. OBJECTIVE: In this review paper, a detailed summary of the latest techniques used for brain MR image feature extraction and classification is presented. A lot of research papers have been published recently with various techniques proposed for identifying an efficient method for the correct recognition and diagnosis of brain MR images. The review paper allows researchers in the field to familiarize themselves with the latest developments and be able to propose novel techniques that have not yet been explored in this research domain. In addition, the review paper will facilitate researchers who are new to machine learning algorithms for brain tumor recognition to understand the basics of the field and pave the way for them to be able to contribute to this vital field of medical research. RESULTS: In this paper, the review is performed for all recently proposed methods for both feature extraction and classification. It also identifies the combination of feature extraction methods and classification methods that, when combined, would be the most efficient technique for the recognition and diagnosis of brain tumor from MR images. In addition, the paper presents the performance metrics, particularly the recognition accuracy, of selected research published between 2017-2021.

Recent Advancements in Fuzzy C-means Based Techniques for Brain MRI Segmentation.

BACKGROUND: Variations of image segmentation techniques, particularly those used for Brain MRI segmentation, vary in complexity from basic standard Fuzzy C-means (FCM) to more complex and enhanced FCM techniques. OBJECTIVE: In this paper, a comprehensive review is presented on all thirteen variations of FCM segmentation techniques. In the review process, the concentration is on the use of FCM segmentation techniques for brain tumors. Brain tumor segmentation is a vital step in the process of automatically diagnosing brain tumors. Unlike segmentation of other types of images, brain tumor segmentation is a very challenging task due to the variations in brain anatomy. The low contrast of brain images further complicates this process. Early diagnosis of brain tumors is indeed beneficial to patients, doctors, and medical providers. RESULTS: FCM segmentation works on images obtained from magnetic resonance imaging (MRI) scanners, requiring minor modifications to hospital operations to early diagnose tumors as most, if not all, hospitals rely on MRI machines for brain imaging. CONCLUSION: In this paper, we critically review and summarize FCM based techniques for brain MRI segmentation.

Brain MR Image Classification for Glioma Tumor detection using Deep Convolutional Neural Network Features.

BACKGROUND: Detection of brain tumor is a complicated task, which requires specialized skills and interpretation techniques. Accurate brain tumor classification and segmentation from MR images provide an essential choice for medical treatments. Different objects within an MR image have similar size, shape, and density, which makes the tumor classification and segmentation even more complex. OBJECTIVE: Classification of the brain MR images into tumorous and non-tumorous using deep features and different classifiers to get higher accuracy. METHODS: In this study, a novel four-step process is proposed; pre-processing for image enhancement and compression, feature extraction using convolutional neural networks (CNN), classification using the multilayer perceptron and finally, tumor segmentation using enhanced fuzzy cmeans method. RESULTS: The system is tested on 65 cases in four modalities consisting of 40,300 MR Images obtained from the BRATS-2015 dataset. These include images of 26 Low-Grade Glioma (LGG) tumor cases and 39 High-Grade Glioma (HGG) tumor cases. The proposed CNN feature-based classification technique outperforms the existing methods by achieving an average accuracy of 98.77% and a noticeable improvement in the segmentation results are measured. CONCLUSION: The proposed method for brain MR image classification to detect Glioma Tumor detection can be adopted as it gives better results with high accuracies.

The transport mechanisms of (222)Rn in soil at Tateishi as an anomaly spot in Japan.

The (222)Rn concentration profiles in soil have been measured at an anomaly spot in Tateishi, Japan. In winter, the concentrations were low and showed a negative gradient with depth, but in other seasons, the concentration had both positive and negative gradients with depth, and dramatically changed by time. On the assumption that there was ventilation in deep layers and with driving forces of wind and temperatures, these phenomena were successfully explained. This finding would contribute to a numerical model for (222)Rn transport in soil.

Molecular systematics and biogeography of the fanged frogs of Southeast Asia.

Our analysis of parts of the mitochondrial ribosomal 12S and 16S genes from 39 populations of Southeast Asian ranid frogs confirms that the fanged frogs are a monophyletic clade. This group, properly called Limnonectes, appears to have arisen in the early Tertiary at a time when free faunal exchange was possible among Southeast Asia, Borneo, Sumatra, Java, and, probably, Sulawesi. Four species groups are tentatively identified within the clade. Part of group 1 includes species related to L. kuhlii that occur in Borneo. Another part of group 1 includes species from Malay Peninsula and Thailand that are related to L. pileata. Species group 2, L. leporina, occurs only in Borneo. Species group 3 is restricted to species distributed in Sulawesi and the Philippines. Species group 4 includes L. blythii and relatives. There is a lack of compatibility between phylogenetic hypotheses generated from molecular and morphological data sets. These differences are related, in large part, to whether some species of Limnonectes have secondarily lost fangs or whether lack of fangs represents the primitive condition.

[Electrophoretic variability in a total of 11 structural sites in murine rodents (Muridae, Rodentia)]. / Variabilité électrophorétique totale à 11 loci structuraux chez les rongeurs muridés (Muridae, Rodentia).

Forty-six rodent species of the Muridae family were submitted to sequential electrophoresis for the study of 11 protein loci, using 12 buffer systems which differed in pH and ionic composition. The complete set of electrophoretic conditions yielded 135 variants of which 68 were detected through a single condition. The twofold increase of revealed variants was essentially limited to intergeneric comparisons because few additional variants within genera were revealed, despite the use of several buffers. These results show that estimation of the degree of genetic differentiation among taxa at a higher level than that of the genus by standard electrophoretic data may, although of current use, lead to erroneous results.