The development of leptomeningeal disease (LMD) among melanoma patients is associated with short survival. Unspecific clinical symptoms and imprecise diagnostic criteria often delay diagnosis. ...Because melanoma patients with LMD have been excluded from most clinical trials, the efficacy of immune checkpoint blockade (ICB) and targeted therapies (TTs) has not been adequately investigated among these patients.
We performed a retrospective study in two tertiary-referral skin cancer centres to evaluate the clinical characteristics, diagnostics, treatments, and overall survival (OS) of melanoma patients with LMD between June 2011 and March 2019.
In total, 52 patients were included. The median age at LMD diagnosis was 58 years. Most patients (n = 30, 58%) were men. The median time from the first diagnosis of unresectable disease to the first diagnosis of LMD was 8.5 months (range 0–91.5 months). Most patients (65%, n = 34) were BRAF V600 mutated. Sixteen patients (31%) presented with LMD only, whereas 36 patients (69%) presented with concomitant brain metastases at LMD diagnosis. Eleven patients (21%) showed no evidence of extracranial disease. Forty-four patients (85%) had clinical symptoms at LMD diagnosis. Forty-two patients (81%) had received at least one prior therapy. Forty patients (77%) received at least one treatment after LMD diagnosis, including TT (n = 17), ICB (n = 13), bevacizumab (n = 1), radiotherapy (n = 3), and intrathecal chemotherapy (n = 1); five patients received both TT and ICB. Twelve patients (23%) received no treatment because of rapid progression of LMD. The median OS for the entire cohort was 2.9 months (95% confidence interval CI 1.7–4.1). Among patients receiving systemic therapy, OS was 3.7 months (95% CI 2.4–4.9).
Systemic treatment with TT or ICB seems to improve OS among patients with LMD. However, despite new therapy modalities, the prognosis of LMD remains poor.
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•Around 20% of the patients with leptomeningeal disease (LMD) received no treatment.•The presence of a BRAF mutation seems to be higher in patients with LMD.•At diagnosis of LMD, most of the patients had concomitant brain metastases.•The prognosis of LMD remains poor with a median overall survival of 2.9 months.
IMPORTANCE: Lactation has been shown to be associated with lower rates of diabetes and hypertension in mothers. However, the strength of association has varied between studies, and sample sizes are ...relatively small. OBJECTIVE: To conduct a systematic review and meta-analysis to determine whether lactation is associated with a lower risk of diabetes and hypertension. DATA SOURCES: Ovid MEDLINE, Ovid Embase, Cochrane CENTRAL, and CINAHL databases were searched from inception to July 2018 with manual search of the references. STUDY SELECTION: Studies of adult women that specified duration of breastfeeding for at least 12 months, evaluated primary hypertension and diabetes as outcomes, were full-text articles in English, and reported statistical outcomes as odds ratios were included. DATA EXTRACTION AND SYNTHESIS: Study characteristics were independently extracted using a standard spreadsheet template and the data were pooled using the random-effects model. The Meta-analysis of Observational Studies in Epidemiology (MOOSE) guideline for reporting was followed. MAIN OUTCOMES AND MEASURES: Diabetes and hypertension. RESULTS: The search yielded 1558 articles, from which a total of 6 studies met inclusion criteria for association between breastfeeding and diabetes and/or hypertension. The 4 studies included in the meta-analysis for the association between lactation and diabetes had a total of 206 204 participants, and the 5 studies included in the meta-analysis for the association between lactation and hypertension had a total of 255 271 participants. Breastfeeding for more than 12 months was associated with a relative risk reduction of 30% for diabetes (pooled odds ratio, 0.70 95% CI, 0.62-0.78; P < .001) and a relative risk reduction of 13% for hypertension (pooled odds ratio, 0.87 95% CI, 0.78-0.97; P = .01). CONCLUSIONS AND RELEVANCE: This study suggests that education about the benefits of breastfeeding for prevention of diabetes and hypertension in women is a low-risk intervention that can be easily included in daily practice and may have a positive impact on cardiovascular outcomes in mothers.
•This paper intends to present a social distance framework based on deep learning.•We used Faster-RCNN for human detection in the images.•The architecture is trained on the top view human data ...set.•Taking advantage of transfer learning, a new trained layer is fused with a pre-trained architecture.•After detection, the pair-wise distance between two people is estimated in an image.
The recent outbreak of the COVID-19 affected millions of people worldwide, yet the rate of infected people is increasing. In order to cope with the global pandemic situation and prevent the spread of the virus, various unprecedented precaution measures are adopted by different countries. One of the crucial practices to prevent the spread of viral infection is social distancing. This paper intends to present a social distance framework based on deep learning architecture as a precautionary step that helps to maintain, monitor, manage, and reduce the physical interaction between individuals in a real-time top view environment. We used Faster-RCNN for human detection in the images. As the human's appearance significantly varies in a top perspective; therefore, the architecture is trained on the top view human data set. Moreover, taking advantage of transfer learning, a new trained layer is fused with a pre-trained architecture. After detection, the pair-wise distance between peoples is estimated in an image using Euclidean distance. The detected bounding box's information is utilized to measure the central point of an individual detected bounding box. A violation threshold is defined that uses distance to pixel information and determines whether two people violate social distance or not. Experiments are conducted using various test images; results demonstrate that the framework effectively monitors the social distance between peoples. The transfer learning technique enhances the overall performance of the framework by achieving an accuracy of 96% with a False Positive Rate of 0.6%.
Global overpopulation, industrial expansion, and urbanization have generated massive amounts of wastes. This is considered as a significant worldwide challenge that requires an urgent solution. ...Additionally, remarkable advances in the field of biomedicine have impacted the entire spectrum of healthcare and medicine. This has paved the way for further refining of the outcomes of biomedical strategies toward early detection and treatment of different diseases. Various nanomaterials (NMs) have been dedicated to different biomedical applications including drug delivery, vaccinations, imaging modalities, and biosensors. However, toxicity is still the main factor restricting their use. NMs recycled from different types of wastes present a pioneering approach to not only avoid hazardous effects on the environment, but to also implement circular economy practices, which are crucial to attain sustainable growth. Moreover, recycled NMs have been utilized as a safe, yet revolutionary alternative with outstanding potential for many biomedical applications. This review focuses on waste recycled NMs, their synthesis, properties, and their potential for multiple biomedical applications with special emphasis on their role in the early detection and control of multiple diseases. Their pivotal therapeutic actions as antimicrobial, anticancer, antioxidant nanodrugs, and vaccines will also be outlined. The ongoing advancements in the design of recycled NMs are expanding their diagnostic and therapeutic roles for diverse biomedical applications in the era of precision medicine.
This study aims to assess the impacts of land use and land cover (LULC) changes on the water quality of the Surma river in Bangladesh. For this, seasonal water quality changes were assessed in ...comparison to the LULC changes recorded from 2010 to 2019. Obtained results from this study indicated that pH, electrical conductivity (EC), and total dissolved solids (TDS) concentrations were higher during the dry season, while dissolved oxygen (DO), 5-day biological oxygen demand (BOD5), temperature, total suspended solids (TSS), and total solids (TS) concentrations also changed with the season. The analysis of LULC changes within 1000-m buffer zones around the sampling stations revealed that agricultural and vegetation classes decreased; while built-up, waterbody and barren lands increased. Correlation analyses showed that BOD5, temperature, EC, TDS, and TSS had a significant relationship (5% level) with LULC types. The regression result indicated that BOD5 was sensitive to changing waterbody (predictors, R2 = 0.645), temperature was sensitive to changing waterbodies and agricultural land (R2 = 0.889); and EC was sensitive to built-up, vegetation, and barren land (R2 = 0.833). Waterbody, built-up, and agricultural LULC were predictors for TDS (R2 = 0.993); and waterbody, built-up, and barren LULC were predictors for TSS (R2 = 0.922). Built-up areas and waterbodies appeared to have the strongest effect on different water quality parameters. Scientific finding from this study will be vital for decision makers in developing more robust land use management plan at the local level.
IoT (Internet of Things) devices and smart sensors are used in different life sectors, including industry, business, surveillance, healthcare, transportation, communication, and many others. These ...IoT devices and sensors produce tons of data that might be valued and beneficial for healthcare organizations if it becomes subject to analysis, which brings big data analytics into the picture. Recently, the novel coronavirus pandemic (COVID-19) outbreak is seriously threatening human health, life, production, social interactions, and international relations. In this situation, the IoT and big data technologies have played an essential role in fighting against the pandemic. The applications might include the rapid collection of big data, visualization of pandemic information, breakdown of the epidemic risk, tracking of confirmed cases, tracking of prevention levels, and adequate assessment of COVID-19 prevention and control. In this paper, we demonstrate a health monitoring framework for the analysis and prediction of COVID-19. The framework takes advantage of Big data analytics and IoT. We perform descriptive, diagnostic, predictive, and prescriptive analysis applying big data analytics using a novel disease real data set, focusing on different pandemic symptoms. This work's key contribution is integrating Big Data Analytics and IoT to analyze and predict a novel disease. The neural network-based model is designed to diagnose and predict the pandemic, which can facilitate medical staff. We predict pandemic using neural networks and also compare the results with other machine learning algorithms. The results reveal that the neural network performs comparatively better with an accuracy rate of 99%.
Image segmentation is considered as a key research topic in the area of computer vision. It is pivotal in a broad range of real-life applications. Recently, the emergence of deep learning drives ...significant advancement in image segmentation; the developed systems are now capable of recognizing, segmenting, and classifying objects of specific interest in images. Generally, most of these techniques primarily focused on the asymmetric field of view or frontal view objects. This work explores widely used deep learning-based models for person segmentation using top view data set. The first model employed in this work is Fully Convolutional Neural Network (FCN) with Resnet-101 architecture. The network consists of a set of max-pooling and convolution layers to identify pixel-wise class labels and prediction of the mask. The second model is based on FCN called U-Net with Encoder-Decoder architecture. The encoder is mainly comprised of a contracting path, also called an encoder, which captures the context in the image and symmetric expanding path called decoder to enable accurate location. The third model used for top view person segmentation is a DeepLabV3 model also with encoder-decoder architecture. The encoder consists of trained Convolutional Neural Network (CNN) to encode feature maps of the input image. The decoder is used for up-sampling and reconstruction of output using important information extracted by the encoder. All segmentation models are firstly tested using pre-trained models (trained on frontal view data set). To improve the performance, these models are further trained using person data set captured from a top view. The output of all models consists of a segmented person in the top view images. The experimental results reveal the effectiveness and performance of segmentation models by achieving <inline-formula> <tex-math notation="LaTeX">IoU </tex-math></inline-formula> of 83%, 84%, and 86% and <inline-formula> <tex-math notation="LaTeX">mIoU </tex-math></inline-formula> of 80% 82% and 84% for FCN, U-Net, and DeepLabv3 respectively. Furthermore, the discussion is provided for output results with possible future guidelines.
Demand for agricultural products is increasing as population continues to grow in Africa. To attain a higher crop yield while preserving the environment, appropriate management of macronutrients ...(i.e., nitrogen (N), phosphorus (P) and potassium (K)) and crops are of critical prominence. This paper aims to review the state of art of the use of remote sensing in soil agricultural applications, especially in monitoring NPK availability for widely grown crops in Africa. In this study, we conducted a substantial literature review of the use of airborne imaging technology (e.g., different platforms and sensors), methods available for processing and analyzing spectral information, and advances of these applications in farming practices by the African scientific community. Here we aimed to identify knowledge gaps in this field and challenges related to the acquisition, processing, and analysis of hyperspectral imagery for soil agriculture investigations. To do so, publications over the past 10 years (i.e., 2008–2021) in hyperspectral imaging technology and applications in monitoring macronutrients status for crops were reviewed. In this study, the imaging platforms and sensors, as well as the different methods of processing encountered across the literature, were investigated and their benefit for NPK assessment were highlighted. Furthermore, we identified and selected particular spectral regions, bands, or features that are most sensitive to describe NPK content (both in crop and soil) that allowed to characterize NPK. In this review, we proposed a hyperspectral data-based research protocol to quantify variability of NPK in soil and crop at the field scale for the sake of optimizing fertilizers application. We believe that this review will contribute promoting the adoption of hyperspectral technology (i.e., imaging and spectroscopy) for the optimization of soil NPK investigation, mapping, and monitoring in many African countries.
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