ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Survival pattern of colorectal cancer in Sub-Saharan Africa: A systematic review and meta-analysis

Cancer incidence is relatively low in sub-Saharan Africa (SSA), however, prognosis is expected to be poor in comparison with high-income countries. Comprehensive evidence is limited on the survival pattern of colorectal cancer patients in the region. We conducted a systematic review and meta-analysis to investigate the pattern of colorectal cancer survival in the region and to identify variation across countries and over time. We searched international databases MEDLINE, Scopus, Embase, Web of Science, ProQuest, CINAHL, and Google Scholar to retrieve studies that estimated survival from colorectal cancer in SSA countries from inception to December 31, 2021 without language restriction. Due to between-study heterogeneity, we performed a random-effects meta-analysis to pool survival rates. To identify study-level sources of variation, we performed subgroup analysis and meta-regression. Results are reported in line with the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) 2020 guideline and the protocol was registered in PROSPERO database (CRD42021246935). 23 studies involving 10,031 patients were included in the review, of which, 20 were included in the meta-analysis. The meta-analysis results showed that the pooled 1-, 2-, 3-, 4-, and 5-year survival rates in SSA were 0.74 (95% CI, 0.66-0.81), 0.50 (95% CI, 0.41-0.58), 0.36 (95% CI, 0.27-0.47), 0.31 (95% CI, 0.22-0.42), and 0.28 (95% CI, 0.19-0.38) respectively. Subgroup analyses indicated that the survival rate varied according to year of study, in which those conducted in recent decades showed relatively better survival. The 5-year survival was higher in middle-income SSA countries (0.31; 95%CI: 0.17-0.49) than low-income countries (0.20; 95%CI: 0.11-0.35), however, the difference was not statistically significant. In conclusion, survival from colorectal cancer is low in sub-Saharan Africa compared to other regions. Thus, intervention strategies to improve screening, early diagnosis and treatment of colorectal cancer should be developed and implemented to improve survival in the region.
3D Neuron Microscopy Image Segmentation via the Ray-Shooting Model and a DC-BLSTM Network

The morphology reconstruction (tracing) of neurons in 3D microscopy images is important to neuroscience research. However, this task remains very challenging because of the low signal-to-noise ratio (SNR) and the discontinued segments of neurite patterns in the images. In this paper, we present a neuronal structure segmentation method based on the ray-shooting model and the Long Short-Term Memory (LSTM)-based network to enhance the weak-signal neuronal structures and remove background noise in 3D neuron microscopy images. Specifically, the ray-shooting model is used to extract the intensity distribution features within a local region of the image. And we design a neural network based on the dual channel bidirectional LSTM (DC-BLSTM) to detect the foreground voxels according to the voxel-intensity features and boundary-response features extracted by multiple ray-shooting models that are generated in the whole image. This way, we transform the 3D image segmentation task into multiple 1D ray/sequence segmentation tasks, which makes it much easier to label the training samples than many existing Convolutional Neural Network (CNN) based 3D neuron image segmentation methods. In the experiments, we evaluate the performance of our method on the challenging 3D neuron images from two datasets, the BigNeuron dataset and the Whole Mouse Brain Sub-image (WMBS) dataset. Compared with the neuron tracing results on the segmented images produced by other state-of-the-art neuron segmentation methods, our method improves the distance scores by about 32% and 27% in the BigNeuron dataset, and about 38% and 27% in the WMBS dataset.
MsTGANet: Automatic Drusen Segmentation From Retinal OCT Images

Drusen is considered as the landmark for diagnosis of AMD and important risk factor for the development of AMD. Therefore, accurate segmentation of drusen in retinal OCT images is crucial for early diagnosis of AMD. However, drusen segmentation in retinal OCT images is still very challenging due to the large variations in size and shape of drusen, blurred boundaries, and speckle noise interference. Moreover, the lack of OCT dataset with pixel-level annotation is also a vital factor hindering the improvement of drusen segmentation accuracy. To solve these problems, a novel multi-scale transformer global attention network (MsTGANet) is proposed for drusen segmentation in retinal OCT images. In MsTGANet, which is based on U-Shape architecture, a novel multi-scale transformer non-local (MsTNL) module is designed and inserted into the top of encoder path, aiming at capturing multi-scale non-local features with long-range dependencies from different layers of encoder. Meanwhile, a novel multi-semantic global channel and spatial joint attention module (MsGCS) between encoder and decoder is proposed to guide the model to fuse different semantic features, thereby improving the model's ability to learn multi-semantic global contextual information. Furthermore, to alleviate the shortage of labeled data, we propose a novel semi-supervised version of MsTGANet (Semi-MsTGANet) based on pseudo-labeled data augmentation strategy, which can leverage a large amount of unlabeled data to further improve the segmentation performance. Finally, comprehensive experiments are conducted to evaluate the performance of the proposed MsTGANet and Semi-MsTGANet. The experimental results show that our proposed methods achieve better segmentation accuracy than other state-of-the-art CNN-based methods.
Comparative Appraisal of Leaf Proteomic and Mass Spectrometry Analyses During Fusarium Wilt Infection in Resistance and Susceptible Genotypes of Castor (Ricinus communis L.)

The resistant and susceptible genotypes of castor were utilized for leaf proteomic study during Fusarium wilt infection. The histopathological study was observed under SEM and it confirmed that the infection of Fusarium oxysporum f. sp. ricini was higher in the root of susceptible JI-35, while incompatible interaction is observed in resistant SKI-215 genotype. The acidic and neutral proteins were maximally up-expressed with 2 to 171 kDa in treated resistant and 2 to 150 kDa in treated susceptible interactions. In resistant genotype, the leaf proteins were recognized with 3.0- and 5.8-fold higher at infection stage and post-infection stage, respectively, as compared to susceptible genotype. The highly up expressions of leaf acidic (4.76 pI) and basic (8.77 pI) proteins were found with 224.94- and 61.68-fold change, respectively during the post-infection stage in treated resistance compared to its control. The protein spots at 4.76 pI and 8.77 pI were characterized with nanoLC-MS Triple TOF and were recognized as signalling molecules small GTP binding protein (23 kDa) and actin (8 kDa), respectively, on the basis of mass spectrometry and peptide sequences. However, basic and neutral proteins were up regulated as 30.11- and 20.30-fold changes in treated susceptible compared to its control. These proteins were identified as HSP90 (10 kDa) and LEA (27 kDa) proteins. The 148 kDa protein is recognized as histidine kinase in incompatible resistant interaction compared to compatible susceptible (serine threonine protein kinase, 65 kDa) as common acidic protein at 3.80 pI during infection stage. Some acidic proteins were maximally up-regulated in the leaf of resistant castor genotype and played a significant role in defense response.
The relationship between substance use and physical activity among people living with HIV, chronic pain, and symptoms of depression: a cross-sectional analysis

ABSTRACTChronic pain, depression, and substance use are common among people living with HIV (PLWH). Physical activity can improve pain and mental health. Some substances such as cannabis may alleviate pain, which may allow PLWH to participate in more physical activity. However, risks of substance use include poorer mental health and HIV clinical outcomes. This cross-sectional analysis examined the relationships of self-reported substance use (alcohol, cannabis, and nicotine use), gender, and age with self-reports of walking, moderate physical activity, and vigorous physical activity, converted to Metabolic Equivalent of Task Units (METs), among 187 adults living with HIV, chronic pain, and depressive symptoms in the United States. Women reported less walking, vigorous activity, and total physical activity compared to men. Individuals who used cannabis reported more vigorous physical activity relative to those who did not use cannabis. These findings were partially accounted for by substance use*gender interactions: men using cannabis reported more vigorous activity than all other groups, and women with alcohol use reported less walking than men with and without alcohol use. Research is needed to increase physical activity among women who use substances and to evaluate reasons for the relationship between substance use and physical activity among men.
Allergic bronchopulmonary aspergillosis in association with rheumatic heart disease: report of three cases

Allergic bronchopulmonary aspergillosis is usually seen in patients with asthma or cystic fibrosis. Its association with rheumatic heart disease has not been adequately reported in literature. We report our experience of three cases who were diagnosed cases of rheumatic heart disease. Their symptomatology and clinical findings required further evaluation and investigations, which were suggestive of allergic bronchopulmonary aspergillosis. The patients were treated with steroids and/or antifungals before proceeding with the valve replacement.
A Study on the Public Landscape Order of Xinye Village

In the modernization process since China's reform and liberalization, urban and village space design is reflected in the characteristics of Western cultures. The idea of Western space design has a profound influence on China, but the piecemeal individuation of art design, the disorderly public art modeling and concept, not only interferes with the aesthetic sense of urban and village public space itself, but also seriously affected the landscape order of public space. In fact, Chinese traditional settlement landscape excels in abundant landscape design and spatial sequence. This paper, using the methods of literature discussion, field research and spatial analysis, takes the typical traditional landscape settlement Xinye Village (???) in the south of the Yangtze River as an example, and explores its public landscape order as a whole, and finds its spatial structure based on the Five Elements and Nine Divisions (????) cultural schemata. In the process of development, it has experienced the competition of public space, thus forming a stable and sustainable spatial order form. The purpose is to explore the cultural schema of the public landscape from the traditional Chinese settlement, and to put forward the possibility of constructing the public landscape order based on culture in future urban and village landscape design.
Feasibility of Delivering High-Dose Methotrexate in Adolescent and Adult All Patients: A Retrospective Study

Introduction HD-MTX is a key drug in the treatment protocols for ALL. The regimen needs to be administered with appropriate supportive measures and serum methotrexate level monitoring. A limited testing strategy is relevant in resource constraint settings since it allows a shorter duration of hospitalization. We report our experience with this strategy and its impact on the patient safety outcomes. Methods This is a retrospective study of all patients ≥ 15 years of age with newly diagnosed ALL or Lymphoblastic lymphoma (LBL) who were administered HDMTX (part of BFM-90 ALL protocol) at our institute between March 2013 to November 2013.The medical records were reviewed for clinical characteristics, disease-related details, HDMTX dose and cycles administered, leucovorin rescue and toxicities. Results A total of 423 cycles of HD-MTX were administered to 106 patients during the study period. The median duration for completion of all 4 cycles of HDMTX was 53 (IQR 49-60) days. The grade 3 or higher toxicities were anemia in 9.6%, neutropenia 19.4%, febrile neutropenia 5.7%, thrombocytopenia 4.4% and mucositis in 0.7%. There was statistically significant correlation between the levels at 42 h (≤ 1 mmol/L vs > 1 mmol/L) and toxicity- anemia, FN and mucositis observed more in the late clearance group. With limited sampling strategy whereby if the 42- hour level MTX level are < 1 mmol/L, 57% of patients could be discharged early. Conclusion HD-MTX can be safely administered to adolescent and adult ALL patients. A limited methotrexate level monitoring is a safe strategy that can optimize the resources better.
Monkeypox after Occupational Needlestick Injury from Pustule

We report a case of monkeypox in a physician after an occupational needlestick injury from a pustule. This case highlights risk for occupational transmission and manifestations of the disease after percutaneous transmission: a short incubation period, followed by a solitary lesion at the injured site and later by systemic symptoms.
Editorial: Whither globalization and health in an era of geopolitical uncertainty?

Globalization has been declared dead or dying for many years, although recently, the number of voices declaring it 'over' has swelled [1]. As editors of a journal interrogating how globalization affects health, we confront the question: Have the COVID-19 pandemic, Russia's war against Ukraine, a breakdown in multilateralism, and the risk of a return to the stagflation of the 1970s finally sounded a death knell for the research and scholarship we have been publishing in the journal's 20-year history? We think not and argue below why, in our post-pandemic fractured and fractious era, it is vitally important to retain a focus on this messy construct short-handed as 'globalization.'
Learning Structured Models for Segmentation of 2-D and 3-D Imagery

Efficient and accurate segmentation of cellular structures in microscopic data is an essential task in medical imaging. Many state-of-the-art approaches to image segmentation use structured models whose parameters must be carefully chosen for optimal performance. A popular choice is to learn them using a large-margin framework and more specifically structured support vector machines (SSVM). Although SSVMs are appealing, they suffer from certain limitations. First, they are restricted in practice to linear kernels because the more powerful nonlinear kernels cause the learning to become prohibitively expensive. Second, they require iteratively finding the most violated constraints, which is often intractable for the loopy graphical models used in image segmentation. This requires approximation that can lead to reduced quality of learning. In this paper, we propose three novel techniques to overcome these limitations. We first introduce a method to "kernelize" the features so that a linear SSVM framework can leverage the power of nonlinear kernels without incurring much additional computational cost. Moreover, we employ a working set of constraints to increase the reliability of approximate subgradient methods and introduce a new way to select a suitable step size at each iteration. We demonstrate the strength of our approach on both 2-D and 3-D electron microscopic (EM) image data and show consistent performance improvement over state-of-the-art approaches.
The Low Area Probing Detector as a Countermeasure Against Invasive Attacks

Microprobing allows intercepting data from on-chip wires as well as injecting faults into data or control lines. This makes it a commonly used attack technique against security-related semiconductors, such as smart card controllers. We present the low area probing detector (LAPD) as an efficient approach to detect microprobing. It compares delay differences between symmetric lines such as bus lines to detect timing asymmetries introduced by the capacitive load of a probe. Compared with state-of-the-art microprobing countermeasures from industry, such as shields or bus encryption, the area overhead is minimal and no delays are introduced; in contrast to probing detection schemes from academia, such as the probe attempt detector, no analog circuitry is needed. We show the Monte Carlo simulation results of mismatch variations as well as process, voltage, and temperature corners on a 65-nm technology and present a simple reliability optimization. Eventually, we show that the detection of state-of-the-art commercial microprobes is possible even under extreme conditions and the margin with respect to false positives is sufficient.
A new joint CTC-attention-based speech recognition model with multi-level multi-head attention

A method called joint connectionist temporal classification (CTC)-attention-based speech recognition has recently received increasing focus and has achieved impressive performance. A hybrid end-to-end architecture that adds an extra CTC loss to the attention-based model could force extra restrictions on alignments. To explore better the end-to-end models, we propose improvements to the feature extraction and attention mechanism. First, we introduce a joint model trained with nonnegative matrix factorization (NMF)-based high-level features. Then, we put forward a hybrid attention mechanism by incorporating multi-head attentions and calculating attention scores over multi-level outputs. Experiments on TIMIT indicate that the new method achieves state-of-the-art performance with our best model. Experiments on WSJ show that our method exhibits a word error rate (WER) that is only 0.2% worse in absolute value than the best referenced method, which is trained on a much larger dataset, and it beats all present end-to-end methods. Further experiments on LibriSpeech show that our method is also comparable to the state-of-the-art end-to-end system in WER.
Multi-Level Optimization of an Ultra-Low Power BrainWave System for Non-Convulsive Seizure Detection

We present a systematic evaluation and optimization of a complex bio-medical signal processing application on the BrainWave prototype system, targeted towards ambulatory EEG monitoring within a tiny power budget of <1 mW. The considered BrainWave processor is completely programmable, while maintaining energy-efficiency by means of a Coarse-Grained Reconfigurable Array (CGRA). This is demonstrated through the mapping and evaluation of a state-of-the-art non-convulsive epileptic seizure detection algorithm, while ensuring real-time operation and seizure detection accuracy. Exploiting the CGRA leads to an energy reduction of 73.1%, compared to a highly tuned software implementation (SW-only). A total of 9 complex kernels were benchmarked on the CGRA, resulting in an average 4.7 x speedup and average 4.4 x energy savings over highly tuned SW-only implementations. The BrainWave processor is implemented in 28-nm FDSOI technology with 80 kB of Foundry-provided SRAM. By exploiting near-threshold computing for the logic and voltage-stacking to minimize on-chip voltage-conversion overhead, additional 15.2% and 19.5% energy savings are obtained, respectively. At the Minimum-Energy-Point (MEP) (223 mu W, 8 MHz) we report a measured state-of-the-art 90.6% system conversion efficiency, while executing the epileptic seizure detection in real-time.
A Similarity Metric for the Inputs of OO Programs and Its Application in Adaptive Random Testing

Random testing (RT) has been identified as one of the most popular testing techniques, due to its simplicity and ease of automation. Adaptive random testing (ART) has been proposed as an enhancement to RT, improving its fault-detection effectiveness by evenly spreading random test inputs across the input domain. To achieve the even spreading, ART makes use of distance measurements between consecutive inputs. However, due to the nature of object-oriented software (OOS), its distance measurement can be particularly challenging: Each input may involve multiple classes, and interaction of objects through method invocations. Two previous studies have reported on how to test OOS at a single-class level using ART. In this study, we propose a new similarity metric to enable multiclass level testing using ART. When generating test inputs (for multiple classes, a series of objects, and a sequence of method invocations), we use the similarity metric to calculate the distance between two series of objects, and between two sequences of method invocations. We integrate this metric with ART and apply it to a set of open-source OO programs, with the empirical results showing that our approach outperforms other RT and ART approaches in OOS testing.
Arylboration of Enecarbamates for the Synthesis of Borylated Saturated N-Heterocycles

Two catalytic systems have been developed for the arylboration of endocyclic enecarbamates to deliver synthetically versatile borylated saturated N-heterocycles in good regio- and diastereoselectivities. A Cu/Pd dual catalytic reaction enables the synthesis of borylated, α-arylated azetidines, while a Ni-catalysed arylboration reaction efficiently functionalizes 5-, 6-, and 7-membered enecarbamates. In the case of the Cu/Pd-system, a remarkable additive effect was identified that allowed for broader scope. The products are synthetically useful, as demonstrated by manipulations of the boronic ester to access biologically active compounds.
Molecular Dynamic Simulations and Molecular Docking as a Potential Way for Designed New Inhibitor Drug without Resistance

Mycobacterium tuberculosis is the cause of tuberculosis in humans and is responsible for more than 2 million deaths per year. Despite the development of anti-tuberculosis drugs (Isoniazid, Rifampicin, Ethambutol, pyrazinamide, streptomycin, etc.) and the TB vaccine, this disease has claimed the lives of many people around the world. Drug resistance in this disease is increasing day by day. Conventional methods for discovering and developing drugs are usually time-consuming and expensive. Therefore, a better method is needed to identify, design, and manufacture TB drugs without drug resistance. Bioinformatics applications in obtaining new drugs at the structural level include studies of the mechanism of drug resistance, detection of drug interactions, and prediction of mutant protein structure. In the present study, computer-based approaches including molecular dynamics simulation and molecular docking as a novel and efficient method for the identification and investigation of new cases as well as the investigation of mutated proteins and compounds will be examined .
Novelty in Impact of Neurorehabilitation in a Classic Case of Syringomyelia

A fluid-filled hole inside the parenchyma or central canal of the spinal cord causes syringomyelia, a neurological condition. It is most frequently linked to type 1 Chiari malformations. Syringomyelia can be caused by tumors in the spinal cord, trauma, and post-traumatic or infectious adhesive arachnoiditis. Syringomyelia is shown to have a prevalence of 8.4/100,000 to 0.9/10,000 in certain studies, making it one of the few unusual cases. A large proportion of patients are between 20 and 50 years of age. In our case, the patient is a 17-year-old boy who complained of tingling and weakness in both lower extremities, as well as loss of sensation in both hands. MRI of his spine revealed a Chiari I malformation involving evidence of medulla, fourth ventricle, and cerebellar vermis displacement into the foramen magnum. Arnold Chiari's malformation with cord syringomyelia and tonsillar herniation was diagnosed based on the symptoms and investigation findings. The goal of this case is to highlight the benefits of exercise treatment in improving the patient's quality of life, as physiotherapy protocol instillation is not practiced on a daily basis for such conditions.