ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
EMLI-ICC: an ensemble machine learning-based integration algorithm for metastasis prediction and risk stratification in intrahepatic cholangiocarcinoma

Robust strategies to identify patients at high risk for tumor metastasis, such as those frequently observed in intrahepatic cholangiocarcinoma (ICC), remain limited. While gene/protein expression profiling holds great potential as an approach to cancer diagnosis and prognosis, previously developed protocols using multiple diagnostic signatures for expression-based metastasis prediction have not been widely applied successfully because batch effects and different data types greatly decreased the predictive performance of gene/protein expression profile-based signatures in interlaboratory and data type dependent validation. To address this problem and assist in more precise diagnosis, we performed a genome-wide integrative proteome and transcriptome analysis and developed an ensemble machine learning-based integration algorithm for metastasis prediction (EMLI-Metastasis) and risk stratification (EMLI-Prognosis) in ICC. Based on massive proteome (216) and transcriptome (244) data sets, 132 feature (biomarker) genes were selected and used to train the EMLI-Metastasis algorithm. To accurately detect the metastasis of ICC patients, we developed a weighted ensemble machine learning method based on k-Top Scoring Pairs (k-TSP) method. This approach generates a metastasis classifier for each bootstrap aggregating training data set. Ten binary expression rank-based classifiers were generated for detection of metastasis separately. To further improve the accuracy of the method, the 10 binary metastasis classifiers were combined by weighted voting based on the score from the prediction results of each classifier. The prediction accuracy of the EMLI-Metastasis algorithm achieved 97.1% and 85.0% in proteome and transcriptome datasets, respectively. Among the 132 feature genes, 21 gene-pair signatures were developed to establish a metastasis-related prognosis risk-stratification model in ICC (EMLI-Prognosis). Based on EMLI-Prognosis algorithm, patients in the high-risk group had significantly dismal overall survival relative to the low-risk group in the clinical cohort (P-value < 0.05). Taken together, the EMLI-ICC algorithm provides a powerful and robust means for accurate metastasis prediction and risk stratification across proteome and transcriptome data types that is superior to currently used clinicopathological features in patients with ICC. Our developed algorithm could have profound implications not just in improved clinical care in cancer metastasis risk prediction, but also more broadly in machine-learning-based multi-cohort diagnosis method development. To make the EMLI-ICC algorithm easily accessible for clinical application, we established a web-based server for metastasis risk prediction (http://ibi.zju.edu.cn/EMLI/).
State of the art of numerical modeling for induction processes

Purpose - To provide a selective bibliography for researchers and graduate students who have an interest in induction processes applied to the electromagnetic processing of materials. Design/methodology/approach - The objective is to provide references that identify seminal, early work, and references that represent the current state of the art. References are listed in categories that cover the broad range of induction modeling and application issues. Findings - A brief over-view of the key areas in induction processing of materials is provided, but greater emphasis and space is devoted to the references provided. Research limitations/implications - The middle years of each topic area are not covered. Practical implications - A very comprehensive coverage of material is provided to those with an interest in induction processing of materials. Originality/value - This paper fulfils an identified information/resources need.
A Spatiotemporal Deep Learning Approach for Automatic Pathological Gait Classification

Human motion analysis provides useful information for the diagnosis and recovery assessment of people suffering from pathologies, such as those affecting the way of walking, i.e., gait. With recent developments in deep learning, state-of-the-art performance can now be achieved using a single 2D-RGB-camera-based gait analysis system, offering an objective assessment of gait-related pathologies. Such systems provide a valuable complement/alternative to the current standard practice of subjective assessment. Most 2D-RGB-camera-based gait analysis approaches rely on compact gait representations, such as the gait energy image, which summarize the characteristics of a walking sequence into one single image. However, such compact representations do not fully capture the temporal information and dependencies between successive gait movements. This limitation is addressed by proposing a spatiotemporal deep learning approach that uses a selection of key frames to represent a gait cycle. Convolutional and recurrent deep neural networks were combined, processing each gait cycle as a collection of silhouette key frames, allowing the system to learn temporal patterns among the spatial features extracted at individual time instants. Trained with gait sequences from the GAIT-IT dataset, the proposed system is able to improve gait pathology classification accuracy, outperforming state-of-the-art solutions and achieving improved generalization on cross-dataset tests.
Natech risk and management: an assessment of the state of the art

The present state-of-the-art for natech risk and management is discussed. Examples of recent natechs include catastrophic oil spills associated with Hurricane Katrina and hazardous chemical releases in Europe during the heavy floods of 2002. Natechs create difficult challenges for emergency responders due to the geographical extent of the natural disaster, the likelihood of simultaneous releases, emergency personnel being preoccupied with response to the natural disaster, mitigation measures failing due to the effects of the natural disaster, and others. Recovery from natechs may be much more difficult than for "normal" chemical accidents, as the economic and social conditions of the industrial facility and the surrounding community may have been drastically altered by the natural disaster. Potential safeguards against natechs include adoption of stricter design criteria, chemical process safeguards, community land use planning, disaster mitigation and response planning, and sustainable industrial processes, but these safeguards are only sporadically applied. Ultimately, the public must engage in a comprehensive discussion of acceptable risks for natechs.
Thinking the Future of Agricultural Worker Health on a Warming Planet and an Automating Farm

Over the last 20 years, earth's increasing surface temperature has dramatically altered local climates and risks associated with agricultural work. In parallel, increasing automation has continued to be a hallmark of innovation in agriculture, promising to lower the economic and health externalities of labor in food production by reducing worker demand and hazardous exposure. However, many of these automations neither eliminate labor nor ameliorate climate change pressures on farms. As a result of the confluence between automation and environmental change, empirical studies into the social determinants of agricultural health and safety in rapidly automating industries impacted by local effects of climate change are essential for a responsive agricultural health and safety science. In this commentary, I suggest that looking outside of our disciplinary boundaries to the lessons learned from rural studies (RS), environmental social science (ESS), and science and technology studies (STS) can lend useful theoretical framing for the development of new research trajectories in the areas of automation and climate change as they impact agricultural health and safety.
Baseline water quality of the Gold Coast Broadwater, southern Moreton Bay (Australia)

This study establishes baseline water quality characteristics for the Gold Coast Broadwater, southern Moreton Bay (Australia) utilising routinely monitored parameters between 2016 and 2021, across 18 sites. Combined site mean concentrations of NOx-N, NH3-N and total nitrogen were 11.4 ± 33.4 μg/L, 12.7 ± 27.2 μg/L, and 169 ± 109 μg/L, respectively, whilst PO4-P and total phosphorous were 7.30 ± 5.10 μg/L and 21.7 ± 14.1 μg/L. Additionally, total suspended solids and turbidity combined site means were 6.6 ± 6.0 mg/L and 3.4 ± 2.9 NTU, respectively. During high rainfall periods nutrient concentrations increased by up to >200-, >150-, 15-, 12- and >12-fold for NOx-N, NH3-N, TN, PO4-P and TP, respectively, compared to quiescent conditions. Furthermore, TSS and NTU values increased by up to 15- and 40-fold during periods of measured rainfall compared to quiescent conditions.
Design and Characterization of Hyperpolarized 15N-BBCP as a H2O2-Sensing Probe

Hydrogen peroxide (H2O2) is a type of reactive oxygen species that regulates essential biological processes. Despite the central role of H2O2 in pathophysiological states, available molecular probes for assessing H2O2 in vivo are still limited. This work develops hyperpolarized 15N-boronobenzyl-4-cyanopyridinium (15N-BBCP) as a rationally designed molecular probe for detecting H2O2. The 15N-BBCP demonstrated favorable physicochemical and biochemical properties for H2O2 detection and dynamic nuclear polarization, allowing noninvasive detection of H2O2. In particular, 15N-BBCP and the products possessed long spin-lattice relaxation times and spectrally resolvable 15N chemical shift differences. The performance of hyperpolarized 15N-BBCP was demonstrated both in vitro and in vivo with time-resolved 15N-MRS. This study highlights a promising approach to designing a reaction-based 15N-labeled molecular imaging agent for detecting oxidative stress in vivo.
Brain-wide connectivity map of mouse thermosensory cortices

In the thermal system, skin cooling is represented in the primary somatosensory cortex (S1) and the posterior insular cortex (pIC). Whether S1 and pIC are nodes in anatomically separate or overlapping thermal sensorimotor pathways is unclear, as the brain-wide connectivity of the thermal system has not been mapped. We address this using functionally targeted, dual injections of anterograde viruses or retrograde tracers into the forelimb representation of S1 (fS1) and pIC (fpIC). Our data show that inputs to fS1 and fpIC originate from separate neuronal populations, supporting the existence of parallel input pathways. Outputs from fS1 and fpIC are more widespread than their inputs, sharing a number of cortical and subcortical targets. While, axonal projections were separable, they were more overlapping than the clusters of input cells. In both fS1 and fpIC circuits, there was a high degree of reciprocal connectivity with thalamic and cortical regions, but unidirectional output to the midbrain and hindbrain. Notably, fpIC showed connectivity with regions associated with thermal processing. Together, these data indicate that cutaneous thermal information is routed to the cortex via parallel circuits and is forwarded to overlapping downstream regions for the binding of somatosensory percepts and integration with ongoing behavior.
Does environmental, social, and governance performance mitigate earnings management practices? Evidence from US commercial banks

Environmental, social, and governance (ESG) performance has attracted debates of regulatory bodies and the academic community. Previous studies highlighted the relationship between corporate social responsibility (CSR) disclosure index and earnings management (EM) for non-financial firms. In this paper, we examine the relationship between the ESG performance and EM practices for a sample of US commercial banks over the period 2010-2019. We use two proxies for earnings management: abnormal loan loss provisions (ALLP) and EM to meet the threshold of reporting small positive profit or avoiding losses (SPOS). Consistent with the transparent financial reporting hypothesis, we find that banks reporting higher ESG performance are less likely engaged in income-increasing practice through ALLP. However, no evidence supports that ESG score mitigates EM through loss avoidance. Furthermore, we disaggregate the ESG score into its main three components: environmental, social, and governance. Our findings show that the governance pillar effectively mitigates EM practice under its two proxies. Specifically, the social pillar also seems to be an efficient constraint of banks' EM through income-increasing abnormal loan loss provisions and loss avoidance activity. However, no supporting evidence of a mitigating role for the environmental pillar is provided. Taken together, our results show that, except the environmental pillar, ESG performance score acts as an efficient mitigating tool for EM practices for US banks. Our findings provide a better understanding of banks' earnings management practices. Our findings are helpful for managers when undertaking long-term investment strategies in ESG reporting practices, regulators when issuing new standards, and banks' stakeholders when assessing both the financial and non-financial performance of such entities.
A Case of Pulmonary Nodular Schistosomiasis

Bilharzia is a parasitic infection particularly affecting the digestive tract and urinary tract. Lung involvement is rarely reported. We report a case of pulmonary bilharzioma of nodular type surrounded by ground glass opacities diagnosed on CT-scan and associated with a hepatic nodule, in a 41-year-old woman. The disappearance of the pulmonary nodule under antischistosomal treatment made it possible to make the diagnosis a posteriori without going through an invasive process.
Four decades of experience with carcinoid heart disease: An analysis of 84 patients

Carcinoid heart disease (CHD) is a serious cardiac condition which is caused by elevated serotonin in the systemic circulation, secreted by neuroendocrine tumours (NET). It mostly affects the right-sided heart valves, where it causes fibrotic disturbances and is associated with worse survival. In this study, we describe a large cohort of patients with CHD and provide an insight into their survival over the past decades. All consecutive patients with a serotonin producing NET and CHD referred to the Netherlands Cancer Institute that presented with CHD or developed CHD during their follow up time were included from 1984 until 2021. Patients were divided into three time periods: 1984-2000, 2000-2010 and 2010-2018. Median N-terminal pro B-type natriuretic protein (NT-proBNP) and serum serotonin levels were stratified according to tricuspid regurgitation severity. Kaplan-Meier curves and log rank test were used for visualisation of survival. Cox regression was used for identification of the characteristics associated with disease specific mortality (DSM). A total of 84 patients with CHD were included of whom 49 (58.3%) were male. Median age at NET diagnosis was 62.3 (range 23.9-81.7) years, and median time to development of CHD was 1.1 (range 0-24.2) years. NT-proBNP was significantly higher when more severe tricuspid regurgitation (TR) was present (p = .027). Median survival from CHD diagnosis for 1984-2000, 2000-2010 and 2010-2018 were 1.3 (confidence interval [CI]: 0.9-1.6), 1.9 (CI: 1.2-2.6) and 3.9 (CI: 1.7-6.2) years (p = .025). Valve replacement surgery (VSR) occurred more frequent in later time periods. VSR (hazard ratio [HR] 0.33, p = .005) and NT-proBNP (HR 1.003, 1.00-1.005, p = .036) were significantly associated with DSM. The prognosis of patients with CHD has improved over the past decades, possibly caused by more VSR. NT-proBNP is a valuable biomarker in patients with CHD. Clinical practice should be aimed at timely diagnosis and intervention of CHD.
Severe hypertriglyceridemia in an infant on chronic hemodialysis

Severe hyperlipidemia is a risk factor for cardiovascular disease. Children with chronic kidney disease and end stage renal disease are at risk for development of hyperlipidemia. In this report, we describe a 7-month-old male infant with Denys-Drash syndrome who was found to have a "milky-layer" floating on the deaerator of the hemodialysis machine. Investigations showed severe hypertriglyceridemia of >1000 mg/dl. The patient had been on chronic continuous manual peritoneal dialysis until 6 months of age and recently had been switched to hemodialysis. Management included lowering of caloric intake and addition of medium chain triglyceride with reduction of the serum triglyceride levels to 300-400 mg/dl. Close monitoring of serum lipids and timely intervention is important to prevent serious complications associated with dyslipidemia. Observation of the "milky layer" in the deaerator of the hemodialysis machine may be an interesting visual clue of underlying severe hypertriglyceridemia.
Multifocal cutaneous tuberculosis coexisting with pulmonary tuberculosis

Tuberculosis (TB) is caused by Mycobacterium tuberculosis and it can affect multiple organ systems. Cutaneous TB, a less common type of extrapulmonary TB can coexist with TB of other organs. Here, we describe a case of multifocal cutaneous TB suggestive of two different morphological types with concomitant miliary pulmonary TB.
Multi-Exponential Relaxometry Using l(1)-Regularized Iterative NNLS (MERLIN) With Application to Myelin Water Fraction Imaging

A new parameter estimation algorithm, MERLIN, is presented for accurate and robust multi-exponential relaxometry using magnetic resonance imaging, a tool that can provide valuable insight into the tissue microstructure of the brain. Multi-exponential relaxometry is used to analyze the myelin water fraction and can help to detect related diseases. However, the underlying problem is ill-conditioned, and as such, is extremely sensitive to noise and measurement imperfections, which can lead to less precise and more biased parameter estimates. MERLIN is a fully automated, multi-voxel approach that incorporates state-of-the-art $\ell _{1}$ -regularization to enforce sparsity and spatial consistency of the estimated distributions. The proposed method is validated in simulations and in vivo experiments, using a multi-echo gradient-echo (MEGE) sequence at 3 T. MERLIN is compared to the conventional single-voxel $\ell _{2}$ -regularized NNLS (rNNLS) and a multi-voxel extension with spatial priors (rNNLS SP), where it consistently showed lower root mean squared errors of up to 70 percent for all parameters of interest in these simulations.
A Kernel Classification Framework for Metric Learning

Learning a distance metric from the given training samples plays a crucial role in many machine learning tasks, and various models and optimization algorithms have been proposed in the past decade. In this paper, we generalize several state-of-the-art metric learning methods, such as large margin nearest neighbor (LNINN) and information theoretic metric learning (ITNII,), into a kernel classification framework. First, doublets and triplets are constructed from the training samples, and a family of degree-2 polynomial kernel functions is proposed for pairs of doublets or triplets. Then, a kernel classification framework is established to generalize many popular metric learning methods such as LNINN and ITML. The proposed framework can also suggest new metric learning methods, which can he efficiently implemented, interestingly, using the standard support vector machine (SVNI) solvers. Two novel metric learning methods, namely, doublet-SVM and triplet-SVM, are then developed under the proposed framework. Experimental results show that doublet-SVM and triplet-SVM achieve competitive classification accuracies with state-of-the-art metric learning methods but with significantly less training time.
Incidence and risk factors for anxiety disorders in young adults: A population-based prospective cohort study

Anxiety disorders are among the most common psychiatric disorders in the general population. Our objective was to describe the cumulative incidence and risk factors of anxiety disorders, including obsessive-compulsive disorder (OCD) and posttraumatic stress disorder (PTSD), in a follow-up of young adults over a five-year period. This is a prospective cohort conducted in two waves. The first took place from 2007 to 2009, in which 1,560 young adults aged between 18 and 24 years were evaluated using the Mini-International Neuropsychiatric Interview (MINI). Subjects were invited to participate in the second wave, which wave took place from 2012 to 2014, where 1,244 young adults were evaluated using the MINI-Plus. Our findings showed a cumulative incidence of 10.9% for any anxiety disorder, 6.5% for generalized anxiety disorder, 6.0% for agoraphobia, 2.0% for OCD, 1.6% for panic disorder, 1.1% for social anxiety and 0.7% for PTSD. Being female and having had a depressive episode were risk factors to develop any anxiety disorder. We observed a high cumulative incidence of anxiety disorders in a population-based sample of young adults. Our data highlights the importance of the early identification of these disorders as this could lead to early illness detection, early illness management and a reduced burden of disease.
Game-Based Virtual Reality System for Upper Limb Rehabilitation After Stroke in a Clinical Environment: Systematic Review and Meta-Analysis

The use of virtual reality (VR) for stroke rehabilitation has been implemented during the last decade. At present, most studies still focus on the effects of VR on upper limb rehabilitation, and few studies have explored VR games, VR system designs, and rehabilitation modes for upper limb rehabilitation. This study aims to (1) evaluate the rehabilitation effect of stroke patients using a game-based VR upper limb rehabilitation system in clinical settings; (2) investigate the impact of custom and commercial VR games on patients in clinical settings; and (3) review VR upper limb rehabilitation modes. The PubMed, ScienceDirect, Scopus, Web of Science, and IEEE Xplore databases were searched, and related literature published through December 2021 was included. A total of 4700 articles were retrieved according to the search strategy. We identified 24 studies, including 793 patients. We conducted a systematic search for randomized controlled trials with adult stroke patients to analyze the effect of game-based VR upper limb rehabilitation systems. A meta-analysis was conducted to compare the effects of upper limb function, hand dexterity, daily living ability, and cognitive function between the experimental group (EG, using VR for upper limb rehabilitation) and control group (CG, receiving conventional rehabilitation, including physical therapy and occupational therapy). We also conducted an analysis of both custom and commercial games. The results of the meta-analysis proved that game-based VR upper limb rehabilitation therapy for cerebral apoplexy is an effective method of rehabilitation in clinical settings and is more effective than traditional rehabilitation in improving patients' upper limb function and hand mobility. Custom games heal better than commercial games. This study only includes nonimmersive device rehabilitation modes due to research constraints and classified them into four categories. The mode of VR games combined with rehabilitation instruments may solve the problem that patients with severe upper limb dysfunction cannot operate games. Whether the use of immersive VR devices and the fun of games will affect patients' rehabilitation motivation and effect is the direction of future research.