Platelet count as a prognostic indicator in pregnancy induced hypertension

Background: Hypertensive disorders are most common medical complications of pregnancy, and are one of the major causes of maternal and fetal morbidity and mortality. Thrombocytopenia complicating hypertensive disorders of pregnancy are responsible for approximately 20% of all cases of thrombocytopenia during pregnancy.Our study was done to assess the utility of platelet count as a prognostic indicator in pregnancy induced hypertension to recognize and manage early the complications arising and to have a better pregnancy outcome.Methods: This study includes 76 cases of pregnancy induced hypertension over a period of 18 months. Platelet estimation was done for all cases and patients with documented platelet count of less than 1,50,000/cumm was documented as thrombocytopenia.Results: Of the 76 cases of pregnancy induced hypertension, 32 (42.1%) were diagnosed with thrombocytopenia, and an increased incidence of maternal and fetal morbidity & mortality was observed.Conclusions: Our study and the results show that the assay of platelets can be considered as one of the prognostic tool in management of hypertensive disorders of pregnancy

Long Non Coding RNA in Triple Negative Breast Cancer: A Promising Biomarker in Tumorigenesis

Globally, Triple-negative breast cancer (TNBC) is an unsurpassed variant of breast cancer (BC) with a very high fatality rate, and disease burden. Nevertheless, the deficit of diagnostic markers and focused treatment are major hurdles for potent therapeutics. They are also the reason for bad outcomes and causes of a worse prognosis and a high rate of flare up in patients with TNBC diagnosis. Long non-coding RNAs (lncRNA) are a new class of molecules that have recently gained interest in healthcare management due to their potential as biomarkers for human diseases especially cancers. The growing interest in lncRNA in clinical practice has created an unmet need for developing assays to test lncRNA quickly and accurately for early diagnostics. These lncRNA modulate multiple stages of tumor development, including growth, proliferation, invasion, angiogenesis, and metastases, by controlling several genes and changing metabolic networks. Highly invasive phenotype and chemo resistance are prominent characteristics of TNBC subtypes that require accurate diagnostic and prognostic instruments involving lncRNA. This review focusses on the evolving purpose and coalition of lncRNAs in TNBC and accentuates their capable effects in diagnosis and treatment of cancer. Moreover, the extensive literature analysis of our review creates an opportunity in the translational application concerning the TNBC lncRNAs described until now. The depiction of lncRNAs enrolled in TNBC is comprehensive, and sufficient substantiation studies are the need of the hour to authenticate the current outcomes and create imminent upcoming of elemental research setting into clinical practice

Plastic Response of a 2D Lennard-Jones amorphous solid: Detailed analysis of the local rearrangements at very slow strain-rate

We analyze in details the atomistic response of a model amorphous material submitted to plastic shear in the athermal, quasistatic limit. After a linear stress-strain behavior, the system undergoes a noisy plastic flow. We show that the plastic flow is spatially heterogeneous. Two kinds of plastic events occur in the system: quadrupolar localized rearrangements, and shear bands. The analysis of the individual motion of a particle shows also two regimes: a hyper-diffusive regime followed by a diffusive regime, even at zero temperature

Breaking the Camel's Back: Can Cognitive Overload be Quantified in the Human Brain?

Reductionism lies at the heart of science, yet this pre-occupation with the trees may mean that cognitive science is missing the forest. Based on the assumption that individual cognitive and perceptual processes interact to form bottle-necks of processing, which, in turn, have measurable detrimental effects on human performance, whole-head continuous EEG was recorded as participants undertook baseline, mild cognitive load and heavy cognitive load tasks. Behavioral measures (reaction times and error rates) showed significant performance decrements between the mild and heavy cognitive load conditions. Graph analysis and pattern identification was then used to identify a sub-set of cortical locations reflecting significant, measurable neural differences between the mild and heavy cognitive load states. This thus lays the foundation for future research into suitable metrics for more accurately measuring degree of global cognitive load as well as practical applications such as developing simple devices for measuring cognitive load in real time

A Deep Auto-Optimized Collaborative Learning (DACL) model for disease prognosis using AI-IoMT systems

In modern healthcare, integrating Artificial Intelligence (AI) and Internet of Medical Things (IoMT) is highly beneficial and has made it possible to effectively control disease using networks of interconnected sensors worn by individuals. The purpose of this work is to develop an AI-IoMT framework for identifying several of chronic diseases form the patients’ medical record. For that, the Deep Auto-Optimized Collaborative Learning (DACL) Model, a brand-new AI-IoMT framework, has been developed for rapid diagnosis of chronic diseases like heart disease, diabetes, and stroke. Then, a Deep Auto-Encoder Model (DAEM) is used in the proposed framework to formulate the imputed and preprocessed data by determining the fields of characteristics or information that are lacking. To speed up classification training and testing, the Golden Flower Search (GFS) approach is then utilized to choose the best features from the imputed data. In addition, the cutting-edge Collaborative Bias Integrated GAN (ColBGaN) model has been created for precisely recognizing and classifying the types of chronic diseases from the medical records of patients. The loss function is optimally estimated during classification using the Water Drop Optimization (WDO) technique, reducing the classifier’s error rate. Using some of the well-known benchmarking datasets and performance measures, the proposed DACL’s effectiveness and efficiency in identifying diseases is evaluated and compared

Implementing heuristic-based multiscale depth-wise separable adaptive temporal convolutional network for ambient air quality prediction using real time data

In many emerging nations, rapid industrialization and urbanization have led to heightened levels of air pollution. This sudden rise in air pollution, which affects global sustainability and human health, has become a significant concern for citizens and governments. While most current methods for predicting air quality rely on shallow models and often yield unsatisfactory results, our study explores a deep architectural model for forecasting air quality. We employ a sophisticated deep learning structure to develop an advanced system for ambient air quality prediction. We utilize three publicly available databases and real-world data to obtain accurate air quality measurements. These four datasets undergo a data cleaning to yield a consolidated, cleaned dataset. Subsequently, the Fused Eurasian Oystercatcher-Pathfinder Algorithm (FEO-PFA)—a dual optimization method combining the Eurasian Oystercatcher Optimizer (EOO) and Pathfinder Algorithm (PFA)—is applied. This method aids in selecting weighted features, optimizing weights, and choosing the most relevant attributes for optimal results. These optimal features are then incorporated into the Multiscale Depth-wise Separable Adaptive Temporal Convolutional Network (MDS-ATCN) for the ambient Air Quality Prediction (AQP) process. The variables within MDS-ATCN are further refined using the proposed FEO-PFA to enhance predictive accuracy. An empirical analysis is performed to compare the efficacy of our proposed model with traditional methods, underscoring the superior effectiveness of our approach. The average cost function is reduced to 5.5%, the MAE to 28%, and the RMSE to 14% by the suggested method, according to the performance research conducted with regard to all datasets

ERABiLNet: enhanced residual attention with bidirectional long short-term memory

Alzheimer’s Disease (AD) causes slow death in brain cells due to shrinkage of brain cells which is more prevalent in older people. In most cases, the symptoms of AD are mistaken as age-related stresses. The most widely utilized method to detect AD is Magnetic Resonance Imaging (MRI). Along with Artificial Intelligence (AI) techniques, the efficacy of identifying diseases related to the brain has become easier. But, the identical phenotype makes it challenging to identify the disease from the neuro-images. Hence, a deep learning method to detect AD at the beginning stage is suggested in this work. The newly implemented “Enhanced Residual Attention with Bi-directional Long Short-Term Memory (Bi-LSTM) (ERABi-LNet)” is used in the detection phase to identify the AD from the MRI images. This model is used for enhancing the performance of the Alzheimer’s detection in scale of 2–5%, minimizing the error rates, increasing the balance of the model, so that the multi-class problems are supported. At first, MRI images are given to “Residual Attention Network (RAN)”, which is specially developed with three convolutional layers, namely atrous, dilated and Depth-Wise Separable (DWS), to obtain the relevant attributes. The most appropriate attributes are determined by these layers, and subjected to target-based fusion. Then the fused attributes are fed into the “Attention-based Bi-LSTM”. The final outcome is obtained from this unit. The detection efficiency based on median is 26.37% and accuracy is 97.367% obtained by tuning the parameters in the ERABi-LNet with the help of Modified Search and Rescue Operations (MCDMR-SRO). The obtained results are compared with ROA-ERABi-LNet, EOO-ERABi-LNet, GTBO-ERABi-LNet and SRO-ERABi-LNet respectively. The ERABi_LNet thus provides enhanced accuracy and other performance metrics compared to such deep learning models. The proposed method has the better sensitivity, specificity, F1-Score and False Positive Rate compared with all the above mentioned competing models with values such as 97.49%.97.84%,97.74% and 2.616 respective;y. This ensures that the model has better learning capabilities and provides lesser false positives with balanced prediction

Performance characteristics of an instrument-free point-of-care CD4 test (VISITECTVR CD4) for use in resource-limited settings

Objective: CD4þ T lymphocyte count remains the most common biomarker of immune status and disease progression in human immunodeficiency virus (HIV)-positive individuals. VISITECTVR CD4 is an instrument-free, low-cost point-of-care CD4 test with a cut-off of 350 CD4 cells/lL. This study aimed to evaluate VISITECTVR CD4 test’s diagnostic accuracy. Methods: Two hundred HIV-positive patients attending a tertiary HIV centre in South India were recruited. Patients provided venous blood for reference and VISITECTVR CD4 tests. An additional finger-prick blood sample was obtained for VISITECTVR CD4. VISITECTVR CD4’s diagnostic performance in identifying individuals with CD4 counts 350 cells/lL was assessed by calculating sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) taking flow cytometry as the reference. Results: The overall agreement between VISITECTVR CD4 and flow cytometry was 89.5% using venous blood and 81.5% using finger-prick blood. VISITECTVR CD4 showed better performance using venous blood [sensitivity: 96.6% (95% confidence interval: 92.1%–98.9%), specificity: 70.9% (57.1%–82.4%), PPV: 89.7% (83.9%–94.0%) and NPV: 88.6% (75.4%–96.2%)] than using fingerprick blood [sensitivity: 84.8% (77.9%–90.2%), specificity: 72.7% (59.0%–83.9%), PPV: 89.1% (82.7%–93.8%) and NPV: 64.5% (51.3%–76.3%)]. Conclusion: VISITECTVR CD4 performed well using venous blood, demonstrating its potential utility in decentralization of CD4 testing services in resource-constrained settings