MOB-CBAM: A dual-channel attention-based deep learning generalizable model for breast cancer molecular subtypes prediction using mammograms.

BACKGROUND AND OBJECTIVE: Deep Learning models have emerged as a significant tool in generating efficient solutions for complex problems including cancer detection, as they can analyze large amounts of data with high efficiency and performance. Recent medical studies highlight the significance of molecular subtype detection in breast cancer, aiding the development of personalized treatment plans as different subtypes of cancer respond better to different therapies. METHODS: In this work, we propose a novel lightweight dual-channel attention-based deep learning model MOB-CBAM that utilizes the backbone of MobileNet-V3 architecture with a Convolutional Block Attention Module to make highly accurate and precise predictions about breast cancer. We used the CMMD mammogram dataset to evaluate the proposed model in our study. Nine distinct data subsets were created from the original dataset to perform coarse and fine-grained predictions, enabling it to identify masses, calcifications, benign, malignant tumors and molecular subtypes of cancer, including Luminal A, Luminal B, HER-2 Positive, and Triple Negative. The pipeline incorporates several image pre-processing techniques, including filtering, enhancement, and normalization, for enhancing the model's generalization ability. RESULTS: While identifying benign versus malignant tumors, i.e., coarse-grained classification, the MOB-CBAM model produced exceptional results with 99 % accuracy, precision, recall, and F1-score values of 0.99 and MCC of 0.98. In terms of fine-grained classification, the MOB-CBAM model has proven to be highly efficient in accurately identifying mass with (benign/malignant) and calcification with (benign/malignant) classification tasks with an impressive accuracy rate of 98 %. We have also cross-validated the efficiency of the proposed MOB-CBAM deep learning architecture on two datasets: MIAS and CBIS-DDSM. On the MIAS dataset, an accuracy of 97 % was reported for the task of classifying benign, malignant, and normal images, while on the CBIS-DDSM dataset, an accuracy of 98 % was achieved for the classification of mass with either benign or malignant, and calcification with benign and malignant tumors. CONCLUSION: This study presents lightweight MOB-CBAM, a novel deep learning framework, to address breast cancer diagnosis and subtype prediction. The model's innovative incorporation of the CBAM enhances precise predictions. The extensive evaluation of the CMMD dataset and cross-validation on other datasets affirm the model's efficacy.

Progressive resistance training ameliorates deteriorating cardiac autonomic dysfunction, subclinical inflammation and endothelial dysfunction in type 2 diabetes mellitus: A randomized control trial.

BACKGROUND AND AIMS: To investigate the effect of resistance training (RT) on outcomes of cardiac autonomic control, biomarkers of subclinical inflammation, endothelial dysfunction, and angiotensin II in T2DM patients with CAN. METHODS: Fifty six T2DM patients with CAN were recruited in the present study.After baseline assessment of all outcome variables, patients were randomly allocated into two groups - RT (n = 28) and Control (n = 28). The experimental group underwent 12 weeks of RT and the control group received usual care. RT was performed at an intensity of 65%-75% of 1 RM, 3 times/week for 12 weeks. RT program included 10 exercises of major muscle groups in the body. Cardiac autonomic control parameters, subclinical inflammation and endothelial dysfunction biomarkers, and serum angiotensin II concentration were assessed at baseline and after 12 weeks. RESULTS: Parameters of cardiac autonomic control showed significant improvement after RT (p < 0.05). Interleukin-6, interleukin-18 were significantly reduced while endothelial nitric oxide synthase was significantly increased post-RT (p < 0.05). CONCLUSIONS: Findings of the present study suggest that RT has the potential to enhance deteriorating cardiac autonomic function in T2DM patients with CAN. RT also seems to have an anti-inflammatory role and it may also play some role in vascular remodeling in these patients. TRIAL REGISTRATION: CTRI/2018/04/013321, Registered prospectively on 13th April 2018, Clinical Trial Registry, India.

SCS-Net: An efficient and practical approach towards Face Mask Detection.

Much work has been done in the computer vision domain for the problem of facial mask detection to curb the spread of the Coronavirus disease (COVID-19). Preventive measures developed using deep learning-based models have got enormous attention. With the state-of-the-art results touching perfect accuracies on various models and datasets, two very practical problems are still not addressed - the deployability of the model in the real world and the crucial cases of incorrectly worn masks. To this end, our method proposes a lightweight deep learning model with just 0.12M parameters having up to 496 times reduction as compared to some of the existing models. Our novel architecture of the deep learning model is designed for practical implications in the real world. We also augment an existing dataset with a large set of incorrectly masked face images leading to a more balanced three-class classification problem. A large collection of 25296 synthetically designed incorrect face mask images are provided. This is the first of its kind of data to be proposed with equal diversity and quantity. The proposed model achieves a competitive accuracy of 95.41% on two class classification and 95.54% on the extended three class classification with minimum number of parameters in comparison. The performance of the proposed system is assessed with various state-of-the-art literature and experimental results indicate that our solution is more realistic and rational than many existing works which use overly massive models unsuitable for practical deployability.

Integrating water quality index, GIS and multivariate statistical techniques towards a better understanding of drinking water quality.

Groundwater is considered as an imperative component of the accessible water assets across the world. Due to urbanization, industrialization and intensive farming practices, the groundwater resources have been exposed to large-scale depletion and quality degradation. The prime objective of this study was to evaluate the groundwater quality for drinking purposes in Mewat district of Haryana, India. For this purpose, twenty-five groundwater samples were collected from hand pumps and tube wells spread over the entire district. Samples were analyzed for pH, electrical conductivity (EC), total dissolved solids (TDS), total hardness (TH), turbidity, total alkalinity (TA), cations and anions in the laboratory using the standard methods. Two different water quality indices (weighted arithmetic water quality index and entropy weighted water quality index) were computed to characterize the groundwater quality of the study area. Ordinary Kriging technique was applied to generate spatial distribution map of the WQIs. Four semivariogram models, i.e. circular, spherical, exponential and Gaussian were used and found to be the best fit for analyzing the spatial variability in terms of weighted arithmetic index (GWQI) and entropy weighted water quality index (EWQI). Hierarchical cluster analysis (HCA), principal component analysis (PCA) and discriminant analysis (DA) were applied to provide additional scientific insights into the information content of the groundwater quality data available for this study. The interpretation of WQI analysis based on GWQI and EWQI reveals that 64% of the samples belong to the "poor" to "very poor" bracket. The result for the semivariogram modeling also shows that Gaussian model obtains the best fit for both EWQI and GWQI dataset. HCA classified 25 sampling locations into three main clusters of similar groundwater characteristics. DA validated these clusters and identified a total of three significant variables (pH, EC and Cl) by adopting stepwise method. The application of PCA resulted in three factors explaining 69.81% of the total variance. These factors reveal how processes like rock water interaction, urban waste discharge and mineral dissolution affect the groundwater quality.

Artificial Intelligence against COVID-19 Pandemic: A Comprehensive Insight.

COVID-19 is a pandemic initially identified in Wuhan, China, which is caused by a novel coronavirus, also recognized as the Severe Acute Respiratory Syndrome (SARS-nCoV-2). Unlike other coronaviruses, this novel pathogen may cause unusual contagious pain, which results in viral pneumonia, serious heart problems, and even death. Researchers worldwide are continuously striving to develop a cure for this highly infectious disease, yet there are no well-defined absolute treatments available at present. Several vaccination drives using emergency use authorisation vaccines have been held across many countries; however, their long-term efficacy and side-effects studies are yet to be studied. Various analytical and statistical models have been developed, however, their outcome rate is prolonged. Thus, modern science stresses the application of state-of-the-art methods to combat COVID-19. This paper aims to provide a deep insight into the comprehensive literature about AI and AI-driven tools in the battle against the COVID-19 pandemic. The high efficacy of these AI systems can be observed in terms of highly accurate results, i.e., > 95%, as reported in various studies. The extensive literature reviewed in this paper is divided into five sections, each describing the application of AI against COVID-19 viz. COVID-19 prevention, diagnostic, infection spread trend prediction, therapeutic and drug repurposing. The application of Artificial Intelligence (AI) and AI-driven tools are proving to be useful in managing and fighting against the COVID-19 pandemic, especially by analysing the X-Ray and CT-Scan imaging data of infected subjects, infection trend predictions, etc.

Type 2 Diabetes Mellitus Patients Manifest Greater Muscle Fatigability Than Healthy Individuals During Dynamic Fatigue Protocol.

OBJECTIVE: The present study aimed to investigate the electromyographic (EMG) indices of muscle fatigue along with biochemical marker of fatigue-that is, blood lactate-during a dynamic fatigue protocol in individuals with type 2 diabetes mellitus (T2DM) vs a healthy control group. Secondarily, it aimed to examine the association between EMG indices of muscle fatigue and blood lactate in these patients. METHODS: Thirty-four participants took part in the study: 19 individuals with T2DM (age, 53.5 ± 6.85 years) and 15 age-matched healthy controls (age, 50.2 ± 3.55 years). Participants performed a dynamic fatigue protocol consisting of 5 sets of 10 repetitions each at an intensity of the 10-repetition maximum. Surface EMG of the vastus medialis and vastus lateralis muscles was recorded during the dynamic fatigue protocol, and EMG indices such as median frequency (MF), slope of MF (MFslope), Dimitrov muscle fatigue spectral index, and root-mean-square were evaluated for each contraction across all the 5 sets. Blood lactate concentrations were also assessed 3 times during the fatigue protocol. RESULTS: Findings revealed that EMG muscle fatigue indices such as MF, MFslope, and Dimitrov muscle fatigue spectral index were significantly altered in individuals with T2DM vs healthy individuals across the sets and repetitions for both the vastus medialis (P < .001) and vastus lateralis muscles (P < .001). There was a significantly greater rise in blood lactate in individuals with T2DM than in healthy individuals (P < .001), which was not found to be associated with changes in EMG indices of muscle fatigue. CONCLUSION: Findings suggest the existence of significantly greater fatigue in the knee extensor muscles of individuals with T2DM than healthy individuals.