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The knee is the biggest and complicated lower extremity joint that supports mobility and the entire weight of the human body and lies between the hip joint and ankle joint. Osteoarthritis (OA) is the most common joint disease in the knee among various musculoskeletal disorders globally, with an age-associated increase in incidence and prevalence. Health monitoring of the knee joints in daily life, and early OA diagnosis is challenging and draws attention to the various methods of diagnosis for this irreversible disease. In this review, electronic databases have been searched from inception for a detailed study about knee OA and its management. It focuses on various sensor technologies and different semi-invasive and non-invasive diagnosis methods with their limitations. In the last decade, various researchers have engrossed their attention to the potential of piezoelectric-based acoustic sensors to fabricate a wearable device for OA and its management. A sensor-based wearable device using vibroarthrography as a tool can be an appropriate solution for early-stage disease detection. We firmly believe that wearable technology for the detection of OA in daily life activities will play a significant role in managing this disease and help to reduce the chances of total knee replacements.

Context: Automated detection of COVID-19 in real time can greatly help clinicians to handle increasing number of cases for preliminary screening. Deep CNN models trained with sufficiently large datasets may become best candidates to meet the purpose. Aims: This study aims for automated detection and classification of COVID-19 and viral pneumonia diseases by applying deep CNN model using chest X-ray images. The proposed model performs multiclass classification to meet the purpose. Settings and Design: The proposed model is built on top of VGG16 architecture with pretrained ImageNet weights. The model was fine-tuned using additional custom layers to deliver better performance specific to the target. Subjects and Methods: A total of 15,153 samples are used in this work. These samples include chest X-ray images of COVID-19, viral pneumonia, and normal cases. The entire dataset was split into train and test sets, with a ratio of 80:20 before training the model. To enhance important image features, image preprocessing and augmentation were applied before feeding the image batches to the model. Statistical Analysis Used: Performance of the model is evaluated through accuracy, precision, recall, and F1 score performance metrics. The results produced by the model are also compared with other recent leading studies. Results: The proposed model has achieved a classification accuracy of 98% with 98% precision, 96% recall, and 97% F1 score on the test dataset for multiclass classification. The area under receiver operating characteristic curve score was 0.99 for all three cases of multiclass classification. Conclusions: The proposed classification model may be highly useful for the preliminary diagnosis of COVID-19 and viral pneumonia cases, especially during heavy workloads and large quantities.

Fatigue of metals has been recognized as an important cause of failure of engineering structures. The experiments show that the fatigue life of real mechanical components is characteristically random. The random nature of the fatigue process is most obvious if a structure is subjected to time-varying random loading. This work develops a stochastic phenomenological model for crack growth which incorporates the effects of material inhomogeneity and random loading as well as including deterministic models which try to explain experimentally observed behavior, thus removing a majority of the shortcomings in existing stochastic models.

A variety of life table models were used for the analysis of the (1984-86) Kuwaiti cause-specific mortality data. These models comprised total mortality, multiple-decrement, cause-elimination, cause-delay and disease dependency. The models were illustrated by application to a set of four chronic diseases: hypertensive, ischaemic heart, cerebrovascular and diabetes mellitus. The life table methods quantify the relative weights of different diseases as hazards to mortality after adjustment for other causes. They can also evaluate the extent of dependency between underlying cause of death and other causes mentioned on death certificate using an extended underlying-cause model. I dati sulla mortalità per causa del Kuwait (1984-86) vengono analizzati mediante alcuni modelli di tavole di mortalità. Vengono presentate: tavole di mortalità generale; tavole a decrementi multipli; tavole costruite eliminando l'effetto di qualche causa, supponendo ritardi nella manifestazione di qualche causa e, infine, supponendo l'esistenza di interrelazioni fra alcune cause. I modelli vengono illustrati mediante l'applicazione all'insieme dei dati di mortalità per quattro malattie croniche: ipertensione, malattie ischemiche del cuore, malattie cerebrovascolari e diabete mellito. I modelli presentati permettono di misurare i pesi relativi delle diverse malattie come rischi di mortalità dopo aver tenuto conto delle altre cause. Essi possono anche permettere di valutare l'esistenza di interrelazioni fra causa di morte principale e altre cause menzionate sul certificato di morte, utilizzando in modo estensivo un modello concepito per la causa principale. Les données de mortalité par cause au Kuwait (1984-86) ont été analysées avec un grand choix de modèles de table de survie; notamment table de mortalité générale, table à plusieurs types de sortie, table en écartant l'effet d'une cause particulière de décès, table en supposant un retard dans l'intervention d'une certaine cause, et en supposant une interaction entre quelques causes. Ces méthodes sont illustrées en traittant un sous-ensemble de quatre maladies chroniques: hypertension, maladies ischémiques du coeur, maladies cérebrovasculaires, diabète. Les méthodes expriment de façon numérique le poids relatif de chaque maladie dans le risque de mortalité, en tenant compte de l'effet des autres causes. Elles permettent aussi d'évaluer le degré d'interaction entre la cause principale et l'intervention des causes concomitantes, déclarées sur le certificat de décès; on utilise alors de façon plus large la méthode d'étude de la cause principale.

Post kala-azar dermal leishmaniasis (PKDL) is a skin disorder that usually occurs among patients with a past history of visceral leishmaniasis (VL). Cases are also reported without a history of VL. There is no satisfactory treatment regimen available at present. We aimed to compare the efficacy and safety of amphotericin B in two different doses (0.5mg/kg vs 1mg/kg) in a prospective randomized trial in 50 PKDL patients. In this open label study 50 patients with PKDL, aged between 5-60 years were randomized in two groups. Group A received amphotericin B in the dose of 0.5 mg/kg in 5% dextrose, daily for 20 infusions for 3 courses at an interval of 15 days between each course and Group B received amphotericin B in the dose of 1mg/kg in 5% dextrose on alternate days, 20 infusions for 3 courses an interval of 15 days between each course and followed up for one year. A total of 50 patients were enrolled, 25 in each of group A and group B. Two patients lost to follow up and three patients withdrew consent due to adverse events. The initial cure rate was 92% in group A and 88% in group B by intention to treat analysis and final cure rate by per protocol analysis was 95.65% and 95.45% in group A and group B respectively. Two patients each from either group relapsed. Nephrotoxicity was the most common adverse event occurring in both the groups. The lower dose appears to have fewer adverse events however, nephrotoxicity remains a problem in both regimens. The 0.5mg/kg regimen may be considered instead of the higher dosage however safer treatments remain critical for PKDL treatment.

In addition to its role in virus entry, HIV-1 gp120 has also been implicated in HIV-associated neurocognitive disorders. However, the mechanism(s) responsible for gp120-mediated neuroinflammation remain undefined. In view of increased levels of IL-6 in HIV-positive individuals with neurological manifestations, we sought to address whether gp120 is involved in IL-6 over-expression in astrocytes. Transfection of a human astrocyte cell line with a plasmid encoding gp120 resulted in increased expression of IL-6 at the levels of mRNA and protein by 51.3±2.1 and 11.6±2.2 fold respectively; this effect of gp120 on IL-6 expression was also demonstrated using primary human fetal astrocytes. A similar effect on IL-6 expression was observed when primary astrocytes were treated with gp120 protein derived from different strains of X4 and R5 tropic HIV-1. The induction of IL-6 could be abrogated by use of gp120-specific siRNA. Furthermore, this study showed that the NF-κB pathway is involved in gp120-mediated IL-6 over-expression, as IKK-2 and IKKβ inhibitors inhibited IL-6 expression by 56.5% and 60.8%, respectively. These results were also confirmed through the use of NF-κB specific siRNA. We also showed that gp120 could increase the phosphorylation of IκBα. Furthermore, gp120 transfection in the SVGA cells increased translocation of NF-κB from cytoplasm to nucleus. These results demonstrate that HIV-1 gp120-mediated over-expression of IL-6 in astrocytes is one mechanism responsible for neuroinflammation in HIV-infected individuals and this is mediated by the NF-κB pathway.

As digitization is increasing, threats to our data are also increasing at a faster pace. Generating fake videos does not require any particular type of knowledge, hardware, memory, or any computational device; however, its detection is challenging. Several methods in the past have solved the issue, but computation costs are still high and a highly efficient model has yet to be developed. Therefore, we proposed a new model architecture known as DFN (Deep Fake Network), which has the basic blocks of mobNet, a linear stack of separable convolution, max-pooling layers with Swish as an activation function, and XGBoost as a classifier to detect deepfake videos. The proposed model is more accurate compared to Xception, Efficient Net, and other state-of-the-art models. The DFN performance was tested on a DFDC (Deep Fake Detection Challenge) dataset. The proposed method achieved an accuracy of 93.28% and a precision of 91.03% with this dataset. In addition, training and validation loss was 0.14 and 0.17, respectively. Furthermore, we have taken care of all types of facial manipulations, making the model more robust, generalized, and lightweight, with the ability to detect all types of facial manipulations in videos.

This paper introduces 3D-QTRNet, a novel quantum-inspired neural network for volumetric medical image segmentation. Unlike conventional CNNs, which suffer from slow convergence and high complexity, and QINNs, which are limited to grayscale segmentation, our approach leverages qutrit encoding and tensor ring decomposition. These techniques improve segmentation accuracy, optimize memory usage, and accelerate model convergence. The proposed model demonstrates superior performance on the BRATS19 and Spleen datasets, outperforming state-of-the-art CNN and quantum models in terms of Dice similarity and segmentation precision. This work bridges the gap between quantum computing and medical imaging, offering a scalable solution for real-world applications.

Fuzzy entropy clustering (FEC) is a variant of hard c-means clustering which utilizes the concept of entropy. However, the performance of the FEC method is sensitive to the noise and the fuzzy entropy parameter as it gives incorrect clustering and coincident cluster sometimes. In this work, a variant of the FEC method is proposed which incorporates advantage of intuitionistic fuzzy set and kernel distance measure termed as kernel intuitionistic fuzzy entropy c-means (KIFECM). While intuitionistic fuzzy set allows to handle uncertainty and vagueness associated with data, kernel distance measure helps to reveal the inherent nonlinear structures present in data without increasing the computational complexity. In this work, two popular intuitionistic fuzzy sets generators, Sugeno and Yager’s negation function, have been utilized for generating intuitionistic fuzzy sets corresponding to data. The performance of the proposed method has been evaluated over two synthetic datasets, Iris dataset, publicly available simulated human brain MRI dataset and IBSR real human brain MRI dataset. The experimental results show the superior performance of the proposed KIFECM over FEC, FCM, IFCM, UPCA, PTFECM and KFEC in terms of several performance measures such as partition coefficient, partition entropy, average segmentation accuracy, dice score, Jaccard score, false positive ratio and false negative ratio.