Targeting CERS6-AS1/FGFR1 axis as synthetic vulnerability to constrain stromal cells supported proliferation in Mantle cell lymphoma.

The interaction between stromal and tumor cells in tumor microenvironment is a crucial factor in Mantle cell lymphoma (MCL) progression and therapy resistance. We have identified a long non-coding RNA, CERS6-AS1, upregulated in MCL and associated with poor overall survival. CERS6-AS1 expression was elevated in primary MCL within stromal microenvironment and in a subset of MCL cells adhered to stromal layer. These stromal-adhered MCL-subsets exhibited cancer stem cell signatures than suspension counterparts. Mechanistically, we found that downregulating CERS6-AS1 in MCL reduced Fibroblast Growth Factor Receptor-1 (FGFR1), expression attributed to loss of its interaction with RNA-binding protein nucleolin. In addition, using in-silico approach, we have discovered a direct interaction between nucleolin and 5'UTR of FGFR1, thereby regulating FGFR1 transcript stability. We discovered a positive association of CERS6-AS1 with cancer stem cell signatures, and Wnt signaling. Building on these, we explored potential therapeutic strategies where combining nucleolin-targeting agent with FGFR1 inhibition significantly contributed to reversing cancer stem cell signatures and abrogated primary MCL cell growth on stromal layer. These findings provide mechanistic insights into regulatory network involving CERS6-AS1, nucleolin, and FGFR1 axis-associated crosstalk between tumor cells and stromal cell interaction and highlights therapeutic potential of targeting a non-coding RNA in MCL.

Surgical treatment of Candida albicans spondylodiscitis.

Introduction: Spinal infection poses a demanding diagnostic and treatment problem for which a multidisciplinary approach with spine surgeons, radiologists, and infectious disease specialists is required. Infections are usually caused by bacterial microorganisms, although fungal infections can also occur. Most patients with spinal infections diagnosed in the early stages can be successfully managed conservatively with antibiotics, bed rest, and spinal braces. In cases of gross or pending instability, progressive neurological deficits, failure of conservative treatment, spinal abscess formation, severe symptoms indicating sepsis, and failure of previous conservative treatment, surgical treatment is required. Case presentation: A 64-year-old male presented to the Outpatient Department with a complaint of pain in bilateral upper extremities for 4 months. The pain was shooting in type, radiating to bilateral arms, forearms, and hands with no aggravating and relieving factors. He is a known case of carcinoma pyriform sinus for which he underwent various cycles of chemotherapy. Ten years later, a tracheostomy was performed for laryngeal edema, and again, an endoscopic gastrostomy was performed due to feeding difficulties. He then developed fever and cervical pain along with pain in the bilateral upper extremities. An infectious etiology was suspected for which multiple antibiotics were started with no positive response. An MRI was performed, which was suggestive of spondylodiscitis probably of tubercular origin. A biopsy was done to confirm the diagnosis, following which antitubercular (HRZE) therapy was started. He was also treated with Duloxetine and gabapentin, which resulted in minor improvements. Subsequent MRIs showed diffuse involvement of the multiple cervical vertebrae along with cord compression. Two stages of anterior corpectomy followed by posterior instrumentation were done. Following the procedure, the patient developed an infection, which was managed with antibiotics. The titanium implant was not removed. A muscle graft was planned with the pectoralis muscle and flap closure was done. The tissue was also sent for Gram stain, AFB stain, and GeneXpert, which showed normal findings. Finally, in tissue culture, Candida albicans was isolated. On performing the enzyme immunoassay test, it was found to be Aspergillus (Galactomannan antigen) positive as well. Antitubercular treatment was stopped. Then, he was managed with an antifungal, oral voriconazole, for the duration of 1 and a half years. Clinical discussion: Patients diagnosed with Candida spondylodiscitis tend to have favorable outcomes, likely linked to timely identification, thorough surgical debridement, and proper azole medication. Our case achieved success by promptly identifying and confirming it through tissue culture, detecting spinal cord compression, decompressing it, and initiating specific antifungal treatment. A delay in commencing antifungal therapy has been associated with poorer outcomes, especially in neurological health. Our patient received voriconazole for a full year, suggesting that favorable outcomes are achievable for fungal spondylodiscitis with swift and appropriate surgery and antifungal medication. Conclusion: In summary, evaluation for fungal infection is essential in all cases of unexplained spinal infection in immunocompromised patients, regardless of presentation. If the antifungal treatment proves ineffective, a surgical approach is typically employed for the management of fungal spondylodiscitis. Our report details a successful case of fungal spondylodiscitis treated with a surgical approach and highlights the potential for a fungal infection to be a causative factor in noncompressive myelopathy, which may be sometimes mistaken for radiation myelitis.

Ultrasonography guided erector spinae block in spinal surgery for pain management with enhanced recovery: A comparative study.

Background: Improved and efficient management of pain can certainly aid enhanced recovery after spinal surgery. Our aim is to evaluate the effect of ESPB in thoracic and lumbar surgeries where we have evaluated VAS for pain, cumulative analgesics consumptions, length of hospital stay and post-operative complications. Methods: A cross-sectional comparative study done in HAMS among the erector spinae block group and control group. The analysis of different variable was done according to standard statistical analysis. For quantitative data, univariate and multivariate analysis was performed to determine statistically significant differences using student's t-test for continuous variables. Results: 60 patients were analyzed, 30 got spinae block and 30 in control group.The mean pain score for spinae block group were 1.90 ± 0.712 and 3.27 ± 1.230 for control group (p < 0.001). Cumulative mean analgesic consumption values for spinae block vs. control groups were 0.030 ± 0.042 mg vs. 0.091 ± 0.891 mg (p = 0.001) for fentanyl; 1.06E4 ± 2833.300 mg vs. 1.53E4 ± 2848.349 mg (p < 0.001) for paracetamol; 213 ± 64.656 mg vs. 494 ± 58.816 mg (p < 0.001) for ketorol; 5440.00 ± 2060.064 mg vs. 8667.50 ± 2275.006 mg (p < 0.001) for ibuprofen and 121.67 ± 31.303 mg vs. 185.00 ± 51.108 mg (p < 0.001) for tramadol. Conclusions: The ESPB technique shows early discharge from hospital and lower cumulative analgesics consumption which indicates enhanced recovery after spine surgery than control group. Improvement of pain using VAS shows immediate post-operative period recovery in those who receives spinae block.

Prediction of the Age and Gender Based on Human Face Images Based on Deep Learning Algorithm.

In recent times, nutrition recommendation system has gained increasing attention due to their need for healthy living. Current studies on the food domain deal with a recommendation system that focuses on independent users and their health problems but lack nutritional advice to individual users. The proposed system is developed to suggest nutritional food to people based on age and gender predicted from their face image. The designed methodology preprocesses the input image before performing feature extraction using the deep convolution neural network (DCNN) strategy. This network extracts D-dimensional characteristics from the source face image, followed by the feature selection strategy. The face's distinctive and identifiable traits are chosen utilizing a hybrid particle swarm optimization (HPSO) technique. Support vector machine (SVM) is used to classify a person's age and gender. The nutrition recommendation system relies on the age and gender classes. The proposed system is evaluated using classification rate, precision, and recall using Adience dataset and UTKface dataset, and real-world images exhibit excellent performance by achieving good prediction results and computation time.

Optimization of Leach Protocol in Wireless Sensor Network Using Machine Learning.

The Wireless Sensor Network is a network formed in areas human beings cannot access. The data need to be sensed by the sensor and transferred to the sink node. Many routing protocols are designed to route data from a single node to the sink node. One of the routing protocols is the hierarchical routing protocol, which passes on the sensed data hierarchically. The Low Energy Adaptive Clustering Hierarchy (LEACH) is one of the hierarchical methods in which communication happens in two steps: the setup phase and the steady-state phase. The efficiency of the LEACH has to be optimized to improve the network lifetime. Therefore, the k-means clustering algorithm, which comes under the unsupervised machine learning method, is incorporated with the LEACH algorithm and has shown better results. But the selection of cluster head needs to improvise because it will transfer the summed-up data to the sink node, so it is to be efficient enough. So, this paper proposes the modified k-means algorithm with LEACH protocol for optimizing the Wireless Sensor Network. In the modified k-means algorithm, the weight of the cluster head is tested and elected, and the clusters are formed using the Euclidean distance formula. The proposed work yields 48.85% efficiency compared to the existing protocol. It is also proven that the proposed work showed more successful data transfer to the sink node. The cluster head selection process elects the more efficient node as the head with less failure rate. The proposed work optimistically balanced the whole network in terms of energy and successful data transfer.

CNN Based Multiclass Brain Tumor Detection Using Medical Imaging.

Brain tumors are the 10th leading reason for the death which is common among the adults and children. On the basis of texture, region, and shape there exists various types of tumor, and each one has the chances of survival very low. The wrong classification can lead to the worse consequences. As a result, these had to be properly divided into the many classes or grades, which is where multiclass classification comes into play. Magnetic resonance imaging (MRI) pictures are the most acceptable manner or method for representing the human brain for identifying the various tumors. Recent developments in image classification technology have made great strides, and the most popular and better approach that has been considered best in this area is CNN, and therefore, CNN is used for the brain tumor classification issue in this paper. The proposed model was successfully able to classify the brain image into four different classes, namely, no tumor indicating the given MRI of the brain does not have the tumor, glioma, meningioma, and pituitary tumor. This model produces an accuracy of 99%.

Severity Prediction over Parkinson's Disease Prediction by Using the Deep Brooke Inception Net Classifier.

Parkinson's disease (PD) is a neurodegenerative illness that progresses and is long-lasting. It becomes more difficult to talk, write, walk, and do other basic functions when the brain's dopamine-generating neurons are injured or killed. There is a gradual rise in the intensity of these symptoms over time. Using Parkinson's Telemonitoring Voice Data Set from UCI and deep neural networks, we provide a strategy for predicting the severity of Parkinson's disease in this research. An unprocessed speech recording contains a slew of unintelligible data that makes correct diagnosis difficult. Therefore, the raw signal data must be preprocessed using the signal error drop standardization while the features can be grouped by using the wavelet cleft fuzzy algorithm. Then the abnormal features can be selected by using the firming bacteria foraging algorithm for feature size decomposition process. Then classification was made using the deep brooke inception net classifier. The performances of the classifier are compared where the simulation results show that the proposed strategy accuracy in detecting severity of the Parkinson's disease is better than other conventional methods. The proposed DBIN model achieved better accuracy compared to other existing techniques. It is also found that the classification based on extracted voice abnormality data achieves better accuracy (99.8%) over PD prediction; hence it can be concluded as a better metric for severity prediction.

A Hybrid Deep Learning Approach for ECG-Based Arrhythmia Classification.

Arrhythmias are defined as irregularities in the heartbeat rhythm, which may infrequently occur in a human's life. These arrhythmias may cause potentially fatal complications, which may lead to an immediate risk of life. Thus, the detection and classification of arrhythmias is a pertinent issue for cardiac diagnosis. (1) Background: To capture these sporadic events, an electrocardiogram (ECG), a register containing the heart's electrical function, is considered the gold standard. However, since ECG carries a vast amount of information, it becomes very complex and challenging to extract the relevant information from visual analysis. As a result, designing an efficient (automated) system to analyse the enormous quantity of data possessed by ECG is critical. (2) Method: This paper proposes a hybrid deep learning-based approach to automate the detection and classification process. This paper makes two-fold contributions. First, 1D ECG signals are translated into 2D Scalogram images to automate the noise filtering and feature extraction. Then, based on experimental evidence, by combining two learning models, namely 2D convolutional neural network (CNN) and the Long Short-Term Memory (LSTM) network, a hybrid model called 2D-CNN-LSTM is proposed. (3) Result: To evaluate the efficacy of the proposed 2D-CNN-LSTM approach, we conducted a rigorous experimental study using the widely adopted MIT-BIH arrhythmia database. The obtained results show that the proposed approach provides ≈98.7%, 99%, and 99% accuracy for Cardiac Arrhythmias (ARR), Congestive Heart Failure (CHF), and Normal Sinus Rhythm (NSR), respectively. Moreover, it provides an average sensitivity of the proposed model of 98.33% and a specificity value of 98.35%, for all three arrhythmias. (4) Conclusions: For the classification of arrhythmias, a robust approach has been introduced where 2D scalogram images of ECG signals are trained over the CNN-LSTM model. The results obtained are better as compared to the other existing techniques and will greatly reduce the amount of intervention required by doctors. For future work, the proposed method can be applied over some live ECG signals and Bi-LSTM can be applied instead of LSTM.

Comparison of different optimizers implemented on the deep learning architectures for COVID-19 classification.

COVID-19 is the present-day pandemic around the globe. WHO has estimated that approx 15% of the world's population may have been infected with coronavirus with a large number of population on the verge of being infected. It is quite difficult to break the virus chain since asymptomatic patients can result in the spreading of the infection apart from the seriously infected patients. COVID-19 has many similar symptoms to SARS-D however, the symptoms can worsen depending on the immunity power of the patients. It is necessary to be able to find the infected patients even with no symptoms to be able to break the spread of the chain. In this paper, the comparison table describes the accuracy of deep learning architectures by the implementation of different optimizers with different learning rates. In order to remove the overfitting issue, different learning rate has been experimented. Further in this paper, we have proposed the classification of the COVID-19 images using the ensemble of 2 layered Convolutional Neural Network with the Transfer learning method which consumed lesser time for classification and attained an accuracy of nearly 90.45%.

Designing and implementing an integrated non-communicable disease primary care intervention in rural Nepal.

Low-income and middle-income countries are struggling with a growing epidemic of non-communicable diseases. To achieve the Sustainable Development Goals, their healthcare systems need to be strengthened and redesigned. The Starfield 4Cs of primary care-first-contact access, care coordination, comprehensiveness and continuity-offer practical, high-quality design options for non-communicable disease care in low-income and middle-income countries. We describe an integrated non-communicable disease intervention in rural Nepal using the 4C principles. We present 18 months of retrospective assessment of implementation for patients with type II diabetes, hypertension and chronic obstructive pulmonary disease. We assessed feasibility using facility and community follow-up as proxy measures, and assessed effectiveness using singular 'at-goal' metrics for each condition. The median follow-up for diabetes, hypertension and chronic obstructive pulmonary disease was 6, 6 and 7 facility visits, and 10, 10 and 11 community visits, respectively (0.9 monthly patient touch-points). Loss-to-follow-up rates were 16%, 19% and 22%, respectively. The median time between visits was approximately 2 months for facility visits and 1 month for community visits. 'At-goal' status for patients with chronic obstructive pulmonary disease improved from baseline to endline (p=0.01), but not for diabetes or hypertension. This is the first integrated non-communicable disease intervention, based on the 4C principles, in Nepal. Our experience demonstrates high rates of facility and community follow-up, with comparatively low lost-to-follow-up rates. The mixed effectiveness results suggest that while this intervention may be valuable, it may not be sufficient to impact outcomes. To achieve the Sustainable Development Goals, further implementation research is urgently needed to determine how to optimise non-communicable disease interventions.

External Iliac Artery-Appendicular Fistula due to Antegrade Unusual Migration of K-Wire from Hip to Pelvis: An Unreported Complication.

Background. K-wires are thought to be extremely safe implants and complications as a result of direct insertion or migration are very rare. Complications may be life-threatening in some instances where migration results in injury to vital organs. We report one such case where antegrade migration of K-wire from the hip resulted in injury to external iliac artery and formation of external iliac artery-appendicular fistula. No such complication due to migration has ever been reported in the literature. Case Description. A 15-year-old boy presented with lower abdominal pain, right lower limb swelling and pain, inability to walk, and rectal bleeding for 1 month after 2 K-wires had been inserted in his right hip joint for treatment of slipped capital femoral epiphysis the previous year. On investigation, he was diagnosed to have external iliac artery-appendicular fistula for which he was surgically treated. Clinical Relevance. Antegrade migration of K-wire from hip joint may lead to life-threatening injuries which can be minimized by bending the end of the K-wire, keeping the tip protruding outside the skin wherever possible and by early removal of K-wire once its purpose has been achieved.

A model for the evaluation of domain based classification of GPCR.

G-Protein Coupled Receptors (GPCR) are the largest family of membrane bound receptor and plays a vital role in various biological processes with their amenability to drug intervention. They are the spotlight for the pharmaceutical industry. Experimental methods are both time consuming and expensive so there is need to develop a computational approach for classification to expedite the drug discovery process. In the present study domain based classification model has been developed by employing and evaluating various machine learning approaches like Bagging, J48, Bayes net, and Naive Bayes. Various softwares are available for predicting domains. The result and accuracy of output for the same input varies for these software's. Thus, there is dilemma in choosing any one of it. To address this problem, a simulation model has been developed using well known five softwares for domain prediction to explore the best predicted result with maximum accuracy. The classifier is developed for classification up to 3 levels for class A. An accuracy of 98.59% by Naïve Bayes for level I, 92.07% by J48 for level II and 82.14% by Bagging for level III has been achieved.

Thickness effects on maximum von-Mises stress of a cement mantle in total hip replacement - a finite element study.

AIM: The present study seeks to increase the life term of fully cemented total hip replacements by minimizing the stress values within the cement mantle. METHODS: Three-dimensional (3D) finite element analyses have been carried out to investigate the effects of varying cement thickness on the von-Mises stress of a cement mantle. The magnitude and location of maximum von-Mises stress within the cement mantle have been studied for both straight and tapered prosthetic stems. RESULTS: For prosthetic stems having lower radii sizes, the maximum stress zone is found in the upper region of the cement mantle whilst for stems with higher radii sizes the maximum stress zone is found in the lower region of the cement mantle. For the same cement thickness, straight stems are found to produce lower maximum stress values in the cement when compared to tapered stems. Finally, for the straight models with the same cement thickness, maximum stress values are found to decrease with increasing stem radius. CONCLUSIONS: It can be concluded that the maximum stress values in the cement mantle decrease with decreasing cement thickness.