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Clinical reports and research studies have documented the behavior of chronic low back and neck pain patients. A few hypotheses have attempted to explain these varied clinical and research findings. A new hypothesis, based upon the concept that subfailure injuries of ligaments (spinal ligaments, disc annulus and facet capsules) may cause chronic back pain due to muscle control dysfunction, is presented. The hypothesis has the following sequential steps. Single trauma or cumulative microtrauma causes subfailure injuries of the ligaments and embedded mechanoreceptors. The injured mechanoreceptors generate corrupted transducer signals, which lead to corrupted muscle response pattern produced by the neuromuscular control unit. Muscle coordination and individual muscle force characteristics, i.e. onset, magnitude, and shut-off, are disrupted. This results in abnormal stresses and strains in the ligaments, mechanoreceptors and muscles, and excessive loading of the facet joints. Due to inherently poor healing of spinal ligaments, accelerated degeneration of disc and facet joints may occur. The abnormal conditions may persist, and, over time, may lead to chronic back pain via inflammation of neural tissues. The hypothesis explains many of the clinical observations and research findings about the back pain patients. The hypothesis may help in a better understanding of chronic low back and neck pain patients, and in improved clinical management.

Endocrine therapy (ET) remains the mainstay of treatment for steroid hormone receptor-positive, human epidermal growth factor 2 (HER2)-negative metastatic breast cancer (MBC). Tumor resistance to hormone therapy has led to the development of novel endocrine drug combinations, transforming the landscape of MBC management. The options for ET are expanding, with promising agents in the pipeline. Although MBC remains incurable, many patients can enjoy years of survival with good quality of life by cycling through the many available agents. With the plethora of available agents and rapid approvals, clinicians look to evidence-based guidelines to assist in treatment selection to maximize patient well-being. In this review, we provide a contemporary review of the advances in ET and a suggested algorithm to guide clinicians in daily management of patients with hormone receptor-positive, HER2-negative MBC. We will discuss landmark trials and highlight their impact in reshaping treatment approaches. Finally, we will provide a glimpse into advances on the horizon and the promise they bring to improve outcomes in patients with advanced breast cancer.

Integrating cutting-edge technology with conventional farming practices has been dubbed “smart agriculture” or “the agricultural internet of things.” Agriculture 4.0, made possible by the merging of Industry 4.0 and Intelligent Agriculture, is the next generation after industrial farming. Agriculture 4.0 introduces several additional risks, but thousands of IoT devices are left vulnerable after deployment. Security investigators are working in this area to ensure the safety of the agricultural apparatus, which may launch several DDoS attacks to render a service inaccessible and then insert bogus data to convince us that the agricultural apparatus is secure when, in fact, it has been stolen. In this paper, we provide an IDS for DDoS attacks that is built on one-dimensional convolutional neural networks (IDSNet). We employed prairie dog optimization (PDO) to fine-tune the IDSNet training settings. The proposed model's efficiency is compared to those already in use using two newly published real-world traffic datasets, CIC-DDoS attacks.

Canada has come a long way since Dr. C. Henry Kempe first described battered-child syndrome in 1962. The year 1999 was crucial in Canada’s battle against shaken baby syndrome/abusive head trauma (SBS/AHT), when the first national conference on the topic was held in Saskatoon. This was followed by the issuance of a national statement and multidisciplinary guidelines, recently updated in 2020. Incidence of AHT in Canada is similar to that found in population-based studies from Switzerland and New Zealand. The mainstay of prevention of AHT in Canada is education of parents and caregivers with respect to their response to infant crying. Population-based data for global incidence of AHT are lacking, largely because of social and cultural differences contributing to poor understanding of AHT as a medico-legal entity. India faces a distinct challenge in the battle against female feticide and infanticide.

A comprehensive test program was performed to experimentally investigate the effect of CFRP strengthening on the axial strength and stability of CFS plain angle short columns subjected to monotonic axial compression. A total of 28 specimens were tested by varying the CFRP strengthening configurations for different column heights. Both uni-directional (CF_UD) and bi-directional (CF_BD) CFRP were considered. The influence of various parameters such as the type of CFRP, fiber orientation, and number of CFRP layers was investigated and discussed in detail. For single layer (ply) of CFRP, CF_UD-0° strengthening configuration resulted in maximum increase of axial capacity by 58.33% and 45.72% (in comparison to bare steel specimens), corresponding to 0.5 m and 1.0 m column lengths respectively. All the bare steel and skin-strengthened specimens failed predominantly due to torsional–flexural buckling mode. Additional layer of CFRP wrapping was found to enhance the axial capacity further and CF_UD-0°/BD was found to possess greater capacity in the case of double layer of CFRP. Adopting cardboard in-fill in addition to CF_UD-0° wrap has prevented the torsional mode of buckling and resulted in a peak increase of axial capacity by 192.55% and 240.61% corresponding to 500 mm and 100 mm long specimens, respectively.

Twitter is a social media stage, making it a valuable resource for learning about people’s opinions, feelings, and thoughts. For this reason, experts came up with methods to analyse the tone of tweets and determine whether they were favourable or negative. This article aims to assist businesses, and especially app-based meal delivery businesses, in conducting competitive research on social broadcasting and transforming social broadcasting data into data production for decision-makers. In this analysis, we compared Swiggy, Zomato, and UberEats. Customers’ tweets about all these brands are obtained using R-Studio, and a deep learning-based sentiment examination approach is functional on the retrieved tweets. The pseudo-inverse learning autoencoder is able to provide feature extraction in the form of an analytic solution after pre-processing, without resorting to many iterations. In this research, we suggest framework for combining the Convolutional Neural Network (CNN) and Bi-directional Long Short Term Memory (Bi-LSTM) models. ConvBiLSTM is used, which is a word embedding model that uses numerical values to represent tweets. The CNN layer takes the feature implanting as input and outputs lower features. In this instance, elephant herd optimization is used to fine-tune the Bi-LSTM weights. Among the three firms, the results indicate that Zomato got the most positive feedback (29%), followed by Swiggy (26%), and UberEats (25%). Zomato also had fewer bad reviews than Swiggy and UberEats, with only 11% of users having a poor experience. In addition, tweets were evaluated for unfavourable views against all three meal delivery services, and suggestions for improvement were offered.

Cervical cancer is a leading cause of death in women globally. Systemic chemotherapy offers only limited therapeutic benefit for advanced-stage disease due to toxicity and drug resistance. ONC201 (also known as TIC10 or dordaviprone) is a TRAIL (TNF-Related Apoptosis-Inducing Ligand) and cIpP (caseinolytic protease) agonist currently in Phase II clinical trials for different types of cancer. In the present study, we investigated the anticancer potential of ONC201 in HPV-positive cervical cancer cell lines. ONC201 exerted significant cytotoxicity and inhibited the clonogenic potential of cervical cancer cells. It induced integrated stress response along with S/G2-M arrest and apoptosis in both cell lines. Yet, surprisingly, well-known targets of ONC201 viz. TRAIL, DR5 (death receptor 5) and cIpP were found to be upregulated only in HeLa but not in SiHa cells in response to ONC201 treatment. In addition, expression of BNIP3 and Beclin-1 (both involved in regulation of autophagy) increased in response to certain doses of ONC201. Furthermore, ONC201 exhibited synergism in combination with standard drugs against cervical cancer cells. This study provides a proof of concept for the anticancer activity of versatile drug ONC201 against cervical cancer cells and also delineates its mechanism of action.

Magnetization transfer ratio (MTR), diffusion tensor imaging (DTI) parameters and T1 relaxometry values were used to create parametric maps characterizing the tissue microstructure of the neonatal brain in infants born very premature (24–32 gestational weeks) and scanned at preterm and term equivalent age. Group-wise image registration was used to determine anatomical correspondence between individual scans and the pooled parametric data at the preterm and term ages. These parametric maps showed distinct contrasts whose interrelations varied across brain regions and between the preterm and term period. Discrete patterns of regional variation were observed for the different quantitative parameters, providing evidence that MRI is sensitive to multiple independent aspects of brain maturation. MTR values showed a marked change in the pattern of regional variation at term equivalent age compared to the preterm period such that the ordinal ranking of regions by signal contrast changed. This was unlike all other parameters where the regional ranking was preserved at the two time points. Interpreting the data in terms of myelination and structural organization, we report on the concordance with available histological data and demonstrate the value of quantitative MRI for tracking brain maturation over the neonatal period. ► Quantitative MRI is sensitive to multiple independent aspects of brain maturation. ► Group-wise image registration is used to create multiple averaged parametric maps. ► Various quantitative MRI measures show distinct pattern of regional variations. ► MTR values show a marked change in the pattern of regional variation with time. ► DTI parameters preserve the same order of regional values over the neonatal period.

Multiple sclerosis (MS) diagnostic criteria incorporate MRI features that can be used to predict later diagnosis of MS in adults with acute CNS demyelination. To identify MRI predictors of a subsequent MS diagnosis in a paediatric population, we created a standardised scoring method and applied it to MRI scans from a national prospective incidence cohort of children with CNS demyelination. Clinical and MRI examinations were done at the onset of acute CNS demyelination and every 3 months in the first year after that, and at the time of a second demyelinating attack. MS was diagnosed on the basis of clinical or MRI evidence of relapsing disease. Baseline MRI scans were assessed for the presence of 14 binary response parameters. Parameters were assessed with a multiple tetrachoric correlation matrix. Univariate analyses and multivariable Cox proportional hazards models were used to identify predictors of MS. Between Sept 1, 2004, and June 30, 2010, 332 children and adolescents were assessed for eligibility. 1139 scans were available from 284 eligible participants who had been followed up for 3·9 (SD 1·7) years. 57 (20%) were diagnosed with MS after a median of 188 (IQR 144–337) days. Seven of 14 binary response parameters were retained. The presence of either one or more T1-weighted hypointense lesions (hazard ratio 20·6, 95% CI 5·46–78·0) or one or more periventricular lesions (3·34, 1·27–8·83) was associated with an increased likelihood of MS diagnosis (sensitivity 84%, specificity 93%, positive predictive value 76%, negative predictive value 96%). Risk for MS diagnosis was highest when both parameters were present (34·27, 16·69–70·38). Although the presence of contrast enhancement, cerebral white matter, intracallosal, and brainstem lesions was associated with MS in the univariate analyses, these parameters were not retained in the multivariable models. Specific MRI parameters can be used to predict diagnosis of MS in children with incident demyelination of the CNS. The ability to promptly identify children with MS will enhance timely access to care and will be important for future clinical trials in paediatric MS. Canadian Multiple Sclerosis Scientific Research Foundation.

Tomatoes are widely cultivated and consumed worldwide and are susceptible to various leaf diseases during their growth. Therefore, early detection and prediction of leaf diseases in tomato crops are crucial. Farmers can take proactive measures to prevent the spread and minimize the impact on crop yield and quality by identifying leaf diseases in their early stages. Several Machine Learning (ML) and Deep Learning (DL) frameworks have been developed recently to identify leaf diseases. This research presents an efficient deep-learning approach based on a hybrid classifier by optimizing the CNN and LSTM models, which helps to enhance classification accuracy. Initially, Median Filtering (MF) is used for leaf image pre-processing. Then, an improved watershed approach is used for segmenting the leaf images. Subsequently, enhanced Local Gabor Pattern (LGP) and statistical and color features are extracted. An optimized CNN and LSTM are used for classification, and the weights are tuned using the SISS-OB (Self Improved Shark Smell With Opposition Behavior) algorithm. Finally, we have analyzed the performance using various measures. Since we have done segmentation, feature extraction, and optimization improvisations, our proposed methodology results are higher than other available methods and existing works. The results obtained at Learning Percentage (LP) is 90% which is far superior to those obtained at other LPs. The FNR (False Negative Rate) is much lower (0.05) at the 90th LP. The proposed model achieved better classification performance in terms of Accuracy of 97.13%, Sensitivity of 95.09%, Specificity of 95.24%, Precision of 94.31%, F measure of 96.71% and MCC 87.34%.