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In this work, novel biocompatible and reduction-responsive soft hydrogels were formulated from norbornene (Nb)-functionalized carboxymethyl cellulose (CMCNb). To cross-link the CMC-Nb via a highly bioorthogonal inverse electron demand Diels-Alder (IEDDA) reaction, we employed a water-soluble and reduction-responsive diselenide-based cross-linker possessing two terminal tetrazine (Tz) groups with varying molar concentrations (Nb/Tz molar ratios of 10/10, 10/05, and 10/2.5). The N

Introduction: Pelvic ring and acetabulum injuries are complex injuries varying in severity and complications resulting in serious morbidity in surviving individuals which as a result can be a major contributor to reintegration of patients in professional services. Therefore its important to evaluate to what extent affected patients can return to pre injury work status and how their quality of life is affected. The objective of this study is to evaluate reintegration of affected patients in profession in a two year follow up after acetabular and pelvic ring injuries Material and Methods: Using local Trauma Registry of Orthopedic Trauma department in Shaheed Mohtarma Benazir Bhutto Institute of Trauma (SMBBIT) a retrospective Cohort study was conducted. All patients between age 18 to 60 who had a Acetabulum or Pelvic Ring fracture presented in SMBBIT during July 2017 to July 2019 and were treated by Orthopedic Trauma department were included in study. Multivariate logistic regression analysis was used to model reintegration to profession. Affected patients were classified as “Reintegrated to profession” if they have earned a month salary after 13 to 24 months rehabilitation post treatment. For unemployed patients along with majority of women who were domestic home makers, reciprocate basic household chores to pre injury injury level were classified as “ Reintegrated “ Fracture classification, Age, Gender, Fracture of Spine , Nerve Injuries of Lumbo Sacral Spine and/or lower limb and work status before injury were considered prognostic covarities. Results: Almost 70 percent of 180 researched patients were classified reintegrated. This means a reduction of 20 percent in employment for Pelvic ring injuries and 10 percent reduction in employment for acetabular fractures post treatment. Employment status before injury was the main predictor for reintegration. Older patients had a less chance to return to work in comparison to young individuals. Patients with Nerve injuries and fracture of spine had almost 80 percent higher risk for not being reintegrated. Conclusion: Acetabular fractures and Pelvic ring injuries currently lead to greater loss of reintegration to profession or domestic work. Trauma to Pelvis and Acetabulum threatens the social security of patients. Therefor rehabilitation and follow up care should be optimized to reduce post traumatic disability and reduce the economic burden of work disability.

In this work, we engineered highly biocompatible and fast absorbent injectable hydrogels derived from norbornene (Nb)-functionalized hyaluronic acid (HA-Nb) and a water-soluble cross-linker possessing tetrazine (Tz) functional groups on both ends of polyethylene glycol (PEG-DTz). The by-product (nitrogen gas) of the inverse electron demand Diels–Alder (IEDDA) cross-linking reaction carved porosity in the resulting hydrogels. By varying the molar ratio of HA-Nb and PEG-DTz (Nb:Tz = 10:10, 10:5, 10:2.5), we were able to formulate hydrogels with tunable porosity, gelation time, mechanical strength, and swelling ratios. The hydrogels formed quickly (gelation time < 100 s), offering a possibility to use them as an injectable drug delivery system. The experimental data showed rapid swelling and a high swelling ratio thanks to the existence of PEG chains and highly porous architectures of the hydrogels. The hydrogels were able to encapsulate a high amount of curcumin (~99%) and released the encapsulated curcumin in a temporal pattern. The PEG-DTz cross-linker, HA-Nb, and the resulting hydrogels showed no cytotoxicity in HEK-293 cells. These fast absorbent hydrogels with excellent biocompatibility fabricated from HA-Nb and the IEDDA click-able cross-linker could be promising drug carriers for injectable drug delivery applications.

The DeepRefiner webserver, freely available at http://watson.cse.eng.auburn.edu/DeepRefiner/, is an interactive and fully configurable online system for high-accuracy protein structure refinement. Fuelled by deep learning, DeepRefiner offers the ability to leverage cutting-edge deep neural network architectures which can be calibrated for on-demand selection of adventurous or conservative refinement modes targeted at degree or consistency of refinement. The method has been extensively tested in the Critical Assessment of Techniques for Protein Structure Prediction (CASP) experiments under the group name ‘Bhattacharya-Server’ and was officially ranked as the No. 2 refinement server in CASP13 (second only to ‘Seok-server’ and outperforming all other refinement servers) and No. 2 refinement server in CASP14 (second only to ‘FEIG-S’ and outperforming all other refinement servers including ‘Seok-server’). The DeepRefiner web interface offers a number of convenient features, including (i) fully customizable refinement job submission and validation; (ii) automated job status update, tracking, and notifications; (ii) interactive and interpretable web-based results retrieval with quantitative and visual analysis and (iv) extensive help information on job submission and results interpretation via web-based tutorial and help tooltips., Graphical Abstract Graphical AbstractDeepRefiner webserver for high-accuracy protein structure refinement by deep network calibration.

In this paper, we investigate two new Dombi aggregation operators on bipolar neutrosophic set namely bipolar neutrosophic Dombi prioritized weighted geometric aggregation (BNDPWGA) and bipolar neutrosophic Dombi prioritized ordered weighted geometric aggregation (BNDPOWGA) by means of Dombi t-norm (TN) and Dombi t-conorm (TCN). We discuss their properties along with proofs and multi-attribute decision making (MADM) methods in detail. New algorithms based on proposed models are presented to solve multi-attribute decision-making (MADM) problems. In contrast, with existing techniques a comparison analysis of proposed methods are also demonstrated to test their validity, accuracy and significance.

Internet of Things (IoT) is an emerging domain in which different devices communicate with each other through minimum human intervention. IoT devices are usually operated in hostile and unattended environments. Moreover, routing in current IoT architecture becomes inefficient due to malicious and unauthenticated nodes’ existence, minimum network lifetime, insecure routing, etc. This paper proposes a lightweight blockchain based authentication mechanism where ordinary sensors’ credentials are stored. As IoT nodes have a short lifespan due to energy depletion, few credentials are stored in the blockchain to achieve lightweight authentication. Moreover, the route calculation is performed by a genetic algorithm enabled software defined network controller, which is also used for on-demand routing to optimize the energy consumption of the nodes in the IoT network. Furthermore, a route correctness mechanism is proposed to check the existence of malicious nodes in the calculated route. Moreover, a detection mechanism is proposed to restrict the malicious nodes’ activities, while a malicious node’s list is maintained in the blockchain, which is used in the route correctness mechanism. The proposed model is evaluated by performing intensive simulations. The effectiveness of the proposed model is depicted in terms of gas consumption, which shows the optimized utilization of resources. The residual energy of the network shows optimized route calculation, while the malicious node detection method shows the number of packets dropped.

Local energy trading has attracted the attention of many researchers as a result of its promising benefits. These benefits include minimizing gas emission, reducing power shortage, and establishing a competitive energy market. However, the energy trading between several prosumers causes trust, security, and privacy challenges in energy systems. On the other hand, a single point of failure and an increase in overall system cost occur when the energy system is managed using a centralized model. Therefore, to tackle the mentioned issues, this work proposes a two-layered secure Peer-to-Peer (P2P) energy trading model based on blockchain. The proposed model has two layers: authentication, and secure energy trading. In the authentication layer, in order to protect the proposed model from impersonation attacks, a mutual authentication process is implemented. In the energy trading layer, a new consensus mechanism is proposed to minimize the number of malicious validators. Afterwards, an incentive-punishment algorithm is introduced to motivate energy prosumers to contribute more energy in the model. Next, a dynamic contract theory based on supply-demand ratio pricing scheme is proposed. The purpose of the proposed pricing scheme is to solve the issues associated with the existing pricing schemes. It also preserves the privacy of the actual energy consumption behavior of the trading participants. Furthermore, a consensus mechanism validators’ selection model is proposed. The aim of the proposed work is to have an efficient and secure P2P energy trading platform. Simulations are executed to show the performance of the proposed model in terms of communication and computational costs, reputation, energy contributed, reward, and prices. The results for the authentication process show 7.45 ms computational cost and 1152 bits communication cost, which are better than the existing works. In the consensus process, 66.67% of the validators are selected to conduct the consensus for every transaction. This selection efficiently improves the consensus process and minimizes the number of malicious validators. In the proposed model, the increase in reward is observed for increased energy contribution, decreased non-malicious transactions and adjustment of energy consumption. The proposed model shows a satisfactory performance in terms of trust, security, and privacy.

Accurate electricity consumption forecasting in the power grids ensures efficient generation and distribution of electricity. Keeping this in mind, the paper introduces a novel deep learning model, termed Gated-FCN, for short-term load forecasting. The key idea is to introduce an automated feature selection and deep learning model for forecasting. The model includes an eight-layered Fully Convolutional Network (FCN-8) in which the hand-crafted feature selection that requires expert domain knowledge is avoided. Furthermore, the model also reduces noise as it learns internal dependencies and the correlation of the time series. Enhanced Bidirectional Gated Recurrent Unit (EBiGRU) is used in combination with FCN-8 to learn long-term temporal dependencies of the time series. Moreover, a weighted averaging mechanism of multiple snapshot models is adopted in the proposed model to assign optimized weights to BiGRU. At the end of FCN-8 and BiGRU, a fully connected dense layer is used that gives final prediction results. Gated-FCN is an end-to-end forecasting model that does not require any other model for enhancing its forecasting efficiency. Different activation functions are initially analyzed to determine how the proposed model learns complex patterns from the time series data. Later, the activation function having the best accuracy is used for forecasting. The proposed model extracts both spatial and temporal features from the data. Furthermore, this paper also provides predictive and exploratory data analyses to assist policymakers in making optimal decisions regarding power production and dispatch. In order to demonstrate the applicability of the proposed technique, the simulations are performed using nine years’ load consumption data taken from Independent System Operators New England (ISO-NE). The comparison with five state-of-the-art techniques is also provided to prove the fact that Gated-FCN gives the best forecasting accuracy as compared to other benchmark techniques in terms of two performance metrics: Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE).

For dealing with the electricity theft detection in the smart grids, this article introduces a hybrid deep learning model. The model tackles various issues such as class imbalance problem, curse of dimensionality and low theft detection rate of the existing models. The model integrates the benefits of both GoogLeNet and gated recurrent unit (GRU). The one dimensional electricity consumption (EC) data is fed into GRU to remember the periodic patterns of electricity consumption. Whereas, GoogLeNet model is leveraged to extract the latent features from the two dimensional weekly stacked EC data. Furthermore, the time least square generative adversarial network (TLSGAN) is proposed to solve the class imbalance problem. The TLSGAN uses unsupervised and supervised loss functions to generate fake theft samples, which have high resemblance with real world theft samples. The standard generative adversarial network only updates the weights of those points that are available at the wrong side of the decision boundary. Whereas, TLSGAN even modifies the weights of those points that are available at the correct side of decision boundary that prevent the model from vanishing gradient problem. Moreover, dropout and batch normalization layers are utilized to enhance model’s convergence speed and generalization ability. The proposed model is compared with different state-of-the-art classifiers including multilayer perceptron (MLP), support vector machine, naive bayes, logistic regression, MLP-long short term memory network and wide and deep convolutional neural network. It outperforms all classifiers by achieving 96% and 97% precision-recall area under the curve and receiver operating characteristics area under the curve, respectively.

Background and Objective: Knowledge of the C-reactive protein trend and deviation from the expected value may give an early indication of a possible postoperative infection. According to previous studies, CRP appears to be a more sensitive and specific marker of postoperative infections than ESR and white cell count. This study was conducted to determine the diagnostic accuracy of C-reactive protein to rule out surgical site infection in patients undergoing hip fracture surgery. Methods: This cross-sectional study was conducted at The Indus Hospital and Health Network, Karachi from July 1, 2018 to February 24, 2020. All operative hip fracture patients aged 11-90 years were included. CRP was done on admission, days 3, 14 and 28. Wound assessment was done using the criteria of the Center of Disease Control and prevention on postoperative days 3, 14 and 28. Data was analyzed using STATA version 16. Results: Out of 152 patients, 11(7.2%) developed infection. One patient (0.7%) presented with the infection on day three post-surgery, eight (5.3%) and two (1.3%) patients on days 14 and 28 respectively. CRP levels at admission had poor diagnostic accuracy for diagnosing infection at 14th and 28th day post-surgery respectively (AUC=0.490 and 0.447). CRP levels measured on post-op Day-3 (cutoff value 230mg/dl) had good diagnostic accuracy for diagnosing infection at 14th and 28th day post-surgery respectively (AUC=0.819 and 0.818). Conclusion: CRP level at post-operative day three is a sensitive indicator of infection after hip fracture surgery. doi: https://doi.org/10.12669/pjms.38.6.5577 How to cite this:Ahmed SK, Shahzad MG, Iftikhar S. Diagnostic accuracy of C-reactive protein to rule out infectious complications following hip fracture surgery. Pak J Med Sci. 2022;38(6):---------. doi: https://doi.org/10.12669/pjms.38.6.5577 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Triple negative or basal-like breast cancer (TNBC) is characterised by aggressive progression, lack of standard therapies and poorer overall survival rates for patients. The bad prognosis, high rate of relapse and resistance against anticancer drugs have been associated with a highly abnormal loss of redox control in TNBC cells. Here, we developed docetaxel (DTX)-loaded micellar-like nanoparticles (MLNPs), designed to address the aberrant TNBC biology through the placement of redox responsive cross-links designed into a terpolymer. The MLNPs were derived from poly(ethyleneglycol)-b-poly(lactide)-co-poly(N3-α-e-caprolactone) with a disulfide linker pendant from the caprolactone regions in order to cross-link adjacent chains. The terpolymer contained both polylactide and polycaprolactone to provide a balance of accessibility to reductive agents necessary to ensure stability in transit, but rapid micellar breakdown and concomitant drug release, when in breast cancer cells with increased levels of reducing agents. The empty MLNPs did not show any cytotoxicity in vitro in 2D monolayers of MDA-MB-231 (triple negative breast cancer), MCF7 (breast cancer) and MCF10A (normal breast epithelial cell line), whereas DTX-loaded reducible crosslinked MLNPs exhibited higher cytotoxicity against TNBC and breast cancer cells which present high intracellular levels of glutathione. Crosslinked and non-crosslinked MLNPs showed high and concentration-dependent cellular uptake in monolayers and tumour spheroids, including when assessed in co-cultures of TNBC cells and cancer-associated fibroblasts. DTX loaded crosslinked MLNPs showed the highest efficacy against 3D spheroids of TNBC, in addition the MLNPs also induced higher levels of apoptosis, as assessed by annexin V/PI assays and increased caspase 3/7 activity in MDA-MB-231 cells in comparison to cells treated with DTX-loaded un-crosslinked MLNP (used as a control) and free DTX. Taken together these data demonstrate that the terpolymer micellar-like nanoparticles with reducible crosslinks have high efficacy in both 2D and 3D in vitro cancer models by targeting the aberrant biology, i.e. loss of redox control of this type of tumour, thus may be promising and effective carrier systems for future clinical applications in TNBC.

In this paper, Dombi t-norm (TN) and Dombi t-conorm (TCN) are used to generate more complex, flexible and feasible operation rules by managing a parameter in bipolar neutrosophic fuzzy (BNF) environment. We introduce the notion of bipolar neutrosophic Dombi weighted geometric aggregation (BNDWGA) and bipolar neutrosophic Dombi ordered weighted geometric aggregation (BNDOWGA) operators. We discuss their different properties along with proofs and also investigate multi-attribute decision making (MADM) methods on basis of propose aggregation operators under bipolar neutrosophic fuzzy (BNF) environment. We propose an algorithm of selection of cultivating crop to explain the proposed methods under bipolar neutrosophic fuzzy (BNF) environment. The effect of parameter on our proposed bipolar neutrosophic Dombi aggregation operators are discussed graphically. Moreover, the comparison of our proposed methods with existing methods to test their suitable preferences are discussed in detail.

In this work, we developed novel stimuli-responsive injectable hydrogels composed of a highly biocompatible cartilage acellularized matrix (CAM) and a water-soluble cross-linker containing a diselenide bridge by using ultrafast norbornene (Nb)-tetrazine (Tz) click chemistry. The cross-linking reaction between the Nb groups of the CAM and Tz groups of the cross-linker evolved nitrogen gas and resulted in injectable hydrogels with highly porous structures. The synthesized hydrogels demonstrated high drug loading efficiencies (up to 93%), good swelling ratios, and useful mechanical properties. The doxorubicin (DOX)-loaded hydrogels released minimal amounts of DOX in the simulated physiological medium; however, sustained release of DOX was detected under reducing conditions, revealing more than 90% DOX release after 96 h. Interestingly, the indocyanine green (ICG)-incorporated hydrogels produced reactive oxygen species upon exposure to NIR light and exhibited burst release (>50% DOX release) of DOX during the first 4 h, followed by a sustained release phase. In vitro cytocompatibility tests showed that the synthesized CAM-Nb and hydrogels are essentially nontoxic to HFF-1 fibroblast cells and human colorectal adenocarcinoma cells (HT-29), indicating their excellent bioorthogonality and biocompatibility. Furthermore, DOX-loaded and DOX + ICG-loaded hydrogels inhibited the metabolic activities of HT-29 cells after GSH or NIR exposure and induced antitumor effects similar to those of free DOX. Therefore, these biocompatible and reduction-responsive injectable hydrogels, which exhibited on-demand drug release after NIR exposure, could be promising candidates for minimally invasive local delivery of cancer therapeutics.

The passengers traveling in aircraft require access to the internet to effectively utilizing their precious in-flight time. Providing internet services to such users with similar specifications (i.e., speed, cost, latency, etc) as that of being provided to the terrestrial users is an open research problem. To this end, various attractive solutions are proposed in the literature which include satellite-based, extended terrestrial networkbased, and ad hoc networking-based solutions – to name a few. In this paper, inspired by the widely deployed terrestrial radio cellular networks, an architectural advancement is proposed which together with ad hoc networking renders airborne internet servicesto remote aircraft. Simulation analysis is performed to evaluate the potential ofproposed network architecture, while two notable routing algorithms are implemented and analyzed. In the conducted analysis, mean End-to-End (E2E) packet delay, Packet Delivery Ratio (PDR), and throughput are used as key performance metrics. Routing protocols from two diverse classes are implemented, which are topology-based and position-based protocols. In a scenario when transmitting message length, bit-rate, coverage radius of nodes, velocity of nodes, and direction of motion of nodes is set as 4000 bytes, 300 Mbps, 140 km, up to 250 m/s, and randomly drawn from the uniformdistribution, respectively, the Greedy Perimeter Stateless Routing (GPSR) algorithm is observed to outperform the Ad-hoc On-demand Distance Vector Routing (AODV) algorithm. However, a converse behaviour is observed along an increase in the network load and the velocity of routing (intermediate) and destination aerial nodes. Moreover, the impact of changes in various other important network and physical parameters on the network performance is also investigated and various useful recommendations are made. The conducted analysis is of high significance for determining a suitable choice of routing protocol in given network and nodes mobility conditions in realtime.