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Previous studies reported a relationship between thyroid-stimulating hormone (TSH) and low-density lipoprotein cholesterol (LDL-C) levels. In this study, we aim to evaluate the impact of TSH levels on lipid profile in patients with familial hypercholesterolemia (FH) and euthyroid state. Patients were selected from the Isfahan FH registry. The Dutch Lipid Clinic Network (DLCN) criteria are used to detect FH. Patients were classified into no FH, possible FH, probable FH, and definite FH groups based on the DLCN scores. Patients with any cause of secondary hyperlipidemia, including hypothyroidism, were excluded from this study. The study group consisted of 103 patients with possible FH, 25 patients with definite FH, and 63 individuals with no FH. The mean TSH and LDL-C levels among participants were 2.10 ± 1.22 mU/l and 142.17 ± 62.56 mg/dl, respectively. No positive or negative correlation was found between serum TSH and total cholesterol ( P value = 0.438), high-density lipoprotein cholesterol ( P = 0.225 ), triglycerides ( P value = 0.863), and LDL-C ( P value = 0.203). We found no correlation between serum TSH levels and lipid profiles in euthyroid patients with FH.

In 2019, a deadly coronaviral infection (COVID-19) that infected millions of people globally was detected in China. This fatal virus affects the respiratory system and currently spreads to more than 200 nations worldwide. COVID-19 may be found using a chest X-ray scan, a reliable imaging method. Although an expert may examine an X-ray scan manually, this process takes a lot of time. Therefore, deep convolutional neural networks (CNNs) may be utilized to automate this procedure. In this work, at the first step, a novel isolated 19-layer CNN model is developed from scratch to detect chest infections using X-rays. Then, the developed model is reutilized to distinguish the type of chest infection, such as COVID-19, fibrosis, pneumonia, and tuberculosis, using the transfer learning approach. Stochastic gradient descent with momentum is utilized to optimize the model. The proposed multistage framework shows 98.85% and 97% classification accuracies for chest infection detection (binary classification between normal and patient) and four-class subclassification (COVID-19, fibrosis, pneumonia, and tuberculosis) for an online chest X-ray dataset. The reliability of the proposed multistage CNN model was further validated through a new dataset, showing an accuracy of 98.5%. The proposed multistage methodology took minimal training time compared to publically available pretrained models. Therefore, the presented multistage deep learning framework can help doctors in clinical practices.

Nonconventional protein isolates were prepared from defatted oilseeds (sesame, flaxseed, and canola). Bioevaluation was performed via growth study parameters i.e., protein efficiency ratio (PER), net protein ratio (NPR), and relative net protein ratio (RNPR). The highest values for these parameters were recorded in sesame protein isolates (SPIs) followed by canola protein isolates (CPIs) and flaxseed protein isolates (FPIs). Nitrogen balance study parameters represented maximum true digestibility (TD) in SPI trailed by FPI and CPI. However, biological value (BV) was found higher in FPI. Similarly, the highest value for net protein utilization (NPU) was noticed in FPI tracked by SPI and CPI. Moreover, safety assessment of protein isolates was also performed including serum protein and kidney and liver function tests. All these parameters showed nonsignificant variations among the tested protein isolates. The outcomes of research explicated that these protein isolates can play a pivotal role to increase the protein level of individuals.

Background. Despite of having improved endodontic file designs as well as the reinforced metal alloy file structure, intracanal endodontic file separation (EFS) is still a very problematic and worrisome dental incident, which usually occurs without any visible signs of permanent deformation. Further, there have been conflicting reports regarding the clinical significance of leaving separated files within root canals. Aims. The aim of this study was to look into the current perceptions and awareness about file separation during endodontic treatment among the dental house officers (DHOs). Materials and Methods. A novel validated questionnaire comprising of 15 close-ended questions was distributed anonymously via Google Forms through email to 1100 DHOs across Pakistan. The questionnaire consisted of two parts: the first component (Section I) collected demographic data and the second component (Section II) investigated the causes of EFS during root canal treatment. Following the completion of socioeconomic information, including age and gender, the DHOs were asked to answer a few questions about the various reasons for endodontic instrument fracture. Results. A total of 800 responses were recorded, with an effective rate of 72.8%. The majority of the DHOs ( p value < 0.001) perceived that endodontic instrument fracture occurred in the posterior (61.5%) and apical third of the canal (50.5%) and in older permanent dentition (67.3%), possibly due to patient anxiety (62%). Better choice of instrument (61.15%), operators’ experience (95.3%), knowledge (87.5%), and proper root canal cleaning (91.1%) are believed to be the vital steps in reducing endodontic file separation/fracture. Furthermore, majority of them ( p value < 0.001) perceived that stainless steel was a superior alloy for filing instruments. Manual files tend to be more prone to fractures due to repeated use than rotary files. Conclusion. This study demonstrated that young DHOs had adequate knowledge and awareness regarding the potential predisposing factors and handling techniques for EFS. This study thereby provides an evaluating tool to access the insights of the current perceptions and awareness of DHOs concerning EFS.

Millettia speciosa is a plant extensively used as an important component in Chinese herbal medicine and food-based medicines. The present study was carried out to determine the total flavonoid content (TFC), volatile phytoconstituents, and pharmacological activities, i.e., antityrosinase, sunscreen, and anticancer activity, of different fractions of M. speciosa stem. Different organic solvents of increasing polarity, i.e., petroleum ether (PE), ethyl acetate (EtOAc), and methanol (MeOH), were used for extraction. The highest total flavonoid content, i.e., 48.30 ± 0.90 % , was reported for PE extract. Various important phytocomponents were revealed by gas chromatography-mass spectroscopy (GC-MS) analysis. Based on abundance, the major compounds were n-hexadecanoic acid (16.654%), n-hexadecanoic acid (14.808%), and beta-sitosterol (6.298%) for PE, EtOAc, and MeOH extract, respectively. The significant antityrosinase activity, i.e., 70.97 ± 0.66 % , with an IC50 value of 4.58 mg/mL was noted for PE extract followed by EtOAc extract, i.e., 59.84 ± 0.67 % , with IC50 value of 6.10 mg/mL. The maximum sunscreen activity was reported for PE extract exhibiting the maximum absorbance value ( 0.633 ± 0.06 ) in the ultraviolet (UV) region, i.e., UVC, while EtOAc extract showed the second highest level of absorbance in the UVB range, i.e., 0.632 ± 0.07 . The strongest anticancer activity ( 49.73 ± 0.49 % cell viability) towards MCF-7 breast cancer cell line was reported for PE extract with IC50 197.51 μg/mL. Our results confirmed the presence of potential therapeutic components for each extract with significant biological functions, showing the importance of the M. speciosa stem as a source of biomedicine. To our knowledge, this is the first report on M. speciosa stem extending comprehensive research about its phytochemical profile and various significant pharmacological activities.

With an immense increase in Internet multimedia applications over the past few years, digital content such as digital images are stored and shared over global networks, the probability for information leakage and illegal modifications to the digital content is at high risk. These digital images are transferred using the network bandwidth; therefore, secure encryption schemes facilitate both information security and bandwidth issues. Hence, a state-of-the-art lightweight information security methodology is required to address this challenge. The main objective of this work is to develop a lightweight nonlinear mechanism for digital image security using chaos theory. The proposed scheme starts by changing a plain image into an encrypted image to improve its security. A block cipher, using lightweight chaos, has been added to achieve this objective for digital image security. We utilized multiple chaotic maps to generate random keys for each channel. Also, Arnold cat map and chaotic gingerbread map are used to add confusion and diffusion. During the permutation stage, image pixels are permuted, while in diffusion stage, pixels are distorted utilizing gingerbread map to add more security. The proposed scheme has been validated using different security parameter tests such as correlation coefficient tests (CC), whose results have been observed closer to zero and information entropy (IE) value is 7.99, respectively, which is almost equal to the ideal value of 8. Moreover, number of pixels changing rate (NPCR) obtained value is higher than 99.50%, while the unified average changing intensity (UACI) is 33.33. Other parameters such as mean absolute error (MAE), mean square error (MSE), lower value of peak to signal noise ratio (PSNR), structural content (SC), maximum difference (MD), average difference (AD), normalized cross-correlation (NCC), and histogram analysis (HA) is tested. The computed values of the proposed scheme are better. The achieved results after comparison with existing schemes highlight that the proposed scheme is highly secure, lightweight, and feasible for real-time communications.

In general, solving fractional partial differential equations either numerically or analytically is a difficult task. However, mathematicians have tried their best to make the task easy and promoted various techniques for their solutions. In this regard, a very prominent and accurate technique, which is known as the new technique of the Adomian decomposition method, is developed and presented for the solution of the initial-boundary value problem of the diffusion equation with fractional view analysis. The suggested model is an important mathematical model to study the behavior of degrees of memory in diffusing materials. Some important results for the given model at different fractional orders of the derivatives are achieved. Graphs show the obtained results to confirm the accuracy and validity of the suggested technique. These results are in good contact with the physical dynamics of the targeted problems. The obtained results for both fractional and integer orders problems are explained through graphs and tables. Tables and graphs support the physical behavior of each problem and the best of physical analysis. From the results, it is concluded that as the fractional order derivative is changed, the graphs or paths of dynamics are also changed. Therefore, we now choose the best solution or dynamic of the problem at a particular derivative order. It is analyzed that the present technique is one of the best techniques to handle the solutions of fractional partial differential equations having initial and boundary conditions (BCs), which are very rare in literature. Furthermore, a small number of calculations are done to achieve a very high rate of convergence, which is the novelty of the present research work. The proposed method provides the series solution with twice recursive formulae to increase the desired accuracy and is preferred among the best techniques to find the solution of fractional partial differential equations with mixed initials and BCs.

Routing protocols are an essential component of vehicular ad hoc networks. Software-defined networks and name data networks are new aspects of routing that are coming to the fore as Vehicular Ad Hoc Network technology evolves. Data Network-based VANET routing protocols and Software Defined Network-based VANET routing protocols have been developed in recent research. These newly developed protocols must be part of VANET routing protocol surveys. The taxonomy of traditional routing protocols must take these innovations into account. To the best of our knowledge, no such taxonomy exists at the moment. We present a new taxonomy of routing protocols based on the additional routing aspects of Software-Defined Networks and Named Data Networks. All traditional routing protocols are kept in a single category. This research work aims to update the existing taxonomy of routing protocols with the newly adopted aspects of research in routing. Advanced routing schemes are selected for the survey from each category to evaluate new research results in VANET routing. This article also describes future directions for VANET/ITS routing research.

In this paper, a novel multistep ahead predictor based upon a fusion of kernel recursive least square (KRLS) and Gaussian process regression (GPR) is proposed for the accurate prediction of the state of health (SoH) and remaining useful life (RUL) of lithium-ion batteries. The empirical mode decomposition is utilized to divide the battery capacity into local regeneration (intrinsic mode functions) and global degradation (residual). The KRLS and GPR submodels are employed to track the residual and intrinsic mode functions. For RUL, the KRLS predicted residual signal is utilized. The online available experimental battery aging data are used for the evaluation of the proposed model. The comparison analysis with other methodologies (i.e., GPR, KRLS, empirical mode decomposition with GPR, and empirical mode decomposition with KRLS) reveals the distinctiveness and superiority of the proposed approach. For 1-step ahead prediction, the proposed method tracks the trajectory with the root mean square error (RMSE) of 0.2299, and the increase of only 0.2243 RMSE is noted for 30-step ahead prediction. The RUL prediction using residual signal shows an increase of 3 to 5% in accuracy. This proposed methodology is a prospective approach for an efficient battery health prognostic.

Thermal conductivity (TC) of a phase change material (PCM) may be enhanced by distributing nanostructured materials (NSMs) termed nano-PCM. It is critical to accurately estimate the TC of nano-PCM to assess heat transfer during phase transition processes, namely, solidification and melting. Here, we propose Gaussian process regression (GPR) strategies involving four various kernel functions (KFs) (including exponential (E), squared exponential (SE), rational quadratic (RQ), and matern (M)) to predict TC of n-octadecane as a PCM. The accessible computational techniques indicate the accuracy of our proposed GPR model compared to the previously proposed methods. In this research, the foremost forecasting strategy has been considered as a GPR method. This model consists of the matern KF whose R2 values of training and testing phases are 1 and 1, respectively. In the following, a sensitivity analysis (SA) is used to explore the effectiveness of variables in terms of outputs and shows that the temperature (T) of nanofluid (NF) is the most efficient input parameter. The work describes the physical properties of NFs and the parameters that should be determined to optimize their efficiency.

Athletes such as long-distance runners, sprinters, hockey, and/or football players may have proximal hamstring tendinopathy (PHT). Laser therapy has been shown to be effective in tendinopathies. High power laser therapy (HPLT) is used for the treatment of several musculoskeletal conditions; however, its efficacy on PHT has not been investigated. This study is aimed at examining the effects of HPLT on pain and isokinetic peak torque (IPT) in athletes with PHT. The two-arm comparative pretest-posttest experimental design was used with random allocation of 36 athletes aged 18-35 years into two groups (experimental and conventional group). The experimental group included the application of HPLT for 3 weeks. The conventional group included treatment with a conventional physiotherapy program including ultrasound therapy, moist heat pack, and home exercises for a total of 3 weeks. Pain and IPT of the hamstring muscle were measured before and after the application of the intervention. Pain score decreased, and IPT increased significantly ( p < 0.05 ) after application of HPLT, by 61.26% and 13.18%, respectively. In the conventional group, a significant difference ( p < 0.05 ) was observed in pain scores only, which decreased by 41.14%. No significant difference ( p > 0.05 ) was observed in IPT in the conventional group. When HPLT was compared with conventional physiotherapy, a significant difference was found in pain scores only. HPLT for 3 weeks was found to be effective in improving pain in athletes with PHT. However, no significant difference was found between HPLT and conventional physiotherapy (US, moist heat, and home exercises) in improving the IPT of the hamstring muscle.

The sheer complexity of the factors influencing decision-making has required organizations to use a tool to understand the relationships between data and make various appropriate decisions based on the information obtained. On the other hand, agricultural products need proper planning and decision-making, like any country’s economic pillars. This is while the segmentation of customers and the analysis of their behavior in the manufacturing and distribution industries are of particular importance due to the targeted marketing activities and effective communication with customers. Customer segmentation is done using data mining techniques based on the variables of purchase volume, repeat purchase, and purchase value. This article deals with the grouping of agricultural product customers. Based on this, the K-means clustering method is used based on the Davies–Bouldin index. The results show that grouping customers into three clusters can increase their purchase value and customer lifespan.

Cancer is among the major public health problems as well as a burden for Pakistan. About 148,000 new patients are diagnosed with cancer each year, and almost 100,000 patients die due to this fatal disease. Lung, breast, liver, cervical, blood/bone marrow, and oral cancers are the most common cancers in Pakistan. Perhaps smoking, physical inactivity, infections, exposure to toxins, and unhealthy diet are the main factors responsible for the spread of cancer. We preferred a novel four-component mixture model under Bayesian estimation to estimate the average number of incidences and death of both genders in different age groups. For this purpose, we considered 28 different kinds of cancers diagnosed in recent years. Data of registered patients all over Pakistan in the year 2012 were taken from GLOBOCAN. All the patients were divided into 4 age groups and also split based on genders to be applied to the proposed mixture model. Bayesian analysis is performed on the data using a four-component exponential mixture model. Estimators for mixture model parameters are derived under Bayesian procedures using three different priors and two loss functions. Simulation study and graphical representation for the estimates are also presented. It is noted from analysis of real data that the Bayes estimates under LINEX loss assuming Jeffreys’ prior is more efficient for the no. of incidences in male and female. As far as no. of deaths are concerned again, LINEX loss assuming Jeffreys’ prior gives better results for the male population, but for the female population, the best loss function is SELF assuming Jeffreys’ prior.

For the study and valuation of social graphs, which affect an extensive range of applications such as community decision-making support and recommender systems, it is highly recommended to sustain the resistance of a social graph G to active attacks. In this regard, a novel privacy measure, called the k , l -anonymity, is used since the last few years on the base of k -metric antidimension of G in which l is the maximum number of attacker nodes defining the k -metric antidimension of G for the smallest positive integer k . The k -metric antidimension of G is the smallest number of attacker nodes less than or equal to l such that other k nodes in G cannot be uniquely identified by the attacker nodes. In this paper, we consider four families of wheel-related social graphs, namely, Jahangir graphs, helm graphs, flower graphs, and sunflower graphs. By determining their k -metric antidimension, we prove that each social graph of these families is the maximum degree metric antidimensional, where the degree of a vertex is the number of vertices linked with that vertex.

Clostridioides difficile infection (CDI) is a health issue of utmost significance in Europe and North America, due to its high prevalence, morbidity, and mortality rate. The clinical spectrum of CDI is broad, ranging from asymptomatic to deadly fulminant colitis. When associated with chronic kidney disease (CKD), CDI is more prevalent and more severe than in the general population, due to specific risk factors such as impaired immune system, intestinal dysmotility, high antibiotic use leading to disturbed microbiota, frequent hospitalization, and PPI use. We performed a systematic review on the issue of prevention and treatment of CDI in the CKD population, analysing the suitable randomized controlled cohort studies published between 2000 and 2021. The results show that the most important aspect of prevention is isolation and disinfection with chlorine-based solution and hydrogen peroxide vapour to stop the spread of bacteria. In terms of prevention, using Lactobacillus plantarum (LP299v) proved to be more efficient than disinfection measures in transplant patients, leading to higher cure rates and less recurrent episodes of CDI. Treatment with oral fidaxomycin is more effective than with oral vancomycin for the initial episode of CDI in CKD patients. Faecal microbiota transplantation (FMT) is more effective than vancomycin in recurrent CDI in CKD patients. More large-sample RCTs are necessary to conclude on the best treatment and prevention strategy of CDI in CKD patients.