Pleural Fluid Alkaline Phosphate Levels to Differentiate between Tuberculosis and Malignant Pleural Effusion a Tertiary Care Experience

Introduction: Pleural effusion (PF) is a common clinical presentation in several diseases. Tuberculosis is one of the most frequent causes of exudative pleural effusions in immunocompetent patients. Tuberculosis is the leading cause of morbidity and mortality from an infectious disease in developing countries. Pakistan is ranked fifth in the world in terms of tuberculosis high-burden countries. Various pleural fluid parameters have been used to identify the cause of pleural effusion. It has been discovered that tuberculous pleural effusions had a greater alkaline phosphatase (ALP) concentration than transudative effusions. This study used pleural fluid alkaline phosphatase levels to distinguish between tuberculous pleural effusion and malignant pleural effusion because there is little information from tuberculosis-high burden nations like Pakistan. Study Design: A descriptive cross-sectional study conducted at the Jinnah Postgraduate Medical Center in Karachi between October 2016 and October 2017. Material and Methods: The study comprised all patients who were admitted to the department of chest medicine at Jinnah post graduate medical centre (JPMC) of either gender between the ages of 18 and 70 who had exudative lymphocytic pleural effusions lasting two weeks or more included in the study. Non probability consecutive sampling was used to collect data. Patients who have tonsillitis, pharyngitis, pneumonia, asthma, Chronic obstructive pulmonary disease (COPD), or a history of hemoptysis, Bleeding disorders like, platelet function disorder, thrombocytopenia, Liver cirrhosis and Pregnant women were excluded. Parents’ informed consent was obtained after being informed of the study’s protocol, hazards, and advantages. Each patient had their level of pleural fluid alkaline phosphate (PALP) assessed. In order to evaluate the patient’s pleural effusion, a pre-made questionnaire was used. All the collected data were entered into the SPSS 20. An independent sample t-test was used to recognize alkaline phosphate levels association with pleural fluid secondary to tuberculosis or malignancy. Results: In this Descriptive Cross-Sectional Study, the total of 156 patients with age Mean ± SD of was 41.96 ± 17.05 years. The majority of patients 110 (70.5%) were male and 46 (29.5%) were female. Advanced age was associated with raised pleural fluid alkaline phosphatase. The difference of pleural fluid alkaline phosphate level between tuberculous v/s malignant group was found to be (38.03 ± 45.97) v/s (82.77 ± 61.80) respectively with P-value (P = 0.0001). Conclusion: Malignant pleural effusions had elevated PALP when compared to tuberculous pleural effusions in exudative lymphocytic pleural effusions;better differences are seen in older ages and shorter disease durations.

Effect of Measurement Error on the Multivariate CUSUM Control Chart for Compositional Data

Control charts(CCs)are one of the main tools in Statistical Process Control that have been widely adopted in manufacturing sectors as an effective strategy for malfunction detection throughout the previous decades.Measurement errors(M.E’s)are involved in the quality characteristic of interest,which can effect the CC’s performance.The authors explored the impact of a linearmodel with additive covariate M.E on the multivariate cumulative sum(CUSUM)CC for a specific kind of data known as compositional data(CoDa).The average run length(ARL)is used to assess the performance of the proposed chart.The results indicate that M.E’s significantly affects themultivariate CUSUM-CoDaCCs.The authors haveused theMarkov chainmethod to study the impact of different involved parameters using six different cases for the variance-covariance matrix(VCM)(i.e.,uncorrelated with equal variances,uncorrelated with unequal variances,positively correlated with equal variances,positively correlated with unequal variances,negatively correlatedwith equal variances and negatively correlated with unequal variances).The authors concluded that the error VCM has a negative impact on the performance of themultivariate CUSUM-CoDa CC,as the ARL increases with an increase in the value of the error VCM.The subgroup size m and powering operator b positively impact the proposed CC,as the ARL decreases with an increase in m or b.The number of variables p also has a negative impact on the performance of the proposed CC,as the values of ARL increase with an increase in p.For the implementation of the proposal,two illustrated examples have been reported formultivariate CUSUM-CoDaCCs inthe presence ofM.E’s.Onedealswith themanufacturingprocessof uncoated aspirin tablets,and the other is based on monitoring the machines involved in the muesli manufacturing process.

Malware Detection in Android IoT Systems Using Deep Learning

The Android Operating System(AOS)has been evolving since its inception and it has become one of the most widely used operating system for the Internet of Things(IoT).Due to the high popularity and reliability ofAOS for IoT,it is a target of many cyber-attacks which can cause compromise of privacy,financial loss,data integrity,unauthorized access,denial of services and so on.The Android-based IoT(AIoT)devices are extremely vulnerable to various malwares due to the open nature and high acceptance of Android in the market.Recently,several detection preventive malwares are developed to conceal their malicious activities from analysis tools.Hence,conventional malware detection techniques could not be applied and innovative countermeasures against such anti-detection malwares are indispensable to secure the AIoT.In this paper,we proposed the novel deep learning-based real-time multiclass malware detection techniques for the AIoT using dynamic analysis.The results show that the proposed technique outperforms existing malware detection techniques and achieves detection accuracy up to 99.87%.

Energy Efficient and Intelligent Mosquito Repellent Fuzzy Control System

Mosquitoes are of great concern for occasionally carrying noxious diseases(dengue,malaria,zika,and yellow fever).To control mosquitoes,it is very crucial to effectively monitor their behavioral trends and presence.Traditional mosquito repellent works by heating small pads soaked in repellant,which then diffuses a protected area around you,a great alternative to spraying yourself with insecticide.But they have limitations,including the range,turning them on manually,and then waiting for the protection to kick in when the mosquitoes may find you.This research aims to design a fuzzy-based controller to solve the above issues by automatically determining a mosquito repellent’s speed and active time.The speed and active time depend on the repellent cartridge and the number of mosquitoes.The Mamdani model is used in the proposed fuzzy system(FS).The FS consists of identifying unambiguous inputs,a fuzzification process,rule evaluation,and a defuzzification process to produce unambiguous outputs.The input variables used are the repellent cartridge and the number of mosquitoes,and the speed of mosquito repellent is used as the output variable.The whole FS is designed and simulated using MATLAB Simulink R2016b.The proposed FS is executed and verified utilizing a microcontroller using its pulse width modulation capability.Different simulations of the proposed model are performed in many nonlinear processes.Then,a comparative analysis of the outcomes under similar conditions confirms the higher accuracy of the FS,yielding a maximum relative error of 10%.The experimental outcomes show that the root mean square error is reduced by 67.68%,and the mean absolute percentage error is reduced by 52.46%.Using a fuzzy-based mosquito repellent can help maintain the speed of mosquito repellent and control the energy used by the mosquito repellent.

A Double-Branch Xception Architecture for Acute Hemorrhage Detection and Subtype Classification

This study presents a deep learning model for efficient intracranial hemorrhage(ICH)detection and subtype classification on non-contrast head computed tomography(CT)images.ICH refers to bleeding in the skull,leading to the most critical life-threatening health condition requiring rapid and accurate diagnosis.It is classified as intra-axial hemorrhage(intraventricular,intraparenchymal)and extra-axial hemorrhage(subdural,epidural,subarachnoid)based on the bleeding location inside the skull.Many computer-aided diagnoses(CAD)-based schemes have been proposed for ICH detection and classification at both slice and scan levels.However,these approaches performonly binary classification and suffer from a large number of parameters,which increase storage costs.Further,the accuracy of brain hemorrhage detection in existing models is significantly low for medically critical applications.To overcome these problems,a fast and efficient system for the automatic detection of ICH is needed.We designed a double-branch model based on xception architecture that extracts spatial and instant features,concatenates them,and creates the 3D spatial context(common feature vectors)fed to a decision tree classifier for final predictions.The data employed for the experimentation was gathered during the 2019 Radiologist Society of North America(RSNA)brain hemorrhage detection challenge.Our model outperformed benchmark models and achieved better accuracy in intraventricular(99.49%),subarachnoid(99.49%),intraparenchymal(99.10%),and subdural(98.09%)categories,thereby justifying the performance of the proposed double-branch xception architecture for ICH detection and classification.

Interleukin-1 receptor antagonist:From synthesis to therapeutic applications

The cytokine channel’s mechanism for self-regulation involves the application of antagonistic cytokines that are synthesized to connect to the receptors and release soluble cytokine receptors.The very first receptor antagonist of cytokine that was naturally present was interleukin-1 receptor antagonist(IL-1Ra).The IL-1Ra protein forms are disinfected from supernatants of cultured monocytes on stacked IgG.The family of IL-1 consists of IL-1α,IL-1βand IL-1Ra.Human monocytes regulate the production of IL-Ra.IL-Ra takes part in normal physiological functions by using specific antibodies,and acts as an anti-inflammatory agent.IL-Ra is synthesized in the tissues during the period of active disease and can be systematically measured and/or estimated.Maintenance of the levels of IL-Ra and IL-1 is the main factor for host resistance in patients during diseased conditions,as IL-Ra acts as an inherent regulator of various inflammatory responses.In this article,we focuse on how IL-Ra is synthesized and performs its functions once the inflammatory responses are activated.

Biochemical association between the prevalence of genetic polymorphism and myocardial infarction

Genetic polymorphism has a vital role in the pathogenesis and development of myocardial infarction(MI).Single nucleotide polymorphism at any one of the amino acid sequences can result in a diseased state.A single gene can exhibit genetic polymorphism at more than one position giving rise to different variants.Genetic polymorphism of angiotensinogen(AGT)M235T,AGT T174M,and angiotensin-1-converting enzyme(ACE)I/D,endothelial nitric oxide synthase(eNOS),and methylenetetrahydrofolate reductase(MTHFR)can be a risk factor for MI.However,it is important to study the prevalence of genetic polymorphisms of these genes among different populations.MI is influenced by genetic polymorphism of various genes,including AGT,ACE,eNOS,MTHFR,etc.However,the association of genetic polymorphism of these genes varies among different populations,but different ethnic groups could show contradictory results.These genes have shown a positive association with risks of MI in some populations,whereas the results have not been consistent with every ethnic group.In this article,we have summarized the genetic variations in the aforementioned genes and their association with MI.

Inoculation of Chlorella and Food Waste Improves the Physio-Morphological Features of Red Pepper by Regulating Activating Antioxidant Defense System

Food waste is recognized as a valuable source for potential agricultural applications to supply organic matter and nutrients to arable soil.However,the information on the combined application of food waste and the plant growth-promoting bacterial strain,Chlorella,related to plant metabolic features and sodium chloride content in arable soil is limited.The present study was conducted to investigate the exogenous application of food waste along with Chlorella,which improved the physio-morphological features of red pepper.Our results revealed that this combination enhanced the organic matter in the soil,ultimately improving the fertility rate of the soil,and the physio-morphological features,such as chlorophyll a content(24.5±0.7),root(7.8±0.7)cm and shoot length(12.1±0.7)cm,fresh weight(2.1±0.05)g,dry weight(0.19±0.05)g,mineral contents,and hormonal concentration(ABA by up to 2 folds).The combined treatment also minimized free radicals via the activation of the intrinsic antioxidant series cascade and electrolyte leakage.Our findings showed that adding Chlorella and food wastes improved growth characteristics and can be used as a green bio-fertilizer for sustainable agriculture.

Image-Based Automatic Energy Meter Reading Using Deep Learning

We propose to perform an image-based framework for electrical energy meter reading.Our aim is to extract the image region that depicts the digits and then recognize them to record the consumed units.Combining the readings of serial numbers and energy meter units,an automatic billing system using the Internet of Things and a graphical user interface is deployable in a real-time setup.However,such region extraction and character recognition become challenging due to image variations caused by several factors such as partial occlusion due to dust on the meter display,orientation and scale variations caused by camera positioning,and non-uniform illumination caused by shades.To this end,our work evaluates and compares the stateof-the art deep learning algorithm You Only Look Once(YOLO)along with traditional handcrafted features for text extraction and recognition.Our image dataset contains 10,000 images of electrical energymeters and is further expanded by data augmentation such as in-plane rotation and scaling tomake the deep learning algorithms robust to these image variations.For training and evaluation,the image dataset is annotated to produce the ground truth of all the images.Consequently,YOLO achieves superior performance over the traditional handcrafted features with an average recognition rate of 98%for all the digits.It proves to be robust against the mentioned image variations compared with the traditional handcrafted features.Our proposed method can be highly instrumental in reducing the time and effort involved in the currentmeter reading,where workers visit door to door,take images ofmeters and manually extract readings from these images.

Deep Learning-Based Digital Image Forgery Detection Using Transfer Learning

Deep learning is considered one of the most efficient and reliable methods through which the legitimacy of a digital image can be verified.In the current cyber world where deepfakes have shaken the global community,confirming the legitimacy of a digital image is of great importance.With the advancements made in deep learning techniques,now we can efficiently train and develop state-of-the-art digital image forensic models.The most traditional and widely used method by researchers is convolution neural networks(CNN)for verification of image authenticity but it consumes a considerable number of resources and requires a large dataset for training.Therefore,in this study,a transfer learning based deep learning technique for image forgery detection is proposed.The proposed methodology consists of three modules namely;preprocessing module,convolutional module,and the classification module.By using our proposed technique,the training time is drastically reduced by utilizing the pre-trained weights.The performance of the proposed technique is evaluated by using benchmark datasets,i.e.,BOW and BOSSBase that detect five forensic types which include JPEG compression,contrast enhancement(CE),median filtering(MF),additive Gaussian noise,and resampling.We evaluated the performance of our proposed technique by conducting various experiments and case scenarios and achieved an accuracy of 99.92%.The results show the superiority of the proposed system.

Cellulase Production from Species of Fungi and Bacteria from Agricultural Wastes and Its Utilization in Industry: A Review

In energy deficient world, cellulases play a major role for the production of alternative energy resources utilizing lignocellulosic waste materials for bioethanol and biogas production. This study highlights fungal and bacterial strains for the production of cellulases and its industrial applications. Solid State Fermentation (SSF) is more suitable process for cellulase production as compared to submerge fermentation techniques. Fungal cellulosomes system for the production of cellulases is more desirable and resistant to harsh environmental conditions. Trichoderma species are considered as most suitable candidate for cellulase production and utilization in industry as compared to Aspergillus and Humicola species. However, genetically modified strains of Aspergillus have capability to produce cellulase in relatively higher amount. Bacterial cellulase are more resistant to alkaline and thermophile conditions and good candidate in laundries. Cellulases are used in variety of industries such as textile, detergents and laundries, food industry, paper and pulp industry and biofuel production. Thermally stable modified strains of fungi and bacteria are good future prospect for cellulase production.

Hepatitis B virus therapy:What's the future holding for us?

Hepatitis B is one of the leading causes of liver cancer worldwide and unfortunately the number of people affected with hepatitis B virus(HBV) infection is still on the rise. Although the HBV has been known to cause fatal illness since decades but the population effected by this lethal virus have still only a few options for its management. The major treatment strategies include interferons and nucleos(t)ide analogues. These agents have so far produced unsatisfactory results in terms of complete virus eradication. Interferons cannot be used for long term therapy because of their potential side effects. Prolong treatment with nucleos(t)ide analogues has also been reported to cause serious side effects besides the increasing resistance by the virus. The need for new innovative solutions for treatment of HBV has been realized by global research institutes and pharmaceutical industry. Present review focuses in detail on the new ideas that are being transformed into therapeutic tools for use as future therapies in HBV infection. Modern drug designing and screening methods have made the drug discovery process shorter and more reliable. HBV therapeutics will take a new turn in coming years owing to these intelligent drug designing and screening methods. Future therapy of HBV is aiming to include the use of vaccines(both prophylactic and therapeutic), immunomodulators such as antibodies, non-nucleoside antivirals such as RNAi and inhibitors of viral life cycle.

Hepatitis C virus NS5A promotes insulin resistance through IRS-1 serine phosphorylation and increased gluconeogenesis

AIM: To investigate the mechanisms of insulin resistance in human hepatoma cells expressing hepatitis C virus(HCV) nonstructural protein 5A(NS5A).METHODS: The human hepatoma cell lines,Huh7 and Huh7.5,were infected with HCV or transientlytransfected with a vector expressing HCV NS5 A. The effect of HCV NS5 A on the status of the critical players involved in insulin signaling was analyzed using realtime quantitative polymerase chain reaction and Western blot assays. Data were analyzed using Graph Pad Prism version 5.0.RESULTS: To investigate the effect of insulin treatment on the players involved in insulin signaling pathway,we analyzed the status of insulin receptor substrate-1(IRS-1) phosphorylation in HCV infected cells or Huh7.5 cells transfected with an HCV NS5 A expression vector. Our results indicated that there was an increased phosphorylation of IRS-1(Ser307) in HCV infected or NS5 A transfected Huh7.5 cells compared to their respective controls. Furthermore,an increased phosphorylation of Akt(Ser473) was observed in HCV infected and NS5 A transfected cells compared to their mock infected cells. In contrast,we observed decreased phosphorylation of Akt Thr308 phosphorylation in HCV NS5 A transfected cells. These results suggest that Huh7.5 cells either infected with HCV or ectopically expressing HCV NS5 A alone have the potential to induce insulin resistance by the phosphorylation of IRS-1 at serine residue(Ser307) followed by decreased phosphorylation of Akt Thr308,Fox01 Ser256 and GSK3β Ser9,the downstream players of the insulin signalingpathway. Furthermore,increased expression of PECK and glucose-6-phosphatase,the molecules involved in gluconeogenesis,in HCV NS5 A transfected cells was observed.CONCLUSION: Taken together,our results suggest the role of HCV NS5 A in the induction of insulin resistance by modulating various cellular targets involved in the insulin signaling pathway.

Dynamic characteristics of nanoindentation in Ni:A molecular dynamics simulation study

In this work,three-dimensional molecular dynamics simulation is carried out to elucidate the nanoindentation behaviour of single crystal Ni.The substrate indenter system is modelled using hybrid interatomic potentials including the manybody potential(embedded atom method) and two-body Morse potential.The spherical indenter is chosen,and the simulation is performed for different loading rates from 10 m/s to 200 m/s.Results show that the maximum indentation load and hardness of the system increase with the increase of velocity.The effect of indenter size on the nanoindentation response is also analysed.It is found that the maximum indentation load is higher for the large indenter whereas the hardness is higher for the smaller indenter.Dynamic nanoindentation is carried out to investigate the behaviour of Ni substrate to multiple loading-unloading cycles.It is observed from the results that the increase in the number of loading unloading cycles reduces the maximum load and hardness of the Ni substrate.This is attributed to the decrease in recovery force due to defects and dislocations produced after each indentation cycle.

Molecular dynamics study of the mechanical characteristics of Ni/Cu bilayer using nanoindentation

In the present work,a three-dimensional molecular dynamics simulation is carried out to perform the nanoindentation experiment on Ni single crystal.The substrate indenter system is modeled using hybrid interatomic potentials including the many-body potential embedded atom method(EAM),and two-body morse potential.To simulate the indentation process,a spherical indenter(diameter = 80 A,1 A=0.1 nm) is chosen.The results show that the mechanical behaviour of a monolithic Ni is not affected by crystalline orientation.To elucidate the effect of a heterogeneous interface, three bilayer interface systems are constructed,namely Ni(100)/Cu(111),Ni(110)/Cu(111),and Ni(111)/Cu(111).The simulations along these systems clearly describe that mechanical behaviour directly depends on the lattice mismatch. The interface with the smaller mismatch between the specified crystal planes is proved to be harder and vice versa.To describe the relationship between film thickness and interface effect,we choose various values of film thickness ranging from 20 A to 50 A to perform the nanoindentation experiment.It is observed that the interface is significant only for the relatively small thickness of film and the separation between interface and the indenter tip.It is shown that with the increase in film thickness,the mechanical behaviour of the film shifts more toward that of monolithic material.

Blockchain-enabled resource management and sharing for 6G communications

The sixth-generation(6G)network must provide better performance than previous generations to meet the requirements of emerging services and applications,such as multi-gigabit transmission rate,higher reliability,and sub-1 ms latency and ubiquitous connection for the Internet of Everything(IoE).However,with the scarcity of spectrum resources,efficient resource management and sharing are crucial to achieving all these ambitious requirements.One possible technology to achieve all this is the blockchain.Because of its inherent properties,the blockchain has recently gained an important position,which is of great significance to the 6G network and other networks.In particular,the integration of the blockchain in 6G will enable the network to monitor and manage resource utilization and sharing efficiently.Hence,in this paper,we discuss the potentials of the blockchain for resource management and sharing in 6G using multiple application scenarios,namely,Internet of things,deviceto-device communications,network slicing,and inter-domain blockchain ecosystems.

Structural, electronic, and optical properties of ZnO_(1-x)Se_x alloys using first-principles calculations

The structural, electronic, and optical properties of binary ZnO, ZnSe compounds, and their ternary ZnO 1-x Se x alloys are computed using the accurate full potential linearized augmented plane wave plus local orbital (FP-LAPW + lo) method in the rocksalt (B1) and zincblende (B3) crystallographic phases. The electronic band structures, fundamental energy band gaps, and densities of states for ZnO 1-x Se x are evaluated in the range 0 ≤ x ≤ 1 using Wu-Cohen (WC) generalized gradient approximation (GGA) for the exchange-correlation potential. Our calculated results of lattice parameters and bulk modulus reveal a nonlinear variation for pseudo-binary and their ternary alloys in both phases and show a considerable deviation from Vegard's law. It is observed that the predicted lattice parameter and bulk modulus are in good agreement with the available experimental and theoretical data. We establish that the composition dependence of band gap is semi-metallic in B1 phase, while a direct band gap is observed in B3 phase. The calculated density of states is described by taking into account the contribution of Zn 3d, O2p, and Se 4s, and the optical properties are studied in terms of dielectric functions, refractive index, reflectivity, and energy loss function for the B3 phase and are compared with the available experimental data.

Multi-objective design optimization of composite submerged cylindrical pressure hull for minimum buoyancy and maximum buckling load capacity

This paper presents the design optimization of composite submersible cylindrical pressure hull subjected to 3 MPa hydrostatic pressure.The design optimization study is conducted for cross-ply layups[0_(s)/90_(t)/0_(u)],[0_(s)/90_(t)/0_(u)]s,[0_(s)/90_(t)]s and[90_(s)/0_(t)]s considering three uni-directional composites,i.e.Carbon/Epoxy,Glass/Epoxy,and Boron/Epoxy.The optimization study is performed by coupling a Multi-Objective Genetic Algorithm(MOGA)and Analytical Analysis.Minimizing the buoyancy factor and maximizing the buckling load factor are considered as the objectives of the optimization study.The objectives of the optimization are achieved under constraints on the Tsai-Wu,Tsai-Hill and Maximum Stress composite failure criteria and on buckling load factor.To verify the optimization approach,optimization of one particular layup configuration is also conducted in ANSYS with the same objectives and constraints.

Design optimization of composite egg-shaped submersible pressure hull for minimum buoyancy factor

This paper describes a design optimization study of the composite egg-shaped submersible pressure hull employing optimization and finite element analysis(FEA)tools as a first attempt to provide an optimized design of the composite egg-shaped pressure hull for manufacturing or further investigations.A total of 15 optimal designs for the composite egg-shaped pressure hull under hydrostatic pressure are obtained in terms of fibers’angles and the number of layers for 5 lay-up arrangements and 3 unidirectional(UD)composite materials.The optimization process is performed utilizing a genetic algorithm and FEA in ANSYS.The minimization of the buoyancy factor eB:FT is selected as the objective for the optimization under constraints on both material failure and buckling strength.Nonlinear buckling analysis is conducted for one optimal design considering both geometric nonlinearity and imperfections.A sensitivity study is also conducted to further investigate the influence of the design variables on the optimal design of the egg-shaped pressure hull.

眼血流的变化对青光眼病情进展的影响（英文）

青光眼是由多种因素引起的神经退行性疾病,眼压过高会损害视神经而导致永久性视力丧失。虽然青光眼的基本病理生理机制尚未确定,但眼组织如视神经,视网膜,脉络膜以及虹膜的血流改变是青光眼发病的重要危险因素。由于不同因素所引发的视神经损害的有限认知,测量方法和治疗方面缺乏,人们对青光眼的理解存在障碍。尽管研究人员在不断地积累证据,力证眼血流的变化在青光眼发病机制中起着重要的作用,但大部分情况下,对于眼血流的变化和青光眼的患病风险之间的关系,他们都持有多样甚至矛盾的结论。本文中,我们回顾了青光眼的不同方面以及眼血流在疾病发展中的影响。