Lung Cancer Classification Using Modified Squeezenet

Lung cancer is the primary cause of mortality in individuals diagnosed with cancer. Detecting and diagnosing lung cancer early significantly reduces the mortality rate. The early diagnosis of lung cancer is greatly facilitated by medical imaging. The recommendation is to undergo a CT scan, as it has a higher probability of detecting lung cancer during its initial phases. The detection of lung cancer greatly depends on the utilization of advanced deep learning technology, specifically convolutional neural networks, which assist in accurately classifying the CT image. This paper proposed a Modified light weight SqueezeNet architecture that mixes bottleneck residual network and fully connected layer along with global average pooling in the original network. This modification enhances the classification performance with a slight rise in computational complexity.  CT images of 330 patients are used as a data set for testing the proposed technique, which is executed in MATLAB 2022a platform. The proposed method can identify lung cancer and categorize it as either malignant or normal with test Accuracy of 95.76%, Recall-92.94%, Precision of 98.75%, Specificity-98.75%, and AUC-0.9977. The Modified SqueezeNet gives better classification performance against the base SqueezeNet model. The proposed method outperforms traditional deep learning networks like AlexNet, ShuffleNet, ResNet-50, and GoogleNet

COVID-19-associated acute kidney injury: Consensus report of the 25th Acute Disease Quality Initiative (ADQI) workgroup

Kidney involvement in patients with coronavirus disease 2019 (COVID-19) is common, and can range from the presence of proteinuria and haematuria to acute kidney injury (AKI) requiring renal replacement therapy (RRT; also known as kidney replacement therapy). COVID-19-associated AKI (COVID-19 AKI) is associated with high mortality and serves as an independent risk factor for all-cause in-hospital death in patients with COVID-19. The pathophysiology and mechanisms of AKI in patients with COVID-19 have not been fully elucidated and seem to be multifactorial, in keeping with the pathophysiology of AKI in other patients who are critically ill. Little is known about the prevention and management of COVID-19 AKI. The emergence of regional \u27surges\u27 in COVID-19 cases can limit hospital resources, including dialysis availability and supplies; thus, careful daily assessment of available resources is needed. In this Consensus Statement, the Acute Disease Quality Initiative provides recommendations for the diagnosis, prevention and management of COVID-19 AKI based on current literature. We also make recommendations for areas of future research, which are aimed at improving understanding of the underlying processes and improving outcomes for patients with COVID-19 AKI

TiO2 nanotubes and mesoporous silica as containers in self-healing epoxy coatings

The potential of inorganic nanomaterials as reservoirs for healing agents is presented here. Mesoporous silica (SBA-15) and TiO2 nanotubes (TNTs) were synthesized. Both epoxy-encapsulated TiO2 nanotubes and amine-immobilized mesoporous silica were incorporated into epoxy and subsequently coated on a carbon steel substrate. The encapsulated TiO2 nanotubes was quantitatively estimated using a ‘dead pore ratio’ calculation. The morphology of the composite coating was studied in detail using transmission electron microscopic (TEM) analysis. The self-healing ability of the coating was monitored using electrochemical impedance spectroscopy (EIS); the coating recovered 57% of its anticorrosive property in 5 days. The self-healing of the scratch on the coating was monitored using Scanning Electron Microscopy (SEM). The results confirmed that the epoxy pre-polymer was slowly released into the crack. The released epoxy pre-polymer came into contact with the amine immobilized in mesoporous silica and cross-linked to heal the scratch.This paper was made possible by PDRA grant # PDRA1-1216-13014 from the Qatar National Research Fund (a member of Qatar Foundation)

Inorganic Porous Materials Based Epoxy Self-Healing Coatings

The long-term stability of protective coating for metal is critically important for structural applications [1, 2]. Self-healing ability extend the service life of protective coatings leading to a significant reduction in maintenance cost for oil and gas pipe lines and structural parts in civil and construction industry. Recently, the self-healing technology based on healing agent loaded containers has been receiving attention [3, 4]. The incorporation of self- healing agent loaded containers into polymer matrix can be carried out using existing blending techniques. Hence, this technology facilitate large-scale application of self-healing materials [5]. Different micro or nano containers has been used for the storage and release of self-healing agents upon specific corrosion triggering conditions (e.g. on pH change) or upon mechanical damage [6]. Polymer capsules, polymer nanofibers, hollow glass bubbles, hollow glass fibers etc. were used by the researchers to load the healing agent inside their cavity. The inorganic particles with nano cavity offers large surface area, high pore volume and good stability favorable for the storage of the healing agents. Moreover, the usage of inorganic nanomaterials as reservoirs for healing agent can eliminate the tedious encapsulation process. The present study aims to use inorganic nanotubes and mesoporous silica as containers for healing agents in epoxy coating. The ability of Halloysite nanotubes (HNT), titanium dioxide (TiO2) nanotube and mesoporous silica to load and release the healing agents are investigated and compared their performance. Among them, Halloysite nanotubes are naturally occurring clay mineral. Meanwhile, TiO2 nanotube and mesoporous silica are synthesised in laboratory and characterised using scanning electron microscopic (SEM), transmission electron microscopic (TEM) techniques and Brunauer-Emmett-Teller (BET) surface area analysis. The morphology of the nanotubes and mesoporous silica are shown in Fig. 1 (in supporting file). In this study, the epoxy pre-polymer and hardener are used as healing agents. Containers loaded with epoxy and hardener can provide a repair system with matching chemical entity with host epoxy coating. Both epoxy encapsulated nanotubes (either Halloysite or TiO2 nanotubes) and amine immobilized mesoporous silica are incorporated into epoxy, followed by the addition of diethylenetriamine curing agent. The mixture is coated on the metal with an average thickness of 300 ?m. The controlled epoxy coatings are also prepared without nanotube and mesoporous silica. Epoxy coating loaded with encapsulated Halloysite nanotubes and immobilized mesoporous silica is abbreviated as 'EP/HNT/SiO2' and the one loaded with encapsulated TiO2 nanotubes and immobilized mesoporous silica is abbreviated as 'EP/ TiO2/SiO2'. The self-healing ability of the scratched coatings is monitored by electrochemical impedance spectroscopy (EIS) in definite time intervals for 5 days. Both EIS bode plots and tafel polarization curves are analysed to observe the self-healing ability of the coatings. For the scratched controlled epoxy coating, after an immersion time of 24 hours, the impedance curve drop to its minimum value over the entire frequency range and on further immersion period the impedance curve remains its minimum value. However, in the case of self-healing coatings, the initially declined impedance value recovers in successive days. The recovery in low frequency impedance values (at 0.01 Hz), which is a direct reflection of the recovery of corrosion resistance of the coating are evaluated. While EP/TiO2/SiO2 coating recovered 57% of its anticorrosive property, the EP/HNT/SiO2 coating recovered only 0.026%. This results suggest that the nature of the nanotubes affect the amount and rate of healing agent released into the scratched area from the tube lumen which itself affect the self-healing ability of the coating. SEM is also used to observe the healed scratches on the coatings. After 96 hours of immersion in 3.5 wt% NaCl solution, the scratches in EP/TiO2/SiO2 self-healing coatings are found to be almost covered. The results confirm the effective self-healing ability of the EP/TiO2/SiO2 coating in which the released epoxy pre-polymer from nanotube lumen get contact with the amine hardener immobilized in mesoporous silica and cross-link to cover the scratch. Acknowledgment: This abstract was made possible by PDRA grant # PDRA1-1216-13014 from the Qatar national research fund (a member of Qatar foundation).qscienc

An atlas of healthy and injured cell states and niches in the human kidney

Understanding kidney disease relies on defining the complexity of cell types and states, their associated molecular profiles and interactions within tissue neighbourhood

A survey on artificial intelligence techniques for various wastewater treatment processes

Pollutant removal percentage is a key parameter for every WWTPs, and it is crucial to predict pollutant removal efficiency. The efficiency of pollutant removal processes can be increased with the help of modeling and its optimization. Statistical models are not practical enough for wastewater treatments due to complicated relationship among input and output parameters. AI models are generally more flexible while modeling complex datasets with missing data and nonlinearities. Many AI techniques are available, and the aim is to sort out the best AI technique to design predictive models for WWTPs. Deep Learning and Ensemble are the main techniques reviewed in this work. The Ensemble Learning models showing the most successful performance among other techniques by generally showed their accuracy and efficiency

A review on predictive models designed from artificial intelligence techniques in the wastewater treatment process

Modeling and optimization of pollutant removal processes are the best solutions to increase the efficiency of wastewater treatment. The relationship between input and output parameters in wastewater treatment processes (WWTP) are complicated. Artificial intelligence (AI) models are generally more flexible when compared with statistical models while modeling complex datasets with nonlinearity and missing data. Studies on AI-based WWTP are increasing day by day. Therefore, it is crucial to review the AI techniques available which are implemented for WWTP. Such a review helps classifying the techniques that are invented and helps to identify challenges as well as gaps for future studies. Lastly, it can sort out the best AI technique to design predictive models for WWTPs

Flexible pressure sensor based on PVDF nanocomposites containing reduced graphene oxide-titania hybrid nanolayers

A novel flexible nanocomposite pressure sensor with a tensile strength of about 47 MPa is fabricated in this work. Nanolayers of titanium dioxide (titania nanolayers, TNL) synthesized by hydrothermal method are used to reinforce the polyvinylidene fluoride (PVDF) by simple solution mixing. A hybrid composite is prepared by incorporating the TNL (2.5 wt %) with reduced graphene oxide (rGO) (2.5 wt %) synthesized by improved graphene oxide synthesis to form a PVDF/rGO-TNL composite. A comparison between PVDF, PVDF/rGO (5 wt %), PVDF/TNL (5 wt %) and PVDF/rGO-TNL (total additives 5 wt %) samples are analyzed for their sensing, thermal and dielectric characteristics. The new shape of additives (with sharp morphology), good interaction and well distributed hybrid additives in the matrix increased the sensitivity by 333.46% at 5 kPa, 200.7% at 10.7 kPa and 246.7% at 17.6 kPa compared to the individual PVDF composite of TNL, confirming its possible application in fabricating low cost and light weight pressure sensing devices and electronic devices with reduced quantity of metal oxides. Increase in the β crystallinity percentage and removal of α phase for PVDF was detected for the hybrid composite and linked to the improvement in the mechanical properties. Tensile strength for the hybrid composite (46.91 MPa) was 115% higher than that of the neat polymer matrix. Improvement in the wettability and less roughness in the hybrid composites were observed, which can prevent fouling, a major disadvantage in many sensor applications.Scopu