Enhanced brain tumor classification using graph convolutional neural network architecture

The Brain Tumor presents a highly critical situation concerning the brain, characterized by the uncontrolled growth of an abnormal cell cluster. Early brain tumor detection is essential for accurate diagnosis and effective treatment planning. In this paper, a novel Convolutional Neural Network (CNN) based Graph Neural Network (GNN) model is proposed using the publicly available Brain Tumor dataset from Kaggle to predict whether a person has brain tumor or not and if yes then which type (Meningioma, Pituitary or Glioma). The objective of this research and the proposed models is to provide a solution to the non-consideration of non-Euclidean distances in image data and the inability of conventional models to learn on pixel similarity based upon the pixel proximity. To solve this problem, we have proposed a Graph based Convolutional Neural Network (GCNN) model and it is found that the proposed model solves the problem of considering non-Euclidean distances in images. We aimed at improving brain tumor detection and classification using a novel technique which combines GNN and a 26 layered CNN that takes in a Graph input pre-convolved using Graph Convolution operation. The objective of Graph Convolution is to modify the node features (data linked to each node) by combining information from nearby nodes. A standard pre-computed Adjacency matrix is used, and the input graphs were updated as the averaged sum of local neighbor nodes, which carry the regional information about the tumor. These modified graphs are given as the input matrices to a standard 26 layered CNN with Batch Normalization and Dropout layers intact. Five different networks namely Net-0, Net-1, Net-2, Net-3 and Net-4 are proposed, and it is found that Net-2 outperformed the other networks namely Net-0, Net-1, Net-3 and Net-4. The highest accuracy achieved was 95.01% by Net-2. With its current effectiveness, the model we propose represents a critical alternative for the statistical detection of brain tumors in patients who are suspected of having one.

Natural compounds targeting nuclear receptors for effective cancer therapy

Human nuclear receptors (NRs) are a family of forty-eight transcription factors that modulate gene expression both spatially and temporally. Numerous biochemical, physiological, and pathological processes including cell survival, proliferation, differentiation, metabolism, immune modulation, development, reproduction, and aging are extensively orchestrated by different NRs. The involvement of dysregulated NRs and NR-mediated signaling pathways in driving cancer cell hallmarks has been thoroughly investigated. Targeting NRs has been one of the major focuses of drug development strategies for cancer interventions. Interestingly, rapid progress in molecular biology and drug screening reveals that the naturally occurring compounds are promising modern oncology drugs which are free of potentially inevitable repercussions that are associated with synthetic compounds. Therefore, the purpose of this review is to draw our attention to the potential therapeutic effects of various classes of natural compounds that target NRs such as phytochemicals, dietary components, venom constituents, royal jelly-derived compounds, and microbial derivatives in the establishment of novel and safe medications for cancer treatment. This review also emphasizes molecular mechanisms and signaling pathways that are leveraged to promote the anti-cancer effects of these natural compounds. We have also critically reviewed and assessed the advantages and limitations of current preclinical and clinical studies on this subject for cancer prophylaxis. This might subsequently pave the way for new paradigms in the discovery of drugs that target specific cancer types.

Edge detection using fast pixel based matching and contours mapping algorithms

Current methods of edge identification were constrained by issues like lighting changes, position disparity, colour changes, and gesture variability, among others. The aforementioned modifications have a significant impact, especially on scaled factors like temporal delay, gradient data, effectiveness in noise, translation, and qualifying edge outlines. It is obvious that an image's borders hold the majority of the shape data. Reducing the amount of time it takes for image identification, increase gradient knowledge of the image, improving efficiency in high noise environments, and pinpointing the precise location of an image are some potential obstacles in recognizing edges. the boundaries of an image stronger and more apparent locate those borders in the image initially, sharpening it by removing any extraneous detail with the use of the proper filters, followed by enhancing the edge-containing areas. The processes involved in recognizing edges are filtering, boosting, recognizing, and localizing. Numerous approaches have been suggested for the previously outlined identification of edges procedures. Edge detection using Fast pixel-based matching and contours mappingmethods are used to overcome the aforementioned restrictions for better picture recognition. In this article, we are introducing the Fast Pixel based matching and contours mapping algorithms to compare the edges in reference and targeted frames using mask-propagation and non-local techniques. Our system resists significant item visual fluctuation as well as copes with obstructions because we incorporate input from both the first and prior frames Improvement in performance in proposed system is discussed in result section, evidences are tabulated and sketched. Mainly detection probabilities and detection time is remarkably reinforced Effective identification of such things were widely useful in fingerprint comparison, medical diagnostics, Smart Cities, production, Cyber Physical Systems, incorporating Artificial Intelligence, and license plate recognition are conceivable applications of this suggested work.

Underwater Wireless Sensor Networks with RSSI-Based Advanced Efficiency-Driven Localization and Unprecedented Accuracy

Deep-sea object localization by underwater acoustic sensor networks is a current research topic in the field of underwater communication and navigation. To find a deep-sea object using underwater wireless sensor networks (UWSNs), the sensors must first detect the signals sent by the object. The sensor readings are then used to approximate the object's position. A lot of parameters influence localization accuracy, including the number and location of sensors, the quality of received signals, and the algorithm used for localization. To determine position, the angle of arrival (AOA), time difference of arrival (TDoA), and received signal strength indicator (RSSI) are used. The UWSN requires precise and efficient localization algorithms because of the changing underwater environment. Time and position are required for sensor data, especially if the sensor is aware of its surroundings. This study describes a critical localization strategy for accomplishing this goal. Using beacon nodes, arrival distance validates sensor localization. We account for the fact that sensor nodes are not in perfect temporal sync and that sound speed changes based on the medium (water, air, etc.) in this section. Our simulations show that our system can achieve high localization accuracy by accounting for temporal synchronisation, measuring mean localization errors, and forecasting their variation. The suggested system localization has a lower mean estimation error (MEE) while using RSSI. This suggests that measurements based on RSSI provide more precision and accuracy during localization.

Growth, studies of milled and irradiated crystalline samples of DBNT for macro-photonic and electro-mechano functionalities

Single crystals of organic type of NLO crystalline material of DBNT - 8, 9-Dimethoxybenzo[b]naphtho [2,3-d] thiophene are proceeded to be grown by slow evaporation procedure and milled to micro scale and irradiated of Co-60 source of 100 Gy, 500 Gy and 5000 Gy for better scope of classification of system of the monoclinic type of DBNT-pure, micro and irradiated ones. The hardness study specifies the reverse indentation size effect (RISE) with work hardening coefficient above two of all DBNT specimen leads to the micro-tribological workings for springs with proper elastic parameters; the transmittance of DBNT of 5 specimens are 321 nm, 323 nm, 341 nm, 351 nm, and 352 nm for macro, micro, 100 Gy, 500 Gy, 5000 Gy. The photonic utility of identity for 3.86 eV and is 3.8629 eV by the transmittance data. The Non Linear Optical - NLO component this of 1.9, 1.94, 1.95, 1.96, 1.99 times that of KDP from which phase matching provision is enabled the influx property for the DBNT specimens is in the order of microns the (110) and (111) indexing represent for display device configuration. The dielectric behaviour of DBNT shows that polarization properly enabled for all categories by electrical performance, the abnormal variation is due to the vacancies created in the molecule by irradiation.

Ride comfort and segmental vibration transmissibility analysis of an automobile passenger model under whole body vibration

The examination of seated occupants' ride comfort under whole-body vibration is a complex topic that involves multiple factors. Whole-body vibration refers to the mechanical vibration that is transmitted to the entire body through a supporting surface, such as a vehicle seat, when traveling on rough or uneven surfaces. There are several methods to assess ride comfort under whole-body vibration, such as subjective assessments, objective measurements, and mathematical models. Subjective assessments involve asking participants to rate their perceived level of discomfort or satisfaction during the vibration exposure, typically using a numerical scale or questionnaire. Objective measurements include accelerometers or vibration meters that record the actual physical vibrations transmitted to the body during the exposure. Mathematical models use various physiological and biomechanical parameters to predict the level of discomfort based on the vibration data. The examination of seated occupants ride comfort under whole-body vibration has been of great interest for many years. In this paper, a multi-body biomechanical model of a seated occupant with a backrest is proposed to perform ride comfort analysis. The novelty of the present model is that it represents complete passenger by including thighs, legs, and foot which were neglected in the past research. A multi-objective firefly algorithm is developed to evaluate the biomechanical parameters (mass, stiffness and damping) of the proposed model. Based on the optimized parameters, segmental transmissibilities are calculated and compared with experimental readings. The proposed model is then combined with a 7-dofs commercial car model to perform a ride comfort study. The ISO 2631-1:1997 ride comfort standards are used to compare the simulated segmental accelerations. Additionally, the influence of biomechanical parameters on most critical organs is analyzed to improve ride comfort. The outcomes of the analysis reveal that seated occupants perceive maximum vibration in the 3-6 Hz frequency range. To improve seated occupants' ride comfort, automotive designers must concentrate on the pelvis region. The adopted methodology and outcomes are helpful to evaluate protective measures in automobile industries. Furthermore, these procedures may be used to reduce the musculoskeletal disorders in seated occupants.

Morphometric analysis and roosting ecology of bat species Pteropus Medius in Mansehra, Khyber Pakhtunkhwa, Pakistan

Morphometric measurement and roosting ecology of Pteropus medius were aimed to find out in Mansehra district of KP, Pakistan. Total 3149 numbers of bats were found in eight biological spots visited; Baffa Doraha, Darband, Dadar, Jallu, Hazara University, Garhi Habibullah Chattar Plain and Jabori, in total 299 numbers of different species of trees including; Morus alba, Pinus raxburghi, Eucalyptus camaldulensis, Morus nigra, Grevillea robusta, Brousonetia papyrifera, Platanus orientalis, Ailanthus altissima, Hevea brasiliensis and Populus nigra. Morphometric features were measured and found vary according to sex of the bats. The average wing span, wing`s length from tip of wing to neck, from thumb to tip of wing and the body`s length from head and claws were recorded to be 102.98 cm, 49.07cm, 28.7 cm and 22.78 cm respectively in males while 93.67 cm, 44.83cm, 24.78cm and 22.78 cm respectively in female bats. Mean circumference of the body including wings and without wing were measured as 22.78 cm and 17.29 cm in males and that of female were 20.07 cm and 16.9 cm. Average length of thumb 3.64 cm, ear`s length 3.1 cm, snout 5.62cm, eye length were 1.07 cm for both sexes and length between the feet in extended position were16.3 cm. Generally different measurement of males bodies were found to be greater than female such as mean body surface area, mass, volume and pressure were found to be 2691.79 cm2, 855.7gm,1236.4 ml and 295.77 dyne/ c m 3 for male and 2576.46 cm2, 852.71gm,1207 ml and 290.2 dyne/ c m 3 respectively for female. While weight and density for both males and females bats were same with mean of 8.59 newton and 0.701 g/m3. Findings of current reports can add valued information in literature about bats, which can be used for species identification and conservation.

Providing multimedia information to children and young people increases recruitment to trials: pre-planned meta-analysis of SWATs

BACKGROUND

Randomised controlled trials are often beset by problems with poor recruitment and retention. Information to support decisions on trial participation is usually provided as printed participant information sheets (PIS), which are often long, technical, and unappealing. Multimedia information (MMI), including animations and videos, may be a valuable alternative or complement to a PIS. The Trials Engagement in Children and Adolescents (TRECA) study compared MMI to PIS to investigate the effects on participant recruitment, retention, and quality of decision-making.

METHODS

We undertook six SWATs (Study Within A Trial) within a series of host trials recruiting children and young people. Potential participants in the host trials were randomly allocated to receive MMI-only, PIS-only, or combined MMI + PIS. We recorded the rates of recruitment and retention (varying between 6 and 26 weeks post-randomisation) in each host trial. Potential participants approached about each host trial were asked to complete a nine-item Decision-Making Questionnaire (DMQ) to indicate their evaluation of the information and their reasons for participation/non-participation. Odds ratios were calculated and combined in a meta-analysis.

RESULTS

Data from 3/6 SWATs for which it was possible were combined in a meta-analysis (n = 1758). Potential participants allocated to MMI-only were more likely to be recruited to the host trial than those allocated to PIS-only (OR 1.54; 95% CI 1.05, 2.28; p = 0.03). Those allocated to combined MMI + PIS compared to PIS-only were no more likely to be recruited to the host trial (OR = 0.89; 95% CI 0.53, 1.50; p = 0.67). Providing MMI rather than PIS did not impact on DMQ scores. Once children and young people had been recruited to host trials, their trial retention rates did not differ according to intervention allocation.

CONCLUSIONS

Providing MMI-only increased the trial recruitment rate compared to PIS-only but did not affect DMQ scores. Combined MMI + PIS instead of PIS had no effect on recruitment or retention. MMIs are a useful tool for trial recruitment in children and young people, and they could reduce trial recruitment periods.

Patterns, procedures, and indications for pediatric surgery in a Tanzanian Refugee Camp: a 20-year experience

Background

There are 103 million displaced people worldwide, 41% of whom are children. Data on the provision of surgery in humanitarian settings are limited. Even scarcer is literature on pediatric surgery performed in humanitarian settings, particularly protracted humanitarian settings.

Methods

We reviewed patterns, procedures, and indications for pediatric surgery among children in Nyarugusu Refugee Camp using a 20-year retrospective dataset.

Results

A total of 1221 pediatric surgical procedures were performed over the study period. Teenagers between the ages of 12 and 17 years were the most common age group undergoing surgery (n=991; 81%). A quarter of the procedures were performed on local Tanzanian children seeking care in the camp (n=301; 25%). The most common procedures performed were cesarean sections (n=858; 70%), herniorrhaphies (n=197; 16%), and exploratory laparotomies (n=55; 5%). Refugees were more likely to undergo exploratory laparotomy (n=47; 5%) than Tanzanian children (n=7; 2%; p=0.032). The most common indications for exploratory laparotomy were acute abdomen (n=24; 44%), intestinal obstruction (n=10; 18%), and peritonitis (n=9; 16%).

Conclusions

There is a significant volume of basic pediatric general surgery performed in the Nyarugusu Camp. Services are used by both refugees and local Tanzanians. We hope this research will inspire further advocacy and research on pediatric surgical services in humanitarian settings worldwide and illuminate the need for including pediatric refugee surgery within the growing global surgery movement.

Nanocatalysts as fast and powerful medical intervention: Bridging cochlear implant therapies and advanced modelling using Hidden Markov Models (HMMs) for effective treatment of infections

As human population growth and waste from technologically advanced industries threaten to destabilise our delicate ecological equilibrium, the global spotlight intensifies on environmental contamination and climate-related changes. These challenges extend beyond our external environment and have significant effects on our internal ecosystems. The inner ear, which is responsible for balance and auditory perception, is a prime example. When these sensory mechanisms are impaired, disorders such as deafness can develop. Traditional treatment methods, including systemic antibiotics, are frequently ineffective due to inadequate inner ear penetration. Conventional techniques for administering substances to the inner ear fail to obtain adequate concentrations as well. In this context, cochlear implants laden with nanocatalysts emerge as a promising strategy for the targeted treatment of inner ear infections. Coated with biocompatible nanoparticles containing specific nanocatalysts, these implants can degrade or neutralise contaminants linked to inner ear infections. This method enables the controlled release of nanocatalysts directly at the infection site, thereby maximising therapeutic efficacy and minimising adverse effects. In vivo and in vitro studies have demonstrated that these implants are effective at eliminating infections, reducing inflammation, and fostering tissue regeneration in the ear. This study investigates the application of hidden Markov models (HMMs) to nanocatalyst-loaded cochlear implants. The HMM is trained on surgical phases in order to accurately identify the various phases associated with implant utilisation. This facilitates the precision placement of surgical instruments within the ear, with a location accuracy between 91% and 95% and a standard deviation between 1% and 5% for both sites. In conclusion, nanocatalysts serve as potent medicinal instruments, bridging cochlear implant therapies and advanced modelling utilising hidden Markov models for the effective treatment of inner ear infections. Cochlear implants loaded with nanocatalysts offer a promising method to combat inner ear infections and enhance patient outcomes by addressing the limitations of conventional treatments.

Tunable luminescence and electrical properties of cerium doped strontium aluminate (SrAl2O4:Ce3+) phosphors for white LED applications

In new research growth long after glow material is a potential candidate due to its physical properties, chemical stability and wide application in modern solid-state lightning (LED), display devices, dosimetry and sensors. A cerium doped strontium aluminate phosphor (SrAl₂O₄:Ce³⁺) was synthesized by conventional solid-state reaction method. The crystal structure and morphology of phosphors, while doping rare earth metal and lithium metal ion was investigated by using X-ray diffraction, Raman spectroscopy and field emission scanning electron microscopy. Fourier transformed infrared spectrum results of the synthesized phosphor composition conforms the characteristic vibration bands of synthesized phosphor. Surface composition analysis of the prepared samples was examined using X-ray photoelectron spectroscopy. Photoluminescence emission band observed at ∼420 nm, ∼490 nm and ∼610 nm region under the excitation wavelength of 256 nm. Wight light emission was confirmed using the Commission Internationale de L'Eclairage (CIE) chromatic coordinate graph. The correlated colour temperature (CCT) value of 0.5% Ce³⁺ doped SAO of phosphors was calculated is in the range of 1543 K, which is indicated the synthesized phosphors performance as warm white light source. The obtained phosphor has a high dielectric constant and low loss tangent which is useful to optoelectronic devices.

Unsteady ternary hybrid-nanofluid flow over an expanding/shrinking cylinder with multiple slips: A Yamada-Ota model implementation

The primary objective of this investigation is to examine the thermal state of an unsteady ternary hybrid-nanofluid flow over an expanding/shrinking cylinder. The influence of radiation along with a non-uniform thermal source/sink is taken into account to expedite heat distribution. Multiple slips are considered at the cylinder interface. The mathematical model is simplified by incorporating appropriate transformations. A numerical solution is obtained using the bvp4c algorithm. The flow characteristics and behavior of the trihybrid nanoliquid exhibit significant changes when the cylinder expands or contracts. The effects of various emerging parameters are analyzed using graphical representations. The velocity field shows an opposite trend when the unsteadiness and mass transfer parameters are increased. The thermal field improves with higher values of the non-uniform source/sink parameter but deteriorates with an increase in the thermal slip parameter. The drag force increases with higher values of the unsteadiness parameter, while it decreases with amplified values of the mass suction and velocity slip parameters. A strong correlation is observed with previous studies which validates and strengthens the credibility of the present analysis.

Acoustic Wave Reflection in Water Affects Underwater Wireless Sensor Networks

The phenomenon of acoustic wave reflection off fluid-solid surfaces is the focus of this research. This research aims to measure the effect of material physical qualities on oblique incidence acoustic attenuation across a large frequency range. To construct the extensive comparison shown in the supporting documentation, reflection coefficient curves were generated by carefully adjusting the porousness and permeability of the poroelastic solid. The next stage in determining its acoustic response is to determine the pseudo-Brewster angle shift and the reflection coefficient minimum dip for the previously indicated attenuation permutations. This circumstance is made possible by modeling and studying the reflection and absorption of acoustic plane waves encountering half-space and two-layer surfaces. For this purpose, both viscous and thermal losses are taken into account. According to the research findings, the propagation medium has a significant impact on the form of the curve that represents the reflection coefficient, whereas the effects of permeability, porosity, and driving frequency are relatively less significant to the pseudo-Brewster angle and curve minima, respectively. This research additionally found that as permeability and porosity increase, the pseudo-Brewster angle shifts to the left (proportionally to porosity increase) until it reaches a limiting value of 73.4 degrees, and that the reflection coefficient curves for each level of porosity exhibit a greater angular dependence, with an overall decrease in magnitude at all incident angles. These findings are given within the framework of the investigation (in proportion to the increase in porosity). The study concluded that when permeability declined, the angular dependence of frequency-dependent attenuation reduced, resulting in iso-porous curves. The study also discovered that the matrix porosity largely affected the angular dependency of the viscous losses in the range of 1.4 × 10^-14 m² permeability.

Solar Salt with Carbon Nanotubes as a Potential Phase Change Material for High-Temperature Applications: Investigations on Thermal Properties and Chemical Stability

Nano-enhanced phase change materials are highly employed for an enhanced heat-transfer process. The current work reports that the thermal properties of solar salt-based phase change materials were enhanced with carbon nanotubes (CNTs). Solar salt (60:40 of NaNO₃/KNO₃) with a phase change temperature and enthalpy of 225.13 °C and 244.76 kJ/kg, respectively, is proposed as a high-temperature PCM, and CNT is added to improve its thermal conductivity. The ball-milling method was employed to mix CNTs with solar salt at various concentrations of 0.1, 0.3, and 0.5% by weight. SEM images display the even distribution of CNTs with solar salt, with the absence of cluster formations. The thermal conductivity, phase change properties, and thermal and chemical stabilities of the composites were studied before and after 300 thermal cycles. FTIR studies indicated only physical interaction between PCM and CNTs. The thermal conductivity was enhanced with an increase in CNT concentration. The thermal conductivity was enhanced by 127.19 and 125.09% before and after cycling, respectively, in the presence of 0.5% CNT. The phase change temperature decreased by around 1.64% after adding 0.5% CNT, with a decrease of 14.67% in the latent heat during melting. TGA thermograms indicated the weight loss was initiated at about 590 and 575 °C before and after thermal cycling, after which it was rapid with an increase in temperature. Thermal characterization of CNT-enhanced solar salt indicated that the composites could be used as phase change materials for enhanced heat-transfer applications.

NAMSTCD: A Novel Augmented Model for Spinal Cord Segmentation and Tumor Classification Using Deep Nets

Spinal cord segmentation is the process of identifying and delineating the boundaries of the spinal cord in medical images such as magnetic resonance imaging (MRI) or computed tomography (CT) scans. This process is important for many medical applications, including the diagnosis, treatment planning, and monitoring of spinal cord injuries and diseases. The segmentation process involves using image processing techniques to identify the spinal cord in the medical image and differentiate it from other structures, such as the vertebrae, cerebrospinal fluid, and tumors. There are several approaches to spinal cord segmentation, including manual segmentation by a trained expert, semi-automated segmentation using software tools that require some user input, and fully automated segmentation using deep learning algorithms. Researchers have proposed a wide range of system models for segmentation and tumor classification in spinal cord scans, but the majority of these models are designed for a specific segment of the spine. As a result, their performance is limited when applied to the entire lead, limiting their deployment scalability. This paper proposes a novel augmented model for spinal cord segmentation and tumor classification using deep nets to overcome this limitation. The model initially segments all five spinal cord regions and stores them as separate datasets. These datasets are manually tagged with cancer status and stage based on observations from multiple radiologist experts. Multiple Mask Regional Convolutional Neural Networks (MRCNNs) were trained on various datasets for region segmentation. The results of these segmentations were combined using a combination of VGGNet 19, YoLo V2, ResNet 101, and GoogLeNet models. These models were selected via performance validation on each segment. It was observed that VGGNet-19 was capable of classifying the thoracic and cervical regions, while YoLo V2 was able to efficiently classify the lumbar region, ResNet 101 exhibited better accuracy for sacral-region classification, and GoogLeNet was able to classify the coccygeal region with high performance accuracy. Due to use of specialized CNN models for different spinal cord segments, the proposed model was able to achieve a 14.5% better segmentation efficiency, 98.9% tumor classification accuracy, and a 15.6% higher speed performance when averaged over the entire dataset and compared with various state-of-the art models. This performance was observed to be better, due to which it can be used for various clinical deployments. Moreover, this performance was observed to be consistent across multiple tumor types and spinal cord regions, which makes the model highly scalable for a wide variety of spinal cord tumor classification scenarios.

Targeting Autophagy Using Long Non-Coding RNAs (LncRNAs): New Landscapes in the Arena of Cancer Therapeutics

Cancer has become a global health hazard accounting for 10 million deaths in the year 2020. Although different treatment approaches have increased patient overall survival, treatment for advanced stages still suffers from poor clinical outcomes. The ever-increasing prevalence of cancer has led to a reanalysis of cellular and molecular events in the hope to identify and develop a cure for this multigenic disease. Autophagy, an evolutionary conserved catabolic process, eliminates protein aggregates and damaged organelles to maintain cellular homeostasis. Accumulating evidence has implicated the deregulation of autophagic pathways to be associated with various hallmarks of cancer. Autophagy exhibits both tumor-promoting and suppressive effects based on the tumor stage and grades. Majorly, it maintains the cancer microenvironment homeostasis by promoting viability and nutrient recycling under hypoxic and nutrient-deprived conditions. Recent investigations have discovered long non-coding RNAs (lncRNAs) as master regulators of autophagic gene expression. lncRNAs, by sequestering autophagy-related microRNAs, have been known to modulate various hallmarks of cancer, such as survival, proliferation, EMT, migration, invasion, angiogenesis, and metastasis. This review delineates the mechanistic role of various lncRNAs involved in modulating autophagy and their related proteins in different cancers.

Monosodium Glutamate Perturbs Human Trophoblast Invasion and Differentiation through a Reactive Oxygen Species-Mediated Pathway: An In-Vitro Assessment

The overproduction of reactive oxygen species (ROS) has been associated with various human diseases. ROS exert a multitude of biological effects with both physiological and pathological consequences. Monosodium glutamate (MSG), a sodium salt of the natural amino acid glutamate, is a flavor-enhancing food additive, which is widely used in Asian cuisine and is an ingredient that brings out the “umami” meat flavor. MSG consumption in rats is associated with ROS generation. Owing to its consumption as part of the fast-food culture and concerns about its possible effects on pregnancy, we aimed to study the impact of MSG on placental trophoblast cells. MSG exposure influenced trophoblast invasion and differentiation, two of the most critical functions during placentation through enhanced production of ROS. Similar findings were also observed on MSG-treated placental explants, as confirmed by elevated Nrf2 levels. Ultrastructural studies revealed signs of subcellular injury by MSG exposure. Mechanistically, MSG-induced oxidative stress with endoplasmic reticulum stress pathways involving Xbp1s and IRE1α was observed. The effect of MSG through an increased ROS production indicates that its long-term exposure might have adverse health effect by compromising key trophoblast functions.

Nanoparticles in the diagnosis and treatment of cancer metastases: Current and future perspectives

Metastasis accounts for greater than 90% of cancer-related deaths. Despite recent advancements in conventional chemotherapy, immunotherapy, targeted therapy, and their rational combinations, metastatic cancers remain essentially untreatable. The distinct obstacles to treat metastases include their small size, high multiplicity, redundancy, therapeutic resistance, and dissemination to multiple organs. Recent advancements in nanotechnology provide the numerous applications in the diagnosis and prophylaxis of metastatic diseases, including the small particle size to penetrate cell membrane and blood vessels and their capacity to transport complex molecular 'cargo' particles to various metastatic regions such as bones, brain, liver, lungs, and lymph nodes. Indeed, nanoparticles (NPs) have demonstrated a significant ability to target specific cells within these organs. In this regard, the purpose of this review is to summarize the present state of nanotechnology in terms of its application in the diagnosis and treatment of metastatic cancer. We intensively reviewed applications of NPs in fluorescent imaging, PET scanning, MRI, and photoacoustic imaging to detect metastasis in various cancer models. The use of targeted NPs for cancer ablation in conjunction with chemotherapy, photothermal treatment, immuno therapy, and combination therapy is thoroughly discussed. The current review also highlights the research opportunities and challenges of leveraging engineering technologies with cancer cell biology and pharmacology to fabricate nanoscience-based tools for treating metastases.

Comparative Study of Soft Computing and Metaheuristic Models in Developing Reduced Exhaust Emission Characteristics for Diesel Engine Fueled with Various Blends of Biodiesel and Metallic Nanoadditive Mixtures: An ANFIS-GA-HSA Approach

Since the discovery of petrol-based products, a surge in energy-requiring equipment has been established across the world. Recent depletion of the existing crude oil resources has motivated researchers to opt for and analyze potential fuels that could potentially provide a cost-effective and sustainable solution. The current study selects a waste plant known as Eichhornia crassipes through which biodiesel is generated, and its blends are tested in diesel engines for feasibility. Different models using soft computing and metaheuristic techniques are employed for the accurate prediction of performance and exhaust characteristics. The blends are further mixed with nanoadditives, thereby exploring and comparing the changes in performance characteristics. The input attributes considered in the study comprise engine load, blend percentage, nanoparticle concentration, and injection pressure, while the outcomes are brake thermal efficiency, brake specific energy consumption, carbon monoxide, unburnt hydrocarbon, and oxides of nitrogen. Models were further ranked and chosen based on their set of attributes using the ranking technique. The ranking criteria for models were based on cost, accuracy, and skill requirement. The ANFIS harmony search algorithm (HSA) reported a lower error rate, while the ANFIS model reported the lowest cost. The optimal combination achieved was 20.80 kW, 2.48047, 150.501 ppm, 4.05025 ppm, and 0.018326% for brake thermal efficiency (BTE), brake specific energy consumption (BSEC), oxides of nitrogen (NOx), unburnt hydrocarbons (UBHC), and carbon monoxide (CO), respectively, thereby furnishing better results than the adaptive neuro-fuzzy interface system (ANFIS) and the ANFIS-genetic algorithm model. Henceforth, integrating the results of ANFIS with an optimization technique with the harmony search algorithm (HSA) yields accurate results but at a comparatively higher cost.

Artificial Intelligence Based Approach for Classification of Human Activities Using MEMS Sensors Data

The integration of Micro Electronic Mechanical Systems (MEMS) sensor technology in smartphones has greatly improved the capability for Human Activity Recognition (HAR). By utilizing Machine Learning (ML) techniques and data from these sensors, various human motion activities can be classified. This study performed experiments and compiled a large dataset of nine daily activities, including Laying Down, Stationary, Walking, Brisk Walking, Running, Stairs-Up, Stairs-Down, Squatting, and Cycling. Several ML models, such as Decision Tree Classifier, Random Forest Classifier, K Neighbors Classifier, Multinomial Logistic Regression, Gaussian Naive Bayes, and Support Vector Machine, were trained on sensor data collected from accelerometer, gyroscope, and magnetometer embedded in smartphones and wearable devices. The highest test accuracy of 95% was achieved using the random forest algorithm. Additionally, a custom-built Bidirectional Long-Short-Term Memory (Bi-LSTM) model, a type of Recurrent Neural Network (RNN), was proposed and yielded an improved test accuracy of 98.1%. This approach differs from traditional algorithmic-based human activity detection used in current wearable technologies, resulting in improved accuracy.