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.

Effects of COVID-19 on Synaptic and Neuronal Degeneration

Neurons are the basic building blocks of the human body's neurological system. Atrophy is defined by the disintegration of the connections between cells that enable them to communicate. Peripheral neuropathy and demyelinating disorders, as well as cerebrovascular illnesses and central nervous system (CNS) inflammatory diseases, have all been linked to brain damage, including Parkinson's disease (PD). It turns out that these diseases have a direct impact on brain atrophy. However, it may take some time after the onset of one of these diseases for this atrophy to be clearly diagnosed. With the emergence of the Coronavirus disease 2019 (COVID-19) pandemic, there were several clinical observations of COVID-19 patients. Among those observations is that the virus can cause any of the diseases that can lead to brain atrophy. Here we shed light on the research that tracked the relationship of these diseases to the COVID-19 virus. The importance of this review is that it is the first to link the relationship between the Coronavirus and diseases that cause brain atrophy. It also indicates the indirect role of the virus in dystrophy.

Heat Transfer Characteristics of Fullerene and Titania Nanotube Nanofluids under Agitated Quench Conditions

Distilled water and aqueous fullerene nanofluids having concentrations of 0.02, 0.2, and 0.4 vol % and titania (titanium dioxide, TiO₂) nanofluids of 0.0002, 0.002, and 0.02 vol % were analyzed for heat transfer characteristics. Quenching mediums were stirred at impeller speeds of 0, 500, 1,000, and 1,500 RPMs in a typical Tensi agitation system. During the quenching process, a metal probe made of ISO 9950 Inconel was used to record the temperature history. The inverse heat conduction method was used to calculate the spatial and temporal heat flux. The nanofluid rewetting properties were measured and matched to those of distilled water. The maximum mean heat flux was 3.26 MW/m², and the quickest heat extraction was 0.2 vol % fullerene nanofluid, according to the results of the heat transfer investigation.

Effect of Kariba Weed Biodiesel Blended with n-Pentane on the Chosen Parameters of a Ceramic-Coated Thermal Barrier Direct Injection Diesel Engine

In this experimental investigation, Kariba weed biodiesel (KSB) blended with n-pentane has been tested in conventional and ceramic-coated thermal barrier engines, and the results have been compiled and presented. A single-cylinder, four-stroke, direct injection diesel engine has been used as the test engine with eddy current dynamometer loading as used in the experimental setup. The tests were repeated in various ambient conditions to get an optimal value. Ceramic coating has been done with partially stabilized zirconia by the plasma arc spraying process. Among the quantum of tests conducted, 90% KSB blended with 10% n-pentane showed appreciable results when it was compared with the test fuel (neat diesel). The brake thermal efficiency and brake-specific fuel consumption were found to be better when compared with neat diesel. At increasing load, unburnt hydrocarbon, carbon monoxide, and smoke opacity emissions were appreciably reduced.

Structural Morphology and Optical Properties of Strontium-Doped Cobalt Aluminate Nanoparticles Synthesized by the Combustion Method

The study of structural morphology and the optical properties of nanoparticles produced by combustion methods are gaining significance due to their multifold applications. In this regard, in the present work, the strontium-doped cobalt aluminate nanoparticles were synthesized by utilizing Co_1-xSr_xAl₂O₄ (0 ≤ x ≤ 0.5) L-Alanine as a fuel in an ignition cycle. Subsequently, several characterization studies viz., X-ray diffraction (XRD), energy-dispersive X-ray (EDX) analysis, high-resolution scanning electron microscopy (HRSEM), Fourier transform infrared spectroscopy (FTIR), ultraviolet (UV) spectroscopy and vibrating sample magnetometry (VSM) were accomplished to study the properties of the materials. The XRD analysis confirmed the cubic spinel structure, and the average crystallite size was found to be in the range of 14 to 20 nm using the Debye-Scherrer equation. High-resolution scanning electron microscopy was utilized to inspect the morphology of the Co_1-xSr_xAl₂O₄ (0 ≤ x ≤ 0.5) nanoparticles. Further, EDS studies were accomplished to determine the chemical composition. Kubelka-Munk's approach was used to determine the band gap, and the values were found to be in the range of 3.18-3.32 eV. The energy spectra for the nanoparticles were in the range of 560-1100 cm^-1, which is due to the spinel structure of Sr-doped CoAl₂O₄ nanoparticles. The behavior plots of magnetic induction (M) against the magnetic (H) loops depict the ferromagnetic behavior of the nanomaterials synthesized.

Aggressive and Drug-resistant Pancreatic Cancer: Challenges and Novel Treatment Approaches

Pancreatic cancer, especially pancreatic ductal adenocarcinoma (PDAC), has a mortality rate that is among the highest. The single therapy option that has the potential to be curative for the illness is surgery, which is generally accepted as being the only effective treatment available. This is because most pancreatic tumors are intractable to chemotherapy. Because of the severe nature of the illness that these tumors cause, only around twenty percent of these tumors can be surgically removed when the first symptoms appear. This is due to the harm that these tumors cause. Since surgery in late-stage cases does not usually offer benefits, here we shed light on the molecular mechanisms of the most aggressive pancreatic tumors that are highly resistant to drugs. We also describe two latest novel treatment approaches that are used to combat this fierce tumor: targeting exosome-mediated tumor-enhancement mechanisms and radiation therapy in combination with adverse effect-mitigating agents. Effective treatments for pancreatic cancer are needed to meet this urgent medical need.

Unraveling the Potential Role of NEDD4-like E3 Ligases in Cancer

Cancer is a deadly disease worldwide, with an anticipated 19.3 million new cases and 10.0 million deaths occurring in 2020 according to GLOBOCAN 2020. It is well established that carcinogenesis and cancer development are strongly linked to genetic changes and post-translational modifications (PTMs). An important PTM process, ubiquitination, regulates every aspect of cellular activity, and the crucial enzymes in the ubiquitination process are E3 ubiquitin ligases (E3s) that affect substrate specificity and must therefore be carefully regulated. A surfeit of studies suggests that, among the E3 ubiquitin ligases, neuronal precursor cell-expressed developmentally downregulated 4 (NEDD4)/NEDD4-like E3 ligases show key functions in cellular processes by controlling subsequent protein degradation and substrate ubiquitination. In addition, it was demonstrated that NEDD4 mainly acts as an oncogene in various cancers, but also plays a tumor-suppressive role in some cancers. In this review, to comprehend the proper function of NEDD4 in cancer development, we summarize its function, both its tumor-suppressive and oncogenic role, in multiple types of malignancies. Moreover, we briefly explain the role of NEDD4 in carcinogenesis and progression, including cell survival, cell proliferation, autophagy, cell migration, invasion, metastasis, epithelial-mesenchymal transition (EMT), chemoresistance, and multiple signaling pathways. In addition, we briefly explain the significance of NEDD4 as a possible target for cancer treatment. Therefore, we conclude that targeting NEDD4 as a therapeutic method for treating human tumors could be a practical possibility.

Ocular myasthenia gravis saccades as a measure of extraocular muscle function

Background

It is important to understand the pathophysiology of ocular myasthenia gravis (OMG) to improve treatment.

Aim

To use modern video-oculography to characterise saccadic eye movements in patients with OMG, including anti-AChR, anti-MuSK, anti-LRP4, and seronegative OMG.

Methods

In total, 21 patients with OMG and five age-matched healthy control subjects underwent video-oculography. Participants performed a sequence of horizontal saccades (3 minutes each) at ±5°, ± 10°, and ±20°, followed by 3 minutes of saccades directed at randomly presented targets at ±5°, ± 10°, and ±15°. We recorded the direction, amplitude, duration, peak, and average velocity of each saccade for each task for each participant.

Results

Saccadic amplitude, duration, and average velocity were all lower in OMG patients than in control subjects (p < 0.021). Saccadic amplitude and velocity decreased over time, but this decrease was similar in OMG patients and control subjects. Fixation drift and ocular disparity tended to be greater in OMG patients than in control subjects. Saccadic intrusions occurred more frequently in OMG patients than in control subjects (p < 0.001). No significant effects of time or group by time on fixation drift or ocular disparity were found.

Discussion

Saccadic velocities in OMG patients differed from those in normal control subjects, which suggests that OMG affects fast-twitch fibres, although fast-twitch fibres were still able to generate "twitch" or "quiver" movements in the presence of even severe ophthalmoplegia. Slow-twitch muscle fibres involved in gaze holding were also affected, accounting for increased fixation drift following saccades. Our objective finding of increased fixation drift and a larger number of saccadic intrusions mirror our anecdotal experience of patients with OMG who report significant diplopia despite minimal ophthalmoplegia on examination. Such microsaccades may be a surrogate for compensation of a gaze-holding deficit in MG.

Response Surface Methodology Based Optimization of Test Parameter in Glass Fiber Reinforced Polyamide 66 for Dry Sliding, Tribological Performance

The tribological performance of a glass fiber reinforced polyamide66 (GFRPA66) composite with varying fiber weight percentage (wt.%) [30 wt.% and 35 wt.%] is investigated in this study using a pin-on-disc tribometer. GFRPA66 composite specimens in the form of pins with varying percentages of fiber viz., 30 wt.% and 35 wt.% are fabricated by an injection molding process. Tribological performances, such as coefficient of friction (COF) and the specific wear rate (SWR), are investigated. The factors affecting the wear of GFRPA66 composites [with 30 wt.% and 35 wt.% reinforcements] are identified based on the process parameters such as load, sliding velocity, and sliding distance. Design Expert 13.0 software is used for the experimental data analysis, based on the design of experiments planned in accordance with the central composite design (CCD) of the response surface methodology (RSM) technique. The significance of the obtained results are analyzed using analysis of variance (ANOVA) techniques. To attain minimum SWR and COF, the wear performance is optimized in dry sliding conditions. The analysis of experimental data revealed that SWR and COF increased with increasing load, sliding velocity, and sliding distance for GFRPA66 [30 wt.%], but decreased with increasing polyamide weight percentage. The SWR for a maximum load of 80 N, and for a sliding velocity of 0.22 m/s, and a sliding distance of 3500 m for GFRPA66 composite specimens with 30 wt.% reinforcements are found to be 0.0121 m³/Nm, while the SWR for the same set of parameters for GFRPA66 composite specimens with 35 wt.% reinforcements are found to be 0.0102 m³/Nm. The COF for the GFRPA66 composite specimens with 30 wt.% reinforcements for the above set of parameters is found to be 0.37, while the GFRPA66 composite specimens with 35 wt.% reinforcements showed significant improvement in wear performance with a reduction in COF to 0.25. Finally, using a scanning electron microscope (SEM), the worn surfaces of the GFRPA66 are examined and interpreted.

Demystifying the Functional Role of Nuclear Receptors in Esophageal Cancer

Esophageal cancer (EC), an aggressive and poorly understood disease, is one of the top causes of cancer-related fatalities. GLOBOCAN 2020 reports that there are 544,076 deaths and 604,100 new cases expected worldwide. Even though there are various advancements in treatment procedures, this cancer has been reported as one of the most difficult cancers to cure, and to increase patient survival; treatment targets still need to be established. Nuclear receptors (NRs) are a type of transcription factor, which has a key role in several biological processes such as reproduction, development, cellular differentiation, stress response, immunity, metabolism, lipids, and drugs, and are essential regulators of several diseases, including cancer. Numerous studies have demonstrated the importance of NRs in tumor immunology and proved the well-known roles of multiple NRs in modulating proliferation, differentiation, and apoptosis. There are surplus of studies conducted on NRs and their implications in EC, but only a few studies have demonstrated the diagnostic and prognostic potential of NRs. Therefore, there is still a paucity of the role of NRs and different ways to target them in EC cells to stop them from spreading malignancy. This review emphasizes the significance of NRs in EC by discussing their diverse agonists as well as antagonists and their response to tumor progression. Additionally, we emphasize NRs’ potential to serve as a novel therapeutic target and their capacity to treat and prevent EC.

Oroxylin A: A Promising Flavonoid for Prevention and Treatment of Chronic Diseases

There have been magnificent advancements in the understanding of molecular mechanisms of chronic diseases over the past several years, but these diseases continue to be a considerable cause of death worldwide. Most of the approved medications available for the prevention and treatment of these diseases target only a single gene/protein/pathway and are known to cause severe side effects and are less effective than they are anticipated. Consequently, the development of finer therapeutics that outshine the existing ones is far-reaching. Natural compounds have enormous applications in curbing several disastrous and fatal diseases. Oroxylin A (OA) is a flavonoid obtained from the plants Oroxylum indicum, Scutellaria baicalensis, and S. lateriflora, which have distinctive pharmacological properties. OA modulates the important signaling pathways, including NF-κB, MAPK, ERK1/2, Wnt/β-catenin, PTEN/PI3K/Akt, and signaling molecules, such as TNF-α, TGF-ꞵ, MMPs, VEGF, interleukins, Bcl-2, caspases, HIF-1α, EMT proteins, Nrf-2, etc., which play a pivotal role in the molecular mechanism of chronic diseases. Overwhelming pieces of evidence expound on the anti-inflammatory, anti-bacterial, anti-viral, and anti-cancer potentials of this flavonoid, which makes it an engrossing compound for research. Numerous preclinical and clinical studies also displayed the promising potential of OA against cancer, cardiovascular diseases, inflammation, neurological disorders, rheumatoid arthritis, osteoarthritis, etc. Therefore, the current review focuses on delineating the role of OA in combating different chronic diseases and highlighting the intrinsic molecular mechanisms of its action.

Machine Learning Techniques for Evaluating Concrete Strength with Waste Marble Powder

The purpose of the research is to predict the compressive and flexural strengths of the concrete mix by using waste marble powder as a partial replacement of cement and sand, based on the experimental data that was acquired from the laboratory tests. In order to accomplish the goal, the models of Support vector machines, Support vector machines with bagging and Stochastic, Linear regression, and Gaussian processes were applied to the experimental data for predicting the compressive and flexural strength of concrete. The effectiveness of models was also evaluated by using statistical criteria. Therefore, it can be inferred that the gaussian process and support vector machine methods can be used to predict the respective outputs, i.e., flexural and compressive strength. The Gaussian process and Support vector machines Stochastic predicts better outcomes for flexural and compressive strength because it has a higher coefficient of correlation (0.8235 and 0.9462), lower mean absolute and root mean squared error values as (2.2808 and 1.8104) and (2.8527 and 2.3430), respectively. Results suggest that all applied techniques are reliable for predicting the compressive and flexural strength of concrete and are able to reduce the experimental work time. In comparison to input factors for this data set, the number of curing days followed by the CA, C, FA, w, and MP is essential in predicting the flexural and compressive strength of a concrete mix for this data set.

The Impact of Three Communication Channels on the Dissemination of a Serious Game Designed to Enhance COVID-19 Prevention

Infection prevention interventions can only be effective if they are both well known and easily accessible. A randomized controlled trial showed that a serious game, "Escape COVID-19", was significantly more effective at improving the intention of adopting adequate infection prevention behavior than regular guidelines among long-term care facility employees. However, less than a fifth of all potential participants were finally recruited in this study. To determine whether a specific communication intervention was more effective than another, we carried out a retrospective analysis of account creation data over a six-month period. During the first period (53 days), information about the serious game was disseminated by a part-time worker. The second period (15 days) corresponded to a press release, while the third period (15 days) reflected an official communication from the Swiss Federal Office of Public Health. A total of 3995 accounts were created during the study period. Most accounts were created by health care workers (2699/3995, 67.6%). Median daily account creation was highest during the press release period (25; Q1:Q3 9:172) and lowest during the official communication period (6; Q1:Q3 4:20). The association between communication intervention and account creation was statistically significant both when considering the overall population (p = 0.013) and when only analyzing health care workers (p = 0.036).

Prediction of hospital-onset COVID-19 infections using dynamic networks of patient contact: an international retrospective cohort study

BACKGROUND

Real-time prediction is key to prevention and control of infections associated with health-care settings. Contacts enable spread of many infections, yet most risk prediction frameworks fail to account for their dynamics. We developed, tested, and internationally validated a real-time machine-learning framework, incorporating dynamic patient-contact networks to predict hospital-onset COVID-19 infections (HOCIs) at the individual level.

METHODS

We report an international retrospective cohort study of our framework, which extracted patient-contact networks from routine hospital data and combined network-derived variables with clinical and contextual information to predict individual infection risk. We trained and tested the framework on HOCIs using the data from 51 157 hospital inpatients admitted to a UK National Health Service hospital group (Imperial College Healthcare NHS Trust) between April 1, 2020, and April 1, 2021, intersecting the first two COVID-19 surges. We validated the framework using data from a Swiss hospital group (Department of Rehabilitation, Geneva University Hospitals) during a COVID-19 surge (from March 1 to May 31, 2020; 40 057 inpatients) and from the same UK group after COVID-19 surges (from April 2 to Aug 13, 2021; 43 375 inpatients). All inpatients with a bed allocation during the study periods were included in the computation of network-derived and contextual variables. In predicting patient-level HOCI risk, only inpatients spending 3 or more days in hospital during the study period were examined for HOCI acquisition risk.

FINDINGS

The framework was highly predictive across test data with all variable types (area under the curve [AUC]-receiver operating characteristic curve [ROC] 0·89 [95% CI 0·88-0·90]) and similarly predictive using only contact-network variables (0·88 [0·86-0·90]). Prediction was reduced when using only hospital contextual (AUC-ROC 0·82 [95% CI 0·80-0·84]) or patient clinical (0·64 [0·62-0·66]) variables. A model with only three variables (ie, network closeness, direct contacts with infectious patients [network derived], and hospital COVID-19 prevalence [hospital contextual]) achieved AUC-ROC 0·85 (95% CI 0·82-0·88). Incorporating contact-network variables improved performance across both validation datasets (AUC-ROC in the Geneva dataset increased from 0·84 [95% CI 0·82-0·86] to 0·88 [0·86-0·90]; AUC-ROC in the UK post-surge dataset increased from 0·49 [0·46-0·52] to 0·68 [0·64-0·70]).

INTERPRETATION

Dynamic contact networks are robust predictors of individual patient risk of HOCIs. Their integration in clinical care could enhance individualised infection prevention and early diagnosis of COVID-19 and other nosocomial infections.

FUNDING

Medical Research Foundation, WHO, Engineering and Physical Sciences Research Council, National Institute for Health Research (NIHR), Swiss National Science Foundation, and German Research Foundation.

Targeting PD-1/PD-L1 axis as new horizon for ovarian cancer therapy

Ovarian cancer is among the deadliest gynecological cancers and the 7th most commonly occurring cancer in women globally. The 5 year survival rate is estimated to be less than 25 %, as in most cases, diagnosis occurs at an advanced stage. Despite recent advancements in treatment, clinical outcomes still remain poor, thus implicating the need for urgent identification of novel therapeutics for the treatment of this cancer. Ovarian cancer is considered a low immune reactive cancer as the tumor cells express insufficient neoantigens to be recognized by the immune cells and thus tend to escape from immune surveillance. Thus, in the recent decade, immunotherapy has gained significant attention and has rejuvenated the understanding of immune regulation in tumor biology. One of the critical immune checkpoints is programmed cell death-1 (PD-1)/programmed cell death ligand-1 (PD-L1) axis. Engagement of PD-1 to PD-L1 promotes immunologic tolerance and suppresses the effector T cells and maintains tumor Tregs, thus playing a crucial role in enhancing tumor survival. Recent studies are targeted to develop inhibitors that block this signal to augment the anti-tumor activity of immune cells. Also, compared to monotherapy, the combinatorial treatment of immune checkpoint inhibitors with small molecule inhibitors have shown promising results with improved efficacy and acceptable adverse events. The present review provides an overview of the PD-1/PD-L1 axis and role of non-coding RNAs in regulating this axis. Moreover, we have highlighted the various preclinical and clinical investigations on PD-1/PD-L1 immune checkpoint inhibitors and have discussed the limitations of immunotherapies in ovarian cancer.