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Research conducted on homogenous populations can lead to biased and misleading findings, impeding the development of effective interventions and treatments for diverse populations. Low participation among minority groups further leads to disparities in access to innovative cancer care and treatment outcomes associated with trial participation. To better understand cancer patients’ attitudes and willingness to participate in clinical trials, solid tumor patients attending their clinic visits were invited to complete a survey. The survey included questions on demographics, previous trial participation, and future trial interest. Responses were analyzed with frequency tables and chi-square tests. Of 300 participants, only 96 (32%) were asked to participate in a clinical trial. Of these, 81 (84%) chose to participate and 15 (16%) did not. There were notable differences by race but not gender or education level. Of the 204 who had never been asked to participate, 70% indicated that they would be willing to participate in future, and there was a strong sex–race interaction. Non-White males were the most hesitant group. Of 204, 99% indicated that they would participate to access new treatments, and 57% would participate to contribute to research overall. This study shows that many solid tumor patients undergoing treatment are not offered clinical trials. Racial differences in attitudes toward trial participation are evident. Nonetheless, many patients are willing to participate in trials to access innovative treatments and to support research. Culturally relevant outreach to build trust with minority groups is needed to increase overall participation in clinical trials.

This research paper investigates the effectiveness of a virtual reality (VR) game-based intervention using real-time biofeedback for stress management and performance among fire-fighting trainees from the Singapore Civil Defence Force (SCDF). Forty-seven trainees were enrolled in this study and randomly assigned into three groups: control, placebo, and intervention. The participants’ physiological responses, psychological responses, and training performances were evaluated during specific times over the standard 22-week training regimen. Participants from the control and placebo groups showed a similar overall perceived stress profile, with an initial increase in the early stages that was subsequently maintained over the remaining training period. Participants from the intervention group had a significantly lower level of perceived stress compared to the control and placebo groups, and their stress-is-enhancing mindset was significantly increased before the game in week 12 compared to week 3. Cortisol levels remained comparable between pre-game and post-game for the placebo group at week 12, but there was a significant reduction in cortisol levels post-game in comparison to pre-game for the intervention group. The biofeedback data as a measurement of root mean square of successive differences (RMSSD) during the gameplay were also significantly increased at week 12 when compared to week 3. Notably, the intervention group had a significant improvement in the final exercise assessment when compared to the control based on the participants’ role as duty officers. In conclusion, a VR game-based intervention with real-time biofeedback shows promise as an engaging and effective way of training firefighting trainees to enhance their stress mindset and reduce their perceived stress, which may enable them to perform better in the daily emergencies that they respond to.

Chimeric Antigen Receptor T-cell (CAR-T) therapy uses genetically engineered T-cells with specific binding sites. This therapy allows for tumor specificity and durable treatment responses for patients with hematological malignancies. In this review, we study the risk of venous thromboembolism (VTE) associated with CAR-T therapy. We searched the National Institutes of Health library, Cochrane Library Databases, ClinicalTrials.gov database, and medical literature search engines PubMed and Google Scholar for Phase 2 and Phase 3 drug-efficacy and safety trials to determine the aggregate incidence and risk of VTE treated with CAR-T. Of 1127 search results, nine studies were identified and included in our meta-analysis. Of the 1017 patients who received therapy, 805 patients (79.15%) experienced some degree of CRS, and 122 patients (11.9%) experienced severe CRS (higher than grade 3). Only three out of one thousand and seventeen patients were reported to have experienced venous thromboembolism. Our study did not find a statistically significant association between increased VTE incidence (OR = 0.0005, 95% CI [0.0001, 0.0017]) and CRS/ICANS (p < 0.0001). There was a 0.0050 (95% confidence interval [0.0019, 0.0132]) relative risk for VTE. In our study, we did not find a statistically significantly increased risk of developing VTE despite CRS and underlying malignancy, which have been associated with increased risk of VTE.

The β-cells within the pancreas play a pivotal role in insulin production and secretion, responding to fluctuations in blood glucose levels. However, factors like obesity, dietary habits, and prolonged insulin resistance can compromise β-cell function, contributing to the development of Type 2 Diabetes (T2D). A critical aspect of this dysfunction involves β-cell dedifferentiation and transdifferentiation, wherein these cells lose their specialized characteristics and adopt different identities, notably transitioning towards progenitor or other pancreatic cell types like α-cells. This process significantly contributes to β-cell malfunction and the progression of T2D, often surpassing the impact of outright β-cell loss. Alterations in the expressions of specific genes and transcription factors unique to β-cells, along with epigenetic modifications and environmental factors such as inflammation, oxidative stress, and mitochondrial dysfunction, underpin the occurrence of β-cell dedifferentiation and the onset of T2D. Recent research underscores the potential therapeutic value for targeting β-cell dedifferentiation to manage T2D effectively. In this review, we aim to dissect the intricate mechanisms governing β-cell dedifferentiation and explore the therapeutic avenues stemming from these insights.

Fermented foods containing probiotic Leuconostoc mesenteroides 201607 (LM) were used to extract exopolysaccharides. An incomplete understanding exists regarding the immunomodulatory characteristics of exopolysaccharides (EPSs), which are important constituents of bacterial biofilms. In this instance, we examined the immunomodulatory capacity of EPSs from fermented food extracted from L. mesenteroides 201607. Partially purified exopolysaccharide from L. mesenteroides 201607 (PP-LMEPS) consists of glucose (57.1%), rhamnose (29.53%), and galactose (13.36%). The maximum EPS yield was attained after 30 h of incubation at 37 °C and an initial pH of 7.0. When lipopolysaccharide-stimulated RAW264.7 was exposed to PP-LMEPS, the inflammatory cytokines were considerably decreased or elevated dose-dependently. Upon the exposure of lipopolysaccharide-stimulated RAW264.7 cells to PP-LMEPS, a dose-dependent modulation of inflammatory cytokines was observed. This suggests that the extracted EPS possesses immunomodulatory characteristics, as evidenced by a significant decrease or increase in inflammatory cytokine levels. However, further research is warranted to fully elucidate the precise mechanisms and potential therapeutic implications of the immunomodulatory properties of PP-LMEPS.

In search of novel antidiabetic agents, we synthesized a new series of chalcones with benzimidazole scaffolds by an efficient ‘one-pot’ nitro reductive cyclization method and evaluated their α-glucosidase and α-amylase inhibition studies. The ‘one-pot’ nitro reductive cyclization method offered a simple route for the preparation of benzimidazoles with excellent yield and higher purity compared to the other conventional acid- or base-catalyzed cyclization methods. 1H, 13C NMR, IR, and mass spectrum data were used to characterize the compounds. Single-crystal XRD data confirmed the 3D structure of compound 7c, which was crystalized in the P1¯ space group of the triclinic crystal system. Hirshfeld surface analysis validates the presence of O-H..O, O-H…N, and C-H…O intermolecular hydrogen bonds. From the DFT calculations, the energy gap between the frontier molecular orbitals in 7c was found to be 3.791 eV. From the series, compound 7l emerged as a potent antidiabetic agent with IC50 = 22.45 ± 0.36 µg/mL and 20.47 ± 0.60 µg/mL against α-glucosidase and α-amylase enzymes, respectively. The in silico molecular docking studies revealed that compound 7l has strong binding interactions with α-glucosidase and α-amylase proteins. Molecular dynamics studies also revealed the stability of compound 7l with α-glucosidase and α-amylase proteins.

The purpose of this research is to contribute to the development of approaches for the classification and segmentation of various gastrointestinal (GI) cancer diseases, such as dyed lifted polyps, dyed resection margins, esophagitis, normal cecum, normal pylorus, normal Z line, polyps, and ulcerative colitis. This research is relevant and essential because of the current challenges related to the absence of efficient diagnostic tools for early diagnostics of GI cancers, which are fundamental for improving the diagnosis of these common diseases. To address the above challenges, we propose a new hybrid segmentation model, U-MaskNet, which is a combination of U-Net and Mask R-CNN models. Here, U-Net is utilized for pixel-wise classification and Mask R-CNN for instance segmentation, together forming a solution for classifying and segmenting GI cancer. The Kvasir dataset, which includes 8000 endoscopic images of various GI cancers, is utilized to validate the proposed methodology. The experimental results clearly demonstrated that the novel proposed model provided superior segmentation compared to other well-known models, such as DeepLabv3+, FCN, and DeepMask, as well as improved classification performance compared to state-of-the-art (SOTA) models, including LeNet-5, AlexNet, VGG-16, ResNet-50, and the Inception Network. The quantitative analysis revealed that our proposed model outperformed the other models, achieving a precision of 98.85%, recall of 98.49%, and F1 score of 98.68%. Additionally, the novel model achieved a Dice coefficient of 94.35% and IoU of 89.31%. Consequently, the developed model increased the accuracy and reliability in detecting and segmenting GI cancer, and it was proven that the proposed model can potentially be used for improving the diagnostic process and, consequently, patient care in the clinical environment. This work highlights the benefits of integrating the U-Net and Mask R-CNN models, opening the way for further research in medical image segmentation.

The solid phase byproduct obtained after conventional fucoidan extraction from the brown seaweed Fucus vesiculosus can be used as a source containing alginate. This study involves ultrasound-assisted extraction (UAE) of alginate from the byproduct using sodium bicarbonate. Response surface methodology (RSM) was applied to obtain the optimum conditions for alginate extraction. The ultrasound (US) treatments included 20 kHz of frequency, 20–91% of amplitude, and an extraction time of 6–34 min. The studied investigated the crude alginate yield (%), molecular weight, and alginate content (%) of the extracts. The optimum conditions for obtaining alginate with low molecular weight were found to be 69% US amplitude and sonication time of 30 min. The alginate extracts obtained were characterized using Fourier transform infrared (FTIR) spectroscopy, thermogravimetric analysis (TGA), and differential scanning calorimetry (DSC). Ultrasound-assisted extraction involving a short treatment lasting 6–34 min was found to be effective in extracting alginate from the byproduct compared to the conventional extraction of alginate using stirring at 415 rpm and 60 °C for 24 h. The US treatments did not adversely impact the alginate obtained, and the extracted alginates were found to have similar characteristics to the alginate obtained from conventional extraction and commercial sodium alginate.

We investigate changes in the shape of the averaged pulse profile in PSR B0355+54 (PSR J0358+5413) based on data obtained at the center frequency of 1250 MHz using the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Our dataset consists of 12 non-consecutive observations, each lasting between 1 and 2 h. Considerable variation is observed in the averaged profiles across the observations even though each is folded from thousands of single pulses. Changes in the profile are measured through the ratio (R) between the peak intensities of the leading and trailing components. We find that the averaged pulse profile exhibits significant variation across observations, but distinctive from typical profile mode-changing. By dividing the frequency bandwidth into eight sub-bands, we demonstrate that the shape of the averaged profile undergoes significant evolution with frequency. In general, the changes in R across the sub-bands are different in different observations, but its value is uniform at low frequencies implying a more consistent emission. We demonstrate that the profile stabilization timescale for this pulsar is much longer than commonly suggested for ordinary pulsars, which is likely due to non-uniform and varying arrangement of the emission sources in the emission region.

Fucoidan, a sulphated polysaccharide from brown seaweed composed of several monosaccharides, has been stated to have several bioactive properties such as antioxidant, antiviral, anticancer, antithrombic, anti-inflammatory, and immunomodulatory effects. This paper provides research findings on green extraction methods, structural analysis of fucoidan, and its associated bioactivities. Fucoidans from brown seaweeds, Fucus vesiculosus and Ascophyllum nodosum, were extracted using green solvents such as citric acid (CA) followed by MWCO (molecular weight cut-off) filtration to obtain high-purity polysaccharides. The presence of functional groups typical to fucoidans, namely, fucose, sulfate, and glycosidic bonds, in the extracts were confirmed through the data obtained from FTIR (Fourier-transform infrared spectroscopy), TGA (thermogravimetric analysis), DSC (differential scanning calorimetry), and solid-state CP–MAS (cross-polarization magic angle spinning) analysis. The MWCO analysis identified that the >300 kDa fraction can have better content of fucoidan (FV-CA 79.16%, FV-HCl 63.59%, AN-CA 79.21%, AN-HCl 80.70%) than the conventional extraction process. Furthermore, the >300 kDa fraction showed significantly higher antioxidant activities compared to crude fucoidan extracts. Crude fucoidan extracts showed significant inhibition of cell viability in human lung (A459 lung carcinoma cells) and colorectal adenocarcinoma (Caco-2) cells at higher concentrations. The fucoidan extracted with green solvents and avoiding alcohol-based precipitation has substantial antioxidant/antitumor action, so, due to this activity, it can be employed as functional foods in food applications.

The classification of ECG signals is a critical process because it guides the diagnosis of the proper treatment process for the patient. However, any form of disturbance with ECG signals can be highly conspicuous because of the mechanics involved in data acquisition from living beings, which has a significant impact on the classification procedure. The purpose of this research work is to advance ECG signal classification results by employing numerous denoising methods and, in turn, boost the accuracy of cardiovascular diagnoses. To simulate realistic conditions, we added various types of noise to ECG data, including Gaussian, salt and pepper, speckle, uniform, and exponential noise. To overcome the interference of noise from environments in the obtained ECG signals, we employed wavelet transform, median filter, Gaussian filter, and the hybrid of the wavelet and median filters. The proposed hybrid denoising method has better results than the other methods because of the use of wavelet multi-scale analysis and the ability of the median filter to avoid the loss of vital ECG characteristics. Thus, despite a certain proximity in the values, the hybrid method is significantly more accurate and reliable, as evidenced by the mean squared error (MSE), mean absolute error (MAE), R-squared, and Pearson correlation coefficient. More specifically, the hybrid approach provided an MSE of 0.0012 and an MAE of 0.025, the R-squared value for this study was 0.98, and the Pearson correlation coefficient was 0.99, which provides a very good resemblance to the original ECG confirmation. The proposed classification model is based on the modified lightweight CNN or MLCNN that was trained using the noisy and the denoised data. The findings demonstrated that by applying the denoised data, the testing accuracy, precision, recall, and F1 scores achieved 0.92, 0.91, 0.90, and 0.91 for the datasets, while the noisy data achieved 0.80, 0.78, 0.82, and 0.80, respectively. In this study, the signal quality and denoising methods were found to enhance ECG signal classification and diagnostic accuracy while encouraging proper preprocessing in future studies and applications for real-time ECG for cardiac care.

Background: Hydrogen sulfide (H2S) is an endogenous transmitter with the potential to regulate aqueous humor dynamics and protect retinal neurons from degeneration. The aim of the present study was two-fold: (a) to evaluate the release of H2S from two polysulfides, diallyl disulfide (DADS), and diallyl trisulfide (DATS); and (b) to investigate their ocular hypotensive actions in normotensive male and female rabbits in the presence and absence of GSH. Materials and Methods: H2S was quantified hourly for up to 6 h using a H2S-Biosensor (World Precision Instruments, Sarasota, Fl). Intraocular pressure (IOP) was assessed in normotensive New Zealand Albino rabbits using a pneumotonometer (model 30 classic; Reichert Ophthalmic Instruments, Depew, NY, USA). Results: In the presence of GSH, there was an increase in the in vitro release of H2S produced by DADS and DATS. Both DADS and DATS also caused a dose-dependent reduction in IOP in male and female rabbits, in both treated and untreated eyes. For instance, in male animals, the presence of GSH (3% and 5%) significantly (p < 0.05, n = 5) enhanced the ocular hypotensive action of DADS (2%) and DATS (2%) from 14.02 ± 2.89% to 18.67 ± 5.6% and from 16.22 ± 3.48 to 23.62 ± 5.79%, respectively. Conclusions: GSH enhanced both H2S release and ocular hypotensive action of the polysulfides in a manner that was dependent on the number of sulfur atoms present in each polysulfide. Furthermore, female animals were less sensitive to the IOP-lowering action of the polysulfides, when compared to their male counterparts.

In recent decades, the scientific community has increasingly focused on extreme events linked to climate change, which are leading to more intense and frequent natural disasters. The Mediterranean can be considered a hotspot where the effects of these changes are expected to be more intense compared to other regions of the planet. Italy is not exempt; in fact, with its extensive shoreline, it is particularly vulnerable, especially to high sea levels and coastal erosions. In this framework, from late October to early November 2023, six storm surges occurred in the Gulf of Trieste (NE Italy). These events, characterized by winds from 190°N to 220°N and the significant wave height, which reached up to 1.81 m nearshore—an uncommon meteorological condition in the northern Adriatic Sea—caused the occurrence of eight coastal sinkholes and substantial damages to man-made structures. Thanks to Unmanned Aerial Vehicles (UAVs) and their derived products (high-resolution orthomosaics, Digital Elevation Models—DEMs, and point clouds), it was possible to study these features over time, enabling long-term coastal dynamics monitoring, which can be crucial for timely and effective response and restoration efforts.

COVID-19, caused by SARS-CoV-2, and its variants have spread rapidly across the globe in the past few years, resulting in millions of deaths worldwide. Hematological diseases and complications associated with COVID-19 severely impact the mortality and morbidity rates of patients; therefore, there is a need for oversight on what pharmaceutical therapies are prescribed to hematologically at-risk patients. Thrombocytopenia, hemoglobinemia, leukopenia, and leukocytosis are all seen at increased rates in patients infected with COVID-19 and become more prominent in patients with severe COVID-19. Further, COVID-19 therapeutics may be associated with hematological complications, and this became more important in immunocompromised patients with hematological conditions as they are at higher risk of hematological complications after treatment. Thus, it is important to understand and treat COVID-19 patients with underlying hematological conditions with caution. Hematological changes during COVID-19 infection and treatment are important because they may serve as biomarkers as well as to evaluate the treatment response, which will help in changing treatment strategies. In this literature review, we discuss the hematological complications associated with COVID-19, the mechanisms, treatment groups, and adverse effects of commonly used COVID-19 therapies, followed by the hematological adverse events that could arise due to therapeutic agents used in COVID-19.

Introduction: Cellular senescence is the irreversible growth arrest subsequent to oncogenic mutations, DNA damage, or metabolic insult. Senescence is associated with ageing and chronic age associated diseases such as cardiovascular disease and diabetes. The involvement of cellular senescence in acute kidney injury (AKI) and chronic kidney disease (CKD) is not fully understood. However, recent studies suggest that such patients have a higher-than-normal level of cellular senescence and accelerated ageing. Methods: This study aimed to discover key biomarkers of senescence in AKI and CKD patients compared to other chronic ageing diseases in controls using OLINK proteomics. Results: We show that senescence proteins CKAP4 (p-value < 0.0001) and PTX3 (p-value < 0.0001) are upregulated in AKI and CKD patients compared with controls with chronic diseases, suggesting the proteins may play a role in overall kidney disease development. Conclusions: CKAP4 was found to be differentially expressed in both AKI and CKD when compared to UHCs; hence, this biomarker could be a prognostic senescence biomarker of both AKI and CKD.

Breast cancer is one of the most lethal and widespread diseases affecting women worldwide. As a result, it is necessary to diagnose breast cancer accurately and efficiently utilizing the most cost-effective and widely used methods. In this research, we demonstrated that synthetically created high-quality ultrasound data outperformed conventional augmentation strategies for efficiently diagnosing breast cancer using deep learning. We trained a deep-learning model using the EfficientNet-B7 architecture and a large dataset of 3186 ultrasound images acquired from multiple publicly available sources, as well as 10,000 synthetically generated images using generative adversarial networks (StyleGAN3). The model was trained using five-fold cross-validation techniques and validated using four metrics: accuracy, recall, precision, and the F1 score measure. The results showed that integrating synthetically produced data into the training set increased the classification accuracy from 88.72% to 92.01% based on the F1 score, demonstrating the power of generative models to expand and improve the quality of training datasets in medical-imaging applications. This demonstrated that training the model using a larger set of data comprising synthetic images significantly improved its performance by more than 3% over the genuine dataset with common augmentation. Various data augmentation procedures were also investigated to improve the training set’s diversity and representativeness. This research emphasizes the relevance of using modern artificial intelligence and machine-learning technologies in medical imaging by providing an effective strategy for categorizing ultrasound images, which may lead to increased diagnostic accuracy and optimal treatment options. The proposed techniques are highly promising and have strong potential for future clinical application in the diagnosis of breast cancer.

The development of drug resistance in cancer cells poses a significant challenge for treatment, with nearly 90% of cancer-related deaths attributed to it. Over 50% of ovarian cancer patients and 30–40% of breast cancer patients exhibit resistance to therapies such as Taxol. Previous literature has shown that cytotoxic cancer therapies and ionizing radiation damage tumors, prompting cancer cells to exploit the autotaxin (ATX)–lysophosphatidic acid (LPA)–lysophosphatidic acid receptor (LPAR) signaling axis to enhance survival pathways, thus reducing treatment efficacy. Therefore, targeting this signaling axis has become a crucial strategy to overcome some forms of cancer resistance. Addressing this challenge, we identified and assessed ATX-1d, a novel compound targeting ATX, through computational methods and in vitro assays. ATX-1d exhibited an IC50 of 1.8 ± 0.3 μM for ATX inhibition and demonstrated a significant binding affinity for ATX, as confirmed by MM-GBSA, QM/MM-GBSA, and SAPT in silico methods. ATX-1d significantly amplified the potency of paclitaxel, increasing its effectiveness tenfold in 4T1 murine breast carcinoma cells and fourfold in A375 human melanoma cells without inducing cytotoxic effects as a single agent.

Traditional vaccines can be classified into inactivated vaccines, live attenuated vaccines, and subunit vaccines given orally or via intramuscular (IM) injection or subcutaneous (SC) injection for the prevention of infectious diseases. Recently, recombinant protein vaccines, DNA vaccines, mRNA vaccines, and multiple/alternative administering route vaccines (e.g., microneedle or inhalation) have been developed to make vaccines more secure, effective, tolerable, and universal for the public. In addition to preventing infectious diseases, novel vaccines have currently been developed or are being developed to prevent or cure noninfectious diseases, including cancer. These vaccine platforms have been developed using various biotechnologies such as viral vectors, nanoparticles, mRNA, recombination DNA, subunit, novel adjuvants, and other vaccine delivery systems. In this review, we will explore the development of novel vaccines applying biotechnologies, such as vaccines based on novel administration routes, vaccines based on novel vectors, including viruses and nanoparticles, vaccines applied for cancer prevention, and therapeutic vaccines.

Machine learning, a rapidly growing field, has attracted numerous researchers for its ability to automatically learn from and make predictions based on data. This manuscript presents an innovative approach to estimating the covariance matrix of noise in radar measurements for target tracking, resulting from collaborative efforts. Traditionally, researchers have assumed that the covariance matrix of noise in sonar measurements is present in the vast majority of literature related to target tracking. On the other hand, this research aims to estimate it by employing deep learning algorithms with noisy measurements in range, bearing, and elevation from radar sensors. This collaborative approach, involving multiple disciplines, provides a more precise and accurate covariance matrix estimate. Additionally, the unscented Kalman filter was combined with the gated recurrent unit, multilayer perceptron, convolutional neural network, and long short-term memory to accomplish the task of 3D target tracking in an airborne environment. The quantification of the results was achieved through the use of Monte Carlo simulations, which demonstrated that the convolutional neural network performed better than any other approach. The system was simulated using a Python program, and the proposed method offers higher accuracy and faster convergence time than conventional target tracking methods. This is a demonstration of the potential that collaboration can have in research.

Background: The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, has posed unprecedented challenges to healthcare systems worldwide. Here, we have identified proteomic and genetic signatures for improved prognosis which is vital for COVID-19 research. Methods: We investigated the proteomic and genomic profile of COVID-19-positive patients (n = 400 for proteomics, n = 483 for genomics), focusing on differential regulation between hospitalised and non-hospitalised COVID-19 patients. Signatures had their predictive capabilities tested using independent machine learning models such as Support Vector Machine (SVM), Random Forest (RF) and Logistic Regression (LR). Results: This study has identified 224 differentially expressed proteins involved in various inflammatory and immunological pathways in hospitalised COVID-19 patients compared to non-hospitalised COVID-19 patients. LGALS9 (p-value < 0.001), LAMP3 (p-value < 0.001), PRSS8 (p-value < 0.001) and AGRN (p-value < 0.001) were identified as the most statistically significant proteins. Several hundred rsIDs were queried across the top 10 significant signatures, identifying three significant SNPs on the FSTL3 gene showing a correlation with hospitalisation status. Conclusions: Our study has not only identified key signatures of COVID-19 patients with worsened health but has also demonstrated their predictive capabilities as potential biomarkers, which suggests a staple role in the worsened health effects caused by COVID-19.

During the operation of a reversible pump-turbine, a hump area can easily appear under the pump condition, which will greatly affect the performance of a storage unit, with pressure pulsation being the key factor for the stable operation of a pump-turbine. Therefore, in order to explore the pressure pulsation characteristics of each flow component in the hump area, this paper first compared the full characteristics of the model test under different working conditions, and then it analyzed the pressure pulsation characteristics. By analyzing the pressure pulsation characteristics in the unit’s flow component under different flow rates in the hump area, the pulsation rule of a pump-turbine running in the hump area was revealed. It was found that the peak-to-peak value of the draft tube in the hump area was the smallest under the optimal flow condition, and the peak-to-peak value increased along the flow direction, with the rotor and stator interaction (RSI) effects being continuously enhanced. When away from the runner basin, the influence of RSI gradually weakened after leaving the runner. No low frequency was found in the optimal traffic. The peak-to-peak value of the low flow condition increased compared with the optimal flow condition, and the distribution was not uniform. The main frequency of the whole basin was relatively complex, indicating that the flow of water was unstable in the condition of partial load, resulting in the hump area during the unit operation. The research results can provide a theoretical reference for improving the stability of pump-turbines.

The Module-0 Demonstrator is a single-phase 600 kg liquid argon time projection chamber operated as a prototype for the DUNE liquid argon near detector. Based on the ArgonCube design concept, Module-0 features a novel 80k-channel pixelated charge readout and advanced high-coverage photon detection system. In this paper, we present an analysis of an eight-day data set consisting of 25 million cosmic ray events collected in the spring of 2021. We use this sample to demonstrate the imaging performance of the charge and light readout systems as well as the signal correlations between the two. We also report argon purity and detector uniformity measurements and provide comparisons to detector simulations.

Rice blast is one of the most devastating biotic stresses that limits rice productivity. The North Eastern Hill (NEH) region of India is considered to be one of the primary centres of diversity for both rice and pathotypes of Magnaporthe grisea. Therefore, the present study was carried out to elucidate the genetic basis of leaf and neck blast resistance under Meghalaya conditions. A set of 80 diverse genotypes (natural population) and 2 F2 populations involving resistant parent, a wildtype landrace, LR 5 (Lal Jangali) and susceptible genotypes Sambha Mahsuri SUB 1 (SMS) and LR 26 (Chakhao Poireiton) were used for association analysis of reported major gene-linked markers with leaf and neck blast resistance to identify major effective genes under local conditions. Genotyping using twenty-five gene-specific markers across diverse genotypes and F2 progenies revealed genes Pi5 and Pi54 to be associated with leaf blast resistance in all three populations. Genes Pib and qPbm showed an association with neck blast resistance in both natural and LR 5 × SMS populations. Additionally, a set of 184 genome-wide polymorphic markers (SSRs and SNPs), when applied to F2-resistant and F2-susceptible DNA bulks derived from LR 5 × LR 26, suggested that Pi20(t) on chromosome 12 is one of the major genes imparting disease resistance. Markers snpOS318, RM1337 and RM7102 and RM247 and snpOS316 were associated with leaf blast and neck blast resistance, respectively. The genotypes, markers and genes will help in marker-assisted selection and development of varieties with durable resistance.

Chronic diabetic foot ulcers (DFUs) are a significant complication of diabetes mellitus, often leading to amputation, increased morbidity, and a substantial financial burden. Even with the advancements in the treatment of DFU, the risk of amputation still exists, and this occurs due to the presence of gangrene and osteomyelitis. Nonhealing in a chronic DFU is due to decreased angiogenesis, granulation tissue formation, and extracellular matrix remodeling in the presence of persistent inflammation. During wound healing, the proliferation and migration of fibroblasts, smooth muscle cells, and keratinocytes play a critical role in extracellular matrix (ECM) remodeling, angiogenesis, and epithelialization. The molecular factors regulating the migration, proliferation, and differentiation of these cells are scarcely discussed in the literature. The literature review identifies the key factors influencing the proliferation, migration, and differentiation of fibroblasts, keratinocytes, and vascular smooth muscle cells (VSMCs), which are critical in wound healing. This is followed by a discussion on the various novel factors regulating the migration, proliferation, and differentiation of these cells but not in the context of wound healing; however, they may play a role. Using a network analysis, we examined the interactions between various factors, and the findings suggest that the novel factors identified may play a significant role in promoting angiogenesis, granulation tissue formation, and extracellular matrix remodeling during wound healing or DFU healing. However, these interactions warrant further investigation to establish their role alone or synergistically.

The field of sociology has largely ignored critical race theory (CRT) as a relevant theoretical and pedagogical framework for the study of white supremacy and Indigenous and Black race relations in Canada. In the United States, CRT has long been a theoretical framework tethered to and contextualizing the underpinnings of systemic racism and white supremacy as the cornerstone of structural oppression in American legal society. The initial focus of this work was to study the operationalization of the myriad ways in which race and racial power were constructed and represented in American law and society and the attendant ways in which Black civil rights under American law could never be achieved through the application of legal jurisprudence. CRT’s theoretical milieu has expanded beyond legal research to examine the sphere of racist structural oppression as systemically embedded in immigration, housing, education, employment, healthcare, and child welfare systems. The writing of this article has been an intentional active disruption to the claims that multiculturalism has the answers to race relations in an ever-changing Canadian society. While there are six main tenets of CRT, this article specifically focuses on three core tenets of CRT which argue that (1) racism is an ever-present dynamic of life in Canada; (2) racial subordination remains endemically tied to the political, cultural, and social milieu of white supremacy impacting the lives of Indigenous and Black peoples in Canada; and (3) racism has contributed to all historical and contemporary manifestations of structural oppression related to land theft and anti-Black racism. As such, CRT has much to contribute to race-radical research, pedagogy, and praxis when it comes to understanding race relations in a Canadian society grappling with an ever-changing multicultural narrative.

Electrocardiography (ECG) plays a pivotal role in monitoring cardiac health, yet the manual analysis of ECG signals is challenging due to the complex task of identifying and categorizing various waveforms and morphologies within the data. Additionally, ECG datasets often suffer from a significant class imbalance issue, which can lead to inaccuracies in detecting minority class samples. To address these challenges and enhance the effectiveness and efficiency of cardiac arrhythmia detection from imbalanced ECG datasets, this study proposes a novel approach. This research leverages the MIT-BIH arrhythmia dataset, encompassing a total of 109,446 ECG beats distributed across five classes following the Association for the Advancement of Medical Instrumentation (AAMI) standard. Given the dataset’s inherent class imbalance, a 1D generative adversarial network (GAN) model is introduced, incorporating the Bi-LSTM model to synthetically generate the two minority signal classes, which represent a mere 0.73% fusion (F) and 2.54% supraventricular (S) of the data. The generated signals are rigorously evaluated for similarity to real ECG data using three key metrics: mean squared error (MSE), structural similarity index (SSIM), and Pearson correlation coefficient (r). In addition to addressing data imbalance, the work presents three deep learning models tailored for ECG classification: SkipCNN (a convolutional neural network with skip connections), SkipCNN+LSTM, and SkipCNN+LSTM+Attention mechanisms. To further enhance efficiency and accuracy, the test dataset is rigorously assessed using an ensemble model, which consistently outperforms the individual models. The performance evaluation employs standard metrics such as precision, recall, and F1-score, along with their average, macro average, and weighted average counterparts. Notably, the SkipCNN+LSTM model emerges as the most promising, achieving remarkable precision, recall, and F1-scores of 99.3%, which were further elevated to an impressive 99.60% through ensemble techniques. Consequently, with this innovative combination of data balancing techniques, the GAN-SkipNet model not only resolves the challenges posed by imbalanced data but also provides a robust and reliable solution for cardiac arrhythmia detection. This model stands poised for clinical applications, offering the potential to be deployed in hospitals for real-time cardiac arrhythmia detection, thereby benefiting patients and healthcare practitioners alike.

Heart diseases such as cardiovascular and myocardial infarction are the foremost reasons of death in the world. The timely, accurate, and effective prediction of heart diseases is crucial for saving lives. Electrocardiography (ECG) is a primary non-invasive method to identify cardiac abnormalities. However, manual interpretation of ECG recordings for heart disease diagnosis is a time-consuming and inaccurate process. For the accurate and efficient detection of heart diseases from the 12-lead ECG dataset, we have proposed a hybrid residual/inception-based deeper model (HRIDM). In this study, we have utilized ECG datasets from various sources, which are multi-institutional large ECG datasets. The proposed model is trained on 12-lead ECG data from over 10,000 patients. We have compared the proposed model with several state-of-the-art (SOTA) models, such as LeNet-5, AlexNet, VGG-16, ResNet-50, Inception, and LSTM, on the same training and test datasets. To show the effectiveness of the computational efficiency of the proposed model, we have only trained over 20 epochs without GPU support and we achieved an accuracy of 50.87% on the test dataset for 27 categories of heart abnormalities. We found that our proposed model outperformed the previous studies which participated in the official PhysioNet/CinC Challenge 2020 and achieved fourth place as compared with the 41 official ranking teams. The result of this study indicates that the proposed model is an implying new method for predicting heart diseases using 12-lead ECGs.

Hybrid immunity, as a result of infection and vaccination to SARS-CoV-2, has been well studied in adults but limited evidence is available in children. We evaluated the antibody responses to primary SARS-CoV-2 infection among vaccinated and unvaccinated children aged ≥ 5 years. Methods: A longitudinal cohort study of children aged ≥ 5 was conducted during August 2021–August 2022, at sites in Arizona, Texas, Utah, and Florida. Children submitted weekly nasal swabs for PCR testing and provided sera 14–59 days after PCR-confirmed SARS-CoV-2 infection. Antibodies were measured by ELISA against the receptor-binding domain (RBD) and S2 domain of ancestral Spike (WA1), in addition to Omicron (BA.2) RBD, following infection in children, with and without prior monovalent ancestral mRNA COVID-19 vaccination. Results: Among the 257 participants aged 5 to 18 years, 166 (65%) had received at least two mRNA COVID-19 vaccine doses ≥ 14 days prior to infection. Of these, 53 occurred during Delta predominance, with 37 (70%) unvaccinated at the time of infection. The remaining 204 infections occurred during Omicron predominance, with 53 (26%) participants unvaccinated. After adjusting for weight, age, symptomatic infection, and gender, significantly higher mean RBD AUC values were observed among the vaccinated group compared to the unvaccinated group for both WA1 and Omicron (p < 0.0001). A smaller percentage of vaccinated children reported fever during illness, with 55 (33%) reporting fever compared to 44 (48%) unvaccinated children reporting fever (p = 0.021). Conclusions: Children with vaccine-induced immunity at the time of SARS-CoV-2 infection had higher antibody levels during convalescence and experienced less fever compared to unvaccinated children during infection.

Background: Alzheimer’s disease (AD) is a neurodegenerative disease characterized by beta-amyloid (Aβ) deposition and increased acetylcholinesterase (AchE) enzyme activities. Indole 3 carbinol (I3C) and diindolylmethane (DIM) are reported to have neuroprotective activities against various neurological diseases, including ischemic stroke, Parkinson’s disease, neonatal asphyxia, depression, stress, neuroinflammation, and excitotoxicity, except for AD. In the present study, we have investigated the anti-AD effects of I3C and DIM. Methods: Docking and molecular dynamic studies against AchE enzyme and network pharmacological studies were conducted for I3C and DIM. I3C and DIM’s neuroprotective effects against self and AchE-induced Aβ aggregation were investigated. The neuroprotective effects of I3C and DIM against Aβ-induced neurotoxicity were assessed in SH-S5Y5 cells by observing cell viability and ROS. Results: Docking studies against AchE enzyme with I3C and DIM show binding efficiency of −7.0 and −10.3, respectively, and molecular dynamics studies revealed a better interaction and stability between I3C and AchE and DIM and AchE. Network pharmacological studies indicated that I3C and DIM interacted with several proteins involved in the pathophysiology of AD. Further, I3C and DIM significantly inhibited the AchE (IC50: I3C (18.98 µM) and DIM (11.84 µM)) and self-induced Aβ aggregation. Both compounds enhanced the viability of SH-S5Y5 cells that are exposed to Aβ and reduced ROS. Further, I3C and DIM show equipotential neuroprotection when compared to donepezil. Conclusions: Our findings indicate that both I3C and DIM show anti-AD effects by inhibiting the Aβ induced neurotoxicity and AchE activities.

Flooding poses a significant threat as a prevalent natural disaster. To mitigate its impact, identifying flood-prone areas through susceptibility mapping is essential for effective flood risk management. This study conducted flood susceptibility mapping (FSM) in Chandrapur district, Maharashtra, India, using geographic information system (GIS)-based frequency ratio (FR) and Shannon’s entropy index (SEI) models. Seven flood-contributing factors were considered, and historical flood data were utilized for model training and testing. Model performance was evaluated using the area under the curve (AUC) metric. The AUC values of 0.982 for the SEI model and 0.966 for the FR model in the test dataset underscore the robust performance of both models. The results revealed that 5.4% and 8.1% (FR model) and 3.8% and 7.6% (SEI model) of the study area face very high and high risks of flooding, respectively. Comparative analysis indicated the superiority of the SEI model. The key limitations of the models are discussed. This study attempted to simplify the process for the easy and straightforward implementation of FR and SEI statistical flood susceptibility models along with key insights into the flood vulnerability of the study region.

Gene regulation is crucial for cellular function and homeostasis. It involves diverse mechanisms controlling the production of specific gene products and contributing to tissue-specific variations in gene expression. The dysregulation of genes leads to disease, emphasizing the need to understand these mechanisms. Computational methods have jointly studied transcription factors (TFs), microRNA (miRNA), and messenger RNA (mRNA) to investigate gene regulatory networks. However, there remains a knowledge gap in comprehending gene regulatory networks. On the other hand, super-enhancers (SEs) have been implicated in miRNA biogenesis and function in recent experimental studies, in addition to their pivotal roles in cell identity and disease progression. However, statistical/computational methodologies harnessing the potential of SEs in deciphering gene regulation networks remain notably absent. However, to understand the effect of miRNA on mRNA, existing statistical/computational methods could be updated, or novel methods could be developed by accounting for SEs in the model. In this review, we categorize existing computational methods that utilize TF and miRNA data to understand gene regulatory networks into three broad areas and explore the challenges of integrating enhancers/SEs. The three areas include unraveling indirect regulatory networks, identifying network motifs, and enriching pathway identification by dissecting gene regulators. We hypothesize that addressing these challenges will enhance our understanding of gene regulation, aiding in the identification of therapeutic targets and disease biomarkers. We believe that constructing statistical/computational models that dissect the role of SEs in predicting the effect of miRNA on gene regulation is crucial for tackling these challenges.

Delivering nucleic acid therapeutics across cell membranes is a significant challenge. Cell-penetrating peptides (CPPs) containing arginine (R), tryptophan (W), and histidine (H) show promise for siRNA delivery. To improve siRNA delivery and silence a model STAT3 gene, we hypothesized that oleyl acylation to CPPs, specifically (WRH)n, would enhance STAT3 silencing efficiency in breast and ovarian cancer cells. Using Fmoc/tBu solid-phase peptide chemistry, we synthesized, purified, and characterized the oleyl-conjugated (WRH)n (n = 1–4) peptides. The peptide/siRNA complexes were non-cytotoxic at N/P 40 (~20 μM) against MDA-MB-231, MCF-7, SK-OV-3, and HEK-293 cells after 72 h incubation. All peptide/siRNA complexes showed serum stability at N/P ≥ 40. The synthesized conjugates, with a diameter of 3 and oleyl-(WRH)4 showed ~60% and ~75% cellular uptake of siRNA, respectively, in both MDA-MB-231 and SK-OV-3 cells. Western blot analysis of oleyl-(WRH)4 demonstrated effective silencing of the STAT-3 gene, with ~75% silencing in MDA-MB-231 cells and ~45% in SK-OV-3 cells.

African swine fever virus (ASFV) is the causative agent of African swine fever (ASF), a highly infectious and lethal disease of domesticated swine. Outbreaks of ASF have been mostly restricted to the continent of Africa. The outbreaks that have occurred outside of Africa were controlled by extensive depopulation of the domesticated pig population. However, in 2007, an outbreak occurred in the country of Georgia, where ASFV infected wild pigs and quickly spread across eastern Europe. Since the reintroduction of ASF into Europe, variants of the current pandemic strain, ASFV Georgia 2007/01 (ASFV-G), which is classified as Genotype 2 based on p72 sequencing, have been reported in countries within western Europe, Asia, and the island of Hispaniola. Additionally, isolates collected in 2020 confirmed the presence of variants of ASFV-G in Nigeria. Recently, we reported similar variants of ASFV-G collected from domestic pigs suspected of dying of ASF in Ghana in 2022. Here, we retroactively report, based on full-length sequencing, that similar variants were present in Ghana in 2021. The SNP analysis revealed derivatives of ASFV with distinct genetic markers. Furthermore, we identified three full-length ASFV genomes as Genotype 1, indicating that there were two genotypes circulating in proximity during the 2021 ASF outbreaks in Ghana.

The intensive applications of nanomaterials in the agroecosystem led to the creation of several environmental problems. More efforts are needed to discover new insights in the nanomaterial–microbe–plant nexus. This relationship has several dimensions, which may include the transport of nanomaterials to different plant organs, the nanotoxicity to soil microbes and plants, and different possible regulations. This review focuses on the challenges and prospects of the nanomaterial–microbe–plant nexus under agroecosystem conditions. The previous nano-forms were selected in this study because of the rare, published articles on such nanomaterials. Under the study’s nexus, more insights on the carbon nanodot–microbe–plant nexus were discussed along with the role of the new frontier in nano-tellurium–microbe nexus. Transport of nanomaterials to different plant organs under possible applications, and translocation of these nanoparticles besides their expected nanotoxicity to soil microbes will be also reported in the current study. Nanotoxicity to soil microbes and plants was investigated by taking account of morpho-physiological, molecular, and biochemical concerns. This study highlights the regulations of nanotoxicity with a focus on risk and challenges at the ecological level and their risks to human health, along with the scientific and organizational levels. This study opens many windows in such studies nexus which are needed in the near future.

Nitroimidazoles comprise a class of broad-spectrum anti-microbial drugs with efficacy against parasites, mycobacteria, and anaerobic Gram-positive and Gram-negative bacteria. Among these drugs, metronidazole (MTZ) is commonly used with other antibiotics to prevent infection in open fractures. However, the effect of MTZ on bone remains understudied. In this paper, we evaluated six nitroimidazole drugs for their impact on osteoblast differentiation and identified MTZ as having the highest osteogenic effect. MTZ enhanced bone regeneration at the femur osteotomy site in osteopenic ovariectomized (OVX) rats at the human equivalent dose. Moreover, in OVX rats, MTZ significantly improved bone mass and strength and improved microarchitecture compared to the vehicle-treated rats, which was likely achieved by an osteogenic mechanism attributed to the stimulation of the Wnt pathway in osteoblasts. To mitigate the reported neurological and genotoxic effects of MTZ, we designed an injectable sustained-release in situ gel formulation of the drug that improved fracture healing efficacy by 3.5-fold compared to oral administration. This enhanced potency was achieved through a significant increase in the circulating half-life and bioavailability of MTZ. We conclude that MTZ exhibits osteogenic effects, further accentuated by our sustained-release delivery system, which holds promise for enhancing bone regeneration in open fractures.

Baked pretzels are a popular choice for a quick snack, easily identifiable by their classic twisted shape, glossy exterior, and small salt crystals sprinkled on top, making them a standout snack. However, it is not commonly known that compounds with fluorescent properties can be formed during their production. Carbon nanodots (CNDs) with an average size of 3.5 nm were isolated and identified in bakery products. This study delved into the formation of CNDs in pretzel production using a fractional factorial experimental design. The research revealed that the baking temperature had the most significant impact on the concentration of CNDs, followed by the concentration of NaOH in the immersion solution, and then the baking time. This study highlights the unique role of the NaOH immersion step, which is not typically present in bread-making processes, in facilitating the formation of CNDs. This discovery highlights the strong correlation between the formation of CNDs and the heat treatment process. Monitoring and controlling these factors is crucial for regulating the concentration of CNDs in pretzel production and understanding nanoparticle formation in processed foods for food safety.

Biotransformation refers to the metabolic conversion of endogenous and xenobiotic chemicals into more hydrophilic substances. Xenobiotic biotransformation is accomplished by a restricted number of enzymes with broad substrate specificities. The biotransformation of xenobiotics is catalyzed by various enzyme systems that can be divided into four categories based on the reaction they catalyze. The primary concentration is in cytochrome P450, while the CYP enzymes responsible for xenobiotic biotransformation are located within the hepatic endoplasmic reticulum (microsomes). Cytochrome P450 (CYP450) enzymes are also present in extrahepatic tissues. Enzymes catalyzing biotransformation reactions often determine the intensity and duration of the action of drugs and play a key role in chemical toxicity and chemical tumorigenesis. The structure of a given biotransforming enzyme may differ among individuals, which can cause differences in the rates of xenobiotic biotransformation. The study of the molecular mechanisms underlying chemical liver injury is fundamental for preventing or devising new modalities of treatment for liver injury using chemicals. Active metabolites arise from the biotransformation of a parent drug compound using one or more xenobiotic-processing enzymes to generate metabolites with different pharmacological or toxicological properties. Understanding how exogenous chemicals (xenobiotics) are metabolized, distributed, and eliminated is critical to determining the impact of these compounds on human health. Computational tools such as Biotransformer have been developed to predict all the possible metabolites of xenobiotic and enzymatic profiles that are linked to the production of metabolites. The construction of xenobiotic metabolism maps can predict enzymes catalyzing metabolites capable of binding to DNA.

Drought poses a significant threat to agricultural productivity, particularly affecting economic crops like tomatoes. To address this challenge, various alternatives have been explored, including the use of elicitors or biostimulants such as salicylic acid (SA). This study aims to assess the stimulatory action of SA in alleviating drought stress in tomato plants under greenhouse conditions. The experiment was designed with two main factors: water availability (controlled versus drought) and the foliar application of SA at four different concentrations ranging from 100 to 250 mg L−1. The application of SA, particularly at a concentration of 250 mg L−1, showed promising results in mitigating the adverse effects of drought stress followed by 200 mg L−1. This was evidenced by the increased activity of antioxidant enzymes such as superoxide dismutase (SOD), ascorbate peroxidase (APX), and catalase (CAT). Gene expression analysis revealed optimal APX expression with SA application at concentrations of 200 mg L−1 or 250 mg L−1. Additionally, the application of SA at 250 mg L−1 led to a high accumulation of bioactive compounds without compromising yield. Furthermore, SA application positively influenced both shoot and root weights, with the highest values observed at a concentration of 250 mg L−1. While SA is known to enhance plant tolerance to abiotic stress, further research is needed to fully elucidate its biochemical, physiological, and molecular mechanisms in supporting plant tolerance to drought stress. Utilizing salicylic acid can help growers mitigate environmental stresses, enhancing tomato crop yield and quality. Integrating SA treatments into agriculture offers a sustainable alternative elicitor for ensuring food security under challenging climate conditions.

Microalgae are a natural source of lipids, carotenoids, and other value-added compounds. The combination of nutrients and their precise ratio have a significant impact on the productivity of microalgae-mediated compounds. The biomass, lipid, chlorophyll, and carotenoid production of three microalgae species, namely, Monoraphidium sp., Chlorella sorokiniana, and Scenedesmus obliquus, were investigated by employing standard photoautotrophic media, namely, BG-11, BBM, and HS CHU-10. BBM was found as the most effective medium since it obtained higher biomass, lipids, and carotenoids from microalgae. The lipids and carotenoids were identified using chromatographic and microscopy techniques. The findings showed that although the Monoraphidium sp. biomass productivity was the lowest, it emerged as a substantial producer of astaxanthin, whereas the Chlorella sorokiniana culture could grow in a variety of media and produced β-carotene as a major carotenoid. On the other hand, Scenedesmus obliquus was found to be a considerable source of lipids and β-carotene. This study provided a comprehensive understanding of the appropriate medium selection in order to extract an assortment of value-added compounds from freshwater microalgae species.

The microflora of the soil is adversely affected by chemical fertilizers. Excessive use of chemical fertilizers has increased crop yield dramatically at the cost of soil vigor. The pH of the soil is temporarily changed by chemical fertilizers, which kill the beneficial soil microflora and can cause absorption stress on crop plants. This leads to higher dosages during the application, causing groundwater leaching and environmental toxicity. Nanofertilizers (NFs) reduce the quantity of fertilizer needed in agriculture, enhance nutrient uptake efficiency, and decrease fertilizer loss due to runoff and leaching. Moreover, NFs can be used for soil or foliar applications and have shown promising results in a variety of plant species. The main constituents of nanomaterials are micro- and macronutrient precursors and their properties at the nanoscale. Innovative approaches to their application as a growth promoter for crops, their modes of application, and the mechanism of absorption in plant tissues are reviewed in this article. In addition, the review analyzes potential shortcomings and future considerations for the commercial agricultural application of NFs.

Background: The cutting movement assessment score (CMAS) provides a qualitative assessment of the side-step cutting (S-SC) technique. Previous research has been undertaken primarily by biomechanists experienced with S-SC evaluations. Little is known about the agreement between various sports science and medicine practitioners to ascertain whether the tool can be used effectively by different practitioners in the field. Currently, the CMAS uses three camera views (CVS) to undertake the evaluation, and it would be worthwhile to know whether the CMAS can be effectively conducted with fewer camera views to improve clinical utility. Therefore, the aim of the study was to examine the inter-rater agreement between different sports science and medicine practitioners and agreement between using different CVS to evaluate the S-SC technique using the CMAS. Methods: Video data were collected from 12 male rugby union players performing a 45° S-SC manoeuvre toward both the left and right directions. Five different sports science and medicine practitioners evaluated footage from three cameras of one left and one right trial from each player using the CMAS. Twelve different trials were also evaluated by the sports rehabilitator using single and multiple CVS. Agreements (percentage; Kappa coefficients (K)) between different practitioners and configurations of the CVS were explored. Results: Good to excellent inter-rater agreements were found between all practitioners for total score (K = 0.63–0.84), with moderate to excellent inter-rater agreements observed across all items of the CMAS (K = 0.5–1.0). Excellent agreement was found between using three CVS vs. two CVS that included at least a sagittal view (K = 0.96–0.97). Lower agreement (K = 0.83) was found between angle-frontal views with three CVS. Conclusions: The CMAS can be used effectively by various practitioners to evaluate the movement quality of S-SC. The use of two CVS that include at least a sagittal plane view would suffice to evaluate the S-SC technique against the CMAS.

A male in his 60s presented with left lower extremity fractures following a vehicle accident. Hemoglobin, initially, was 12.4 mmol/L, and platelet count was 235 k/mcl. On day 11 of admission, his platelet count initially dropped to 99 k/mcl, and after recovery it rapidly decreased to 11 k/mcl on day 16 when the INR was 1.3 and aPTT was 32 s, and he continued to have a stable anemia throughout admission. There was no response in platelet count post-transfusion of four units of platelets. Hematology initially evaluated the patient for disseminated intravascular coagulation, heparin-induced thrombocytopenia (anti-PF4 antibody was 0.19), and thrombotic thrombocytopenic purpura (PLASMIC score of 4). Vancomycin was administered on days 1–7 for broad spectrum antimicrobial coverage and day 10, again, for concerns of sepsis. Given the temporal association of thrombocytopenia and vancomycin administration, a diagnosis of vancomycin-induced immune thrombocytopenia was established. Vancomycin was discontinued, and 2 doses of 1000 mg/kg of intravenous immunoglobulin 24 h apart were administered with the subsequent resolution of thrombocytopenia.

As one of the most prevalent forms of cancer worldwide, breast cancer has garnered significant attention within the clinical research setting. While traditional treatment employs a multidisciplinary approach including a variety of therapies such as chemotherapy, hormone therapy, and even surgery, researchers have since directed their attention to the budding role of neoantigens. Neoantigens are defined as tumor-specific antigens that result from a multitude of genetic alterations, the most prevalent of which is the single nucleotide variant. As a result of their foreign nature, neoantigens elicit immune responses upon presentation by Major Histocompatibility Complexes I and II followed by recognition by T cell receptors. Previously, researchers have been able to utilize these immunogenic properties and manufacture neoantigen-specific T-cells and neoantigen vaccines. Within the context of breast cancer, biomarkers such as tumor protein 53 (TP53), Survivin, Partner and Localizer of BRCA2 (PALB2), and protein tyrosine phosphatase receptor T (PTPRT) display exceeding potential to serve as neoantigens. However, despite their seemingly limitless potential, neoantigens must overcome various obstacles if they are to be fairly distributed to patients. For instance, a prolonged period between the identification of a neoantigen and the dispersal of treatment poses a serious risk within the context of breast cancer. Regardless of these current obstacles, it appears highly promising that future research into neoantigens will make an everlasting impact on the health outcomes within the realm of breast cancer. The purpose of this literature review is to comprehensively discuss the etiology of various forms of breast cancer and current treatment modalities followed by the significance of neoantigens in cancer therapeutics and their application to breast cancer. Further, we have discussed the limitations, future directions, and the role of transcriptomics in neoantigen identification and personalized medicine. The concepts discussed in the original and review articles were included in this review article.

Offshore structures and ships can be progressively damaged due to repeated mass impacts induced by contacts with ships, ice floes, and dropped and/or floating other objects while in service. This paper aims to predict the residual deflection evolution of the marine structures under such impact repetitions. The side hull structures of the general ice-class vessels were selected for this study. The numerical simulations were performed to predict the deflection response of repeatedly impacted stiffened plates by using the software package Abaqus 6.13. For the simulations, the strain hardening of the relevant ice-class steel grade was adopted using the proposed constitutive equations, and the strain-rate hardening effects were taken into account by employing the existing formulations. The developed numerical model was substantiated against tests available in the open literature. Based on the validated model, a parametric study on various stiffened plates was performed. The evolution of the residual deflection of the repeatedly impacted plates with actual scantlings and various impact scenarios was investigated. A practical formula for the prediction of the residual deflection evolution of the plates under repeated mass impacts was proposed based on the regression analysis of the parametric study results. The reliability and accuracy of the proposed formula were confirmed through comparisons with numerical simulations and existing analytical formulations. It is expected that the proposed formula can be efficiently employed as a quick-hand tool for the reliable prediction of the residual deflection evolution incurred by repeated mass impacts.