Technical Limitations Research Articles

Fcγ-receptor-IIIA bioactivity of circulating and synovial immune complexes in rheumatoid arthritis

ObjectivePrevious technical limitations prevented the proof of Fcγ-receptor (FcγR)-activation by soluble immune complexes (sICs) in patients. FcγRIIIa (CD16) is a risk factor in rheumatoid arthritis (RA). We aimed at determining...

Leveraging AI automated emergency response with natural language processing: Enhancing real-time decision making and communication

Artificial Intelligence (AI) and Natural Language Processing (NLP) technologies play an essential role in the ongoing development and advancements of emergency response systems. This paper explores the new opportunities and possibilities offered by the incorporation of AI-driven automated emergency response systems, and the benefits of using NLP to increase the efficiency of human language processing and communication in real time. Using case studies and applications, we analyse how integrating AI and NLP technologies can reduce response times, improve situational awareness and coordination between emergency services. We then explore how AI-driven models can be used to predict natural disasters, optimise emergency vehicle routes and identify areas of need in real time through the analysis of social media and emergency communications via NLP. However, we also discuss the issues that come with implementing these technologies, such as data-privacy challenges, technical limitations and the need for sustained retraining of AI systems. The conclusions of this research indicate that continued collaboration between stakeholders is necessary to overcome these barriers, and that substantial innovation is needed to maximise the potential of AI and NLP technologies in emergency management.

Adoption of Artificial Intelligence-Enabled Robots in Long-Term Care Homes by Health Care Providers: Scoping Review.

Long-term care (LTC) homes face the challenges of increasing care needs of residents and a shortage of health care providers. Literature suggests that artificial intelligence (AI)-enabled robots may solve such challenges and support person-centered care. There is a dearth of literature exploring the perspectives of health care providers, which are crucial to implementing AI-enabled robots. This scoping review aims to explore this scant body of literature to answer two questions: (1) what barriers do health care providers perceive in adopting AI-enabled robots in LTC homes? (2) What strategies can be taken to overcome these barriers to the adoption of AI-enabled robots in LTC homes? We are a team consisting of 3 researchers, 2 health care providers, 2 research trainees, and 1 older adult partner with diverse disciplines in nursing, social work, engineering, and medicine. Referring to the Joanna Briggs Institute methodology, our team searched databases (CINAHL, MEDLINE, PsycINFO, Web of Science, ProQuest, and Google Scholar) for peer-reviewed and gray literature, screened the literature, and extracted the data. We analyzed the data as a team. We compared our findings with the Person-Centered Practice Framework and Consolidated Framework for Implementation Research to further our understanding of the findings. This review includes 33 articles that met the inclusion criteria. We identified three barriers to AI-enabled robot adoption: (1) perceived technical complexity and limitation; (2) negative impact, doubted usefulness, and ethical concerns; and (3) resource limitations. Strategies to mitigate these barriers were also explored: (1) accommodate the various needs of residents and health care providers, (2) increase the understanding of the benefits of using robots, (3) review and overcome the safety issues, and (4) boost interest in the use of robots and provide training. Previous literature suggested using AI-enabled robots to resolve the challenges of increasing care needs and staff shortages in LTC. Yet, our findings show that health care providers might not use robots because of different considerations. The implication is that the voices of health care providers need to be included in using robots. RR2-doi:10.1136/bmjopen-2023-075278.

Barriers to diffusion and implementation of pediatric minimally invasive surgery in Brazil

BackgroundThe main barriers to the broad implementation of pediatric minimally invasive surgery (MIS) are technological, technical, and epistemological barriers, as well as the rarity of certain pathologies. These issues are presumed to be more significant in low- and middle-income countries. This study aimed to identify and analyze the factors limiting the diffusion and implementation of pediatric MIS in Brazil.MethodsA nationwide cross-sectional survey was conducted via an online questionnaire in Brazil from January 2022 to July 2022. The sample was taken by convenience from the population of pediatric surgeons in Brazil. A total of 187 surgeons were included. The collected data were divided into three sections to evaluate technological, technical, and epistemological limitations to pediatric MIS implementation.ResultsAlthough 85% of the participants had previous training, a lack of adequate training was identified as a significant limiting factor, particularly among those who had taken only short courses (42.3% vs. 64.3%, p = 0.033). Only 14% of the participants reported performing MIS for major pediatric procedures. With respect to intracorporeal suturing, 38.1% of the surgeons with extensive training considered it a limiting factor compared with 60.7% (p = 0.029) of those without prior training. Among those without previous training, 61% cited a lack of financial support or encouragement from their department as the reason. Additionally, 65% of the surgeons considered the lack of basic instruments a limiting factor. Although 95% of the participants agreed that simulation training is indispensable, pediatric surgery fellowship programs in Brazil do not include a standardized curriculum or mandatory training in MIS, and only 47% reported providing training space for their current fellows.ConclusionA combination of technological, technical, and epistemological barriers hinders the implementation of pediatric MIS. Despite its limitations, this study serves as a foundational guide for future analysis and overcoming the identified barriers.

Deep learning applications for quantitative and qualitative PET in PET/MR: technical and clinical unmet needs.

We aim to provide an overview of technical and clinical unmet needs in deep learning (DL) applications for quantitative and qualitative PET in PET/MR, with a focus on attenuation correction, image enhancement, motion correction, kinetic modeling, and simulated data generation. (1) DL-based attenuation correction (DLAC) remains an area of limited exploration for pediatric whole-body PET/MR and lung-specific DLAC due to data shortages and technical limitations. (2) DL-based image enhancement approximating MR-guided regularized reconstruction with a high-resolution MR prior has shown promise in enhancing PET image quality. However, its clinical value has not been thoroughly evaluated across various radiotracers, and applications outside the head may pose challenges due to motion artifacts. (3) Robust training for DL-based motion correction requires pairs of motion-corrupted and motion-corrected PET/MR data. However, these pairs are rare. (4) DL-based approaches can address the limitations of dynamic PET, such as long scan durations that may cause patient discomfort and motion, providing new research opportunities. (5) Monte-Carlo simulations using anthropomorphic digital phantoms can provide extensive datasets to address the shortage of clinical data. This summary of technical/clinical challenges and potential solutions may provide research opportunities for the research community towards the clinical translation of DL solutions.

From Virtual to Reality: How VR, AR, XR, MR are Reshaping Our Lives and Work

This article explores the transformative effects of Virtual Reality (VR), Augmented Reality (AR), Extended Reality (XR), and Mixed Reality (MR) on various aspects of daily life and professional environments. These technologies, collectively known as immersive technologies, are reshaping how we live, learn, and work by offering enhanced interactive experiences and new ways to perceive and interact with the world. The paper reviews their applications across key sectors such as healthcare, education, the workplace, and entertainment. In healthcare, VR and AR are revolutionizing patient care and medical training. In education, these technologies are enhancing learning experiences by providing immersive and interactive environments that facilitate greater engagement and understanding. In the workplace, XR and MR are improving collaboration and training, especially in remote settings. Additionally, in the realm of entertainment, they offer unprecedented immersive experiences that increase engagement. The article also discusses the challenges these technologies face, including technical limitations, high costs, and privacy concerns, and considers their future development and potential societal impacts.

Adaptive channel-weight dual-constrained semi-supervised EEG clustering

When processing Electroencephalography(EEG) for applications such as brain disease diagnosis, rehabilitation, and brain–computer interfaces, we encounter challenges related to missing labels or incorrect annotations. These challenges may arise from subjective factors, technical limitations, or data quality issues. In traditional EEG processing, unsupervised clustering algorithms are commonly used; however, due to the lack of labels, these methods may struggle to fully leverage latent information, resulting in uncertainty and instability in clustering outcomes. To address this issue, we propose a semi-supervised clustering method tailored for partially labeled EEG signals, named Adaptive Channel-weight Dual-constrained Semi-supervised EEG Clustering (ACDSEEGc). This method incorporates dual constraints: a connectivity constraint for the interconnections between EEG points and a neighborhood constraint for the spatial neighborhood relationships among EEG data points. Additionally, it integrates channel weights to emphasize information from important channels. The objective function of ACDSEEGc integrates cross-entropy evaluation, channel weight learning, pseudo-label learning, connectivity constraint, and least squares error minimization. Our aim is to maximize the use of limited prior knowledge to achieve better EEG clustering results. Experimental validation demonstrates that ACDSEEGc effectively produces superior clustering outcomes compared to state-of-the-art standard unsupervised and semi-supervised EEG/time-series clustering algorithms. Furthermore, we conducted sensitivity experiments and ablation studies to verify the performance of ACDSEEGc under different settings.

Deep Learning for MRI Segmentation and Molecular Subtyping in Glioblastoma: Critical Aspects from an Emerging Field

Deep learning (DL) has been applied to glioblastoma (GBM) magnetic resonance imaging (MRI) assessment for tumor segmentation and inference of molecular, diagnostic, and prognostic information. We comprehensively overviewed the currently available DL applications, critically examining the limitations that hinder their broader adoption in clinical practice and molecular research. Technical limitations to the routine application of DL include the qualitative heterogeneity of MRI, related to different machinery and protocols, and the absence of informative sequences, possibly compensated by artificial image synthesis. Moreover, taking advantage from the available benchmarks of MRI, algorithms should be trained on large amounts of data. Additionally, the segmentation of postoperative imaging should be further addressed to limit the inaccuracies previously observed for this task. Indeed, molecular information has been promisingly integrated in the most recent DL tools, providing useful prognostic and therapeutic information. Finally, ethical concerns should be carefully addressed and standardized to allow for data protection. DL has provided reliable results for GBM assessment concerning MRI analysis and segmentation, but the routine clinical application is still limited. The current limitations could be prospectively addressed, giving particular attention to data collection, introducing new technical advancements, and carefully regulating ethical issues.

Comparison of Short-Term Outcomes after Robotic Surgery for Gastric Cancer in Elderly and Younger Patients: A Retrospective Cohort Study.

Robot-assisted surgery has recently been introduced to overcome some drawbacks and technical limitations in performing laparoscopic gastrectomy. The aim of the present study was to evaluate the feasibility and safety of robotic gastrectomy in elderly patients. The study enrolled 143 patients who underwent robotic gastrectomy in a single high-volume centre. All patients were divided into two groups based on age: elderly group ≥ 75 years old (EG; n = 64) and non-elderly group < 75 years old (NEG; n = 79). Comorbidities were significantly more frequent in the EG (95.3%) than in the NEG (81%) (p = 0.011). Similarly, the percentage of ASA 3 patients was significantly higher in the EG than in the NEG (43.8% vs. 24.0%, respectively; p = 0.048). Nevertheless, the incidence of Clavien-Dindo grade III and IV complications did not differ significantly between the two groups (10.9% in the EG and 6.3% in the NEG; p = 0.852). Moreover, operative time, re-operation rate, mean number of harvested lymph nodes, 30-day mortality, and median hospital stay were similar within the two groups. Our study suggests that robotic gastrectomy can be performed safely for elderly patients. In particular, chronological age does not seem to affect either the clinical or oncological short-term outcomes after robotic gastrectomy.

Experience with the cochlear implant enhances the neural tracking of spectrotemporal patterns in the Alberti bass

Cochlear implant (CI) users experience diminished music enjoyment due to the technical limitations of the CI. Nonetheless, behavioral studies have reported that rhythmic features are well-transmitted through the CI. Still, the gradual improvement of rhythm perception after the CI switch-on has not yet been determined using neurophysiological measures. To fill this gap, we here reanalyzed the electroencephalographic responses of participants from two previous mismatch negativity studies. These studies included eight recently implanted CI users measured twice, within the first six weeks after CI switch-on and approximately three months later; thirteen experienced CI users with a median experience of 7 years; and fourteen normally hearing (NH) controls. All participants listened to a repetitive four-tone pattern (known in music as Alberti bass) for 35 min. Applying frequency tagging, we aimed to estimate the neural activity synchronized to the periodicities of the Alberti bass. We hypothesized that longer experience with the CI would be reflected in stronger frequency-tagged neural responses approaching the responses of NH controls. We found an increase in the frequency-tagged amplitudes after only 3 months of CI use. This increase in neural synchronization may reflect an early adaptation to the CI stimulation. Moreover, the frequency-tagged amplitudes of experienced CI users were significantly greater than those of recently implanted CI users, but still smaller than those of NH controls. The frequency-tagged neural responses did not just reflect spectrotemporal changes in the stimuli (i.e., intensity or spectral content fluctuating over time), but also showed non-linear transformations that seemed to enhance relevant periodicities of the Alberti bass. Our findings provide neurophysiological evidence indicating a gradual adaptation to the CI, which is noticeable already after three months, resulting in close to NH brain processing of spectrotemporal features of musical rhythms after extended CI use.

New genomic techniques can contribute to reduced pesticide usage in Europe

Societal Impact StatementCan modern breeding technologies, such as genome editing, contribute to reduced pesticide usage? This question has been accentuated by a recent legal proposal to exempt genome‐edited plants from the strict regulations applied to classical genetically modified (GM) crops within the European Union (EU). Using official statistics on crop cultivation and pesticide usage for two example crops commonly grown in Sweden, we calculate that cereal farmers collectively could save up to 70 million € in pesticide usage for wheat alone and that a late blight‐resistant potato could reduce pesticide usage by over 80% provided that the EU legislation is amended.SummaryThe European Commission has set goals to reduce the use of chemical pesticides, and one way to meet these goals in the agricultural sector is to breed disease‐resistant crops. Here, we ask whether modern breeding technologies, for example, genome editing using site‐directed nucleases, can contribute to these goals. This question has been accentuated by recent legal proposals in the European Union (EU) and several other jurisdictions worldwide to exempt genome‐edited plants from the strict regulations often applied to classical genetically modified (GM) crops. Using official statistics on crop cultivation and pesticide usage for two example crops commonly grown in Sweden (wheat and potato), we show that cereal farmers collectively could potentially save up to 70 million € in pesticide usage for wheat alone and that a late blight‐resistant potato could reduce pesticide usage by over 80% provided that the EU legislation is amended. Given the immense potential of genome‐edited crops, we further discuss details in the legal proposal currently being negotiated in the EU on the so‐called new genomic techniques that includes both genome editing and targeted insertions of cisgenes. Although promising, we argue that several technical limitations in the legal proposal will, if implemented, hamper the development of disease‐resistant crops and make the suggested legislation less future‐proof.

The Method and Experiment of Detecting the Strength of Structural Components Utilizing the Distributed Strain of Sensing Optical Fibers Demodulated by OFDR.

Defects occurring during the welding process of metal structural components directly affect their overall strength, which is crucial to the load-bearing capacity and durability of the components. This signifies the importance of accurate measurement and assessment of weld strength. However, traditional non-destructive testing methods such as ultrasonic and non-contact camera inspection have certain technical limitations. In response to these issues, this paper analyzes the detection principle of weld strength, revealing that weld defects reduce the effective area of the structural bearing section and cause stress concentration around them. Through repeated experimental data analysis of samples, strain distribution data along the one-dimensional direction caused by defects such as slag inclusion and porosity were obtained. Experimental results show that this method can identify defect types in welds, including slag inclusion, porosity, and unevenness, and accurately measure the location and size of defects with a precision of 0.64 mm, achieving qualitative analysis of weld defects. Additionally, by deploying distributed optical fiber sensors (DOFS) at different vertical distances along the weld direction, the propagation law of stress induced by different types of weld defects on samples was thoroughly analyzed. This further validates the advantages of this method in weld strength detection, including high spatial resolution, high sensitivity, and non-destructive measurement.

Functional connectivity across the human subcortical auditory system using an autoregressive matrix-Gaussian copula graphical model approach with partial correlations.

The auditory system comprises multiple subcortical brain structures that process and refine incoming acoustic signals along the primary auditory pathway. Due to technical limitations of imaging small structures deep inside the brain, most of our knowledge of the subcortical auditory system is based on research in animal models using invasive methodologies. Advances in ultrahigh-field functional magnetic resonance imaging (fMRI) acquisition have enabled novel noninvasive investigations of the human auditory subcortex, including fundamental features of auditory representation such as tonotopy and periodotopy. However, functional connectivity across subcortical networks is still underexplored in humans, with ongoing development of related methods. Traditionally, functional connectivity is estimated from fMRI data with full correlation matrices. However, partial correlations reveal the relationship between two regions after removing the effects of all other regions, reflecting more direct connectivity. Partial correlation analysis is particularly promising in the ascending auditory system, where sensory information is passed in an obligatory manner, from nucleus to nucleus up the primary auditory pathway, providing redundant but also increasingly abstract representations of auditory stimuli. While most existing methods for learning conditional dependency structures based on partial correlations assume independently and identically Gaussian distributed data, fMRI data exhibit significant deviations from Gaussianity as well as high-temporal autocorrelation. In this paper, we developed an autoregressive matrix-Gaussian copula graphical model (ARMGCGM) approach to estimate the partial correlations and thereby infer the functional connectivity patterns within the auditory system while appropriately accounting for autocorrelations between successive fMRI scans. Our results show strong positive partial correlations between successive structures in the primary auditory pathway on each side (left and right), including between auditory midbrain and thalamus, and between primary and associative auditory cortex. These results are highly stable when splitting the data in halves according to the acquisition schemes and computing partial correlations separately for each half of the data, as well as across cross-validation folds. In contrast, full correlation-based analysis identified a rich network of interconnectivity that was not specific to adjacent nodes along the pathway. Overall, our results demonstrate that unique functional connectivity patterns along the auditory pathway are recoverable using novel connectivity approaches and that our connectivity methods are reliable across multiple acquisitions.

ScRNMF: An imputation method for single-cell RNA-seq data by robust and non-negative matrix factorization.

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool in genomics research, enabling the analysis of gene expression at the individual cell level. However, scRNA-seq data often suffer from a high rate of dropouts, where certain genes fail to be detected in specific cells due to technical limitations. This missing data can introduce biases and hinder downstream analysis. To overcome this challenge, the development of effective imputation methods has become crucial in the field of scRNA-seq data analysis. Here, we propose an imputation method based on robust and non-negative matrix factorization (scRNMF). Instead of other matrix factorization algorithms, scRNMF integrates two loss functions: L2 loss and C-loss. The L2 loss function is highly sensitive to outliers, which can introduce substantial errors. We utilize the C-loss function when dealing with zero values in the raw data. The primary advantage of the C-loss function is that it imposes a smaller punishment for larger errors, which results in more robust factorization when handling outliers. Various datasets of different sizes and zero rates are used to evaluate the performance of scRNMF against other state-of-the-art methods. Our method demonstrates its power and stability as a tool for imputation of scRNA-seq data.

Advancement and Innovations in Drying of Biopharmaceuticals, Nutraceuticals, and Functional Foods

AbstractDrying is a crucial unit operation within the functional foods and biopharmaceutical industries, acting as a fundamental preservation technique and a mechanism to maintain these products' bioactive components and nutritional values. The heat-sensitive bioactive components, which carry critical quality attributes, necessitate a meticulous selection of drying methods and conditions backed by robust research. In this review, we investigate challenges associated with drying these heat-sensitive materials and examine the impact of various drying methods. Our thorough research extensively covers ten notable drying methods: heat pump drying, freeze-drying, spray drying, vacuum drying, fluidized bed drying, superheated steam drying, infrared drying, microwave drying, osmotic drying, vacuum drying, and supercritical fluid drying. Each method is tailored to address the requirements of specific functional foods and biopharmaceuticals and provides a comprehensive account of each technique's inherent advantages and potential limitations. Further, the review ventures into the exploration of combined hybrid drying techniques and smart drying technologies with industry 4.0 tools such as automation, AI, machine learning, IoT, and cyber-physical systems. These innovative methods are designed to enhance product performance and elevate the quality of the final product in the drying of functional foods and biopharmaceuticals. Through a thorough survey of the drying landscape, this review illuminates the intricacies of these operations and underscores their pivotal role in functional foods and biopharmaceutical production.

Potentiality and Apprehensions of Artificial Intelligence in Education: Perspectives of Education Staff

The present study aimed to assess the potentiality and apprehensions of artificial intelligence (AI) in education. It also investigated the challenges of AI integration into education from the teachers' perspectives. A cross-sectional study design was adopted. Through random sampling, a total of 63 members of faculty were recruited from Kuwait University. An online questionnaire was administered to the study participants. The data was analyzed through SPSS version 26, using descriptive statistics, t-tests, and ANOVA. The results showed that there was a remarkably high consensus about the potentiality of AI for education. The teachers’ readiness to adopt AI was low. Data analysis, machine learning, and natural language processing were the most important aspects of linking education and AI. The participants highlighted that for the empowerment of students, AI system use cases, evaluation of the intelligence of AI systems, and identification of the technical limitations of AI systems were crucial. Greater were challenges and difficulties in using AI such as the lack of availability of suitable educational materials, unavailability of required expertise in the field, and the complexity of the subject. However, no statistical difference attributed to gender, academic degree, and academic department in terms of facing challenges was found.

Genetics of Cardiac Aging Implicate Organ-Specific Variation.

Heart structure and function change with age, and the notion that the heart may age faster for some individuals than for others has driven interest in estimating cardiac age acceleration. However, current approaches have limited feature richness (heart measurements; radiomics) or capture extraneous data and therefore lack cardiac specificity (deep learning [DL] on unmasked chest MRI). These technical limitations have been a barrier to efforts to understand genetic contributions to age acceleration. We hypothesized that a video-based DL model provided with heart-masked MRI data would capture a rich yet cardiac-specific representation of cardiac aging. In 61,691 UK Biobank participants, we excluded noncardiac pixels from cardiac MRI and trained a video-based DL model to predict age from one cardiac cycle in the 4-chamber view. We then computed cardiac age acceleration as the bias-corrected prediction of heart age minus the calendar age. Predicted heart age explained 71.1% of variance in calendar age, with a mean absolute error of 3.3 years. Cardiac age acceleration was linked to unfavorable cardiac geometry and systolic and diastolic dysfunction. We also observed links between cardiac age acceleration and diet, decreased physical activity, increased alcohol and tobacco use, and altered levels of 239 serum proteins, as well as adverse brain MRI characteristics. We found cardiac age acceleration to be heritable (h2g 26.6%); a genome-wide association study identified 8 loci related to linked to cardiomyopathy (near TTN, TNS1, LSM3, PALLD, DSP, PLEC, ANKRD1 and MYO18B) and an additional 16 loci (near MECOM, NPR3, KLHL3, HDGFL1, CDKN1A, ELN, SLC25A37, PI15, AP3M1, HMGA2, ADPRHL1, PGAP3, WNT9B, UHRF1 and DOK5). Of the discovered loci, 21 were not previously associated with cardiac age acceleration. Mendelian randomization revealed that lower genetically mediated levels of 6 circulating proteins (MSRA most strongly), as well as greater levels of 5 proteins (LXN most strongly) were associated with cardiac age acceleration, as were greater blood pressure and Lp(a). A polygenic score for cardiac age acceleration predicted earlier onset of arrhythmia, heart failure, myocardial infarction, and mortality. These findings provide a thematic understanding of cardiac age acceleration and suggest that heart- and vascular-specific factors are key to cardiac age acceleration, predominating over a more global aging program.

Exploration of bacterial lipopolysaccharide-related genes signature based on T cells for predicting prognosis in colorectal cancer.

The intratumoral microorganisms participates in the progression and immunotherapy of colorectal cancer (CRC). However, due to technical limitations, the impact of microorganisms on CRC has not been fully understood. Therefore, we conducted a systematic analysis of relationship between bacterial lipopolysaccharide (LPS)-associated genes and immune cells to explore new biomarkers for predicting the prognosis of CRC. The single-cell RNA sequencing data and the Comparative Toxicogenomics Database were used to screen T cells-associated LPS-related genes (TALRGs). Then, we established and validated the TALRGs risk signature in The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD) cohort and GSE39582 cohort. Besides, we compared the differences in tumor-infiltrating immune cell types, immunotherapeutic response, somatic mutation profiles, and tumor mutation burden (TMB) between high-risk group and low-risk group. In addition, the immunotherapeutic cohort (Imvigor210) treated with an anti-PD-L1 agent was performed to explore the potential value of the TALRGs signature on immunotherapy. Five prognostic TALRGs were identified and selected to build the prognostic model. The high-risk group had poor prognosis in both TCGA-COAD cohort (P < 0.0001) and GSE39582 cohort (P = 0.00019). The areas under the curves (AUCs) of TALRGs signature were calculated (TCGA-COAD cohort: 0.624 at 1 years, 0.639 at 3 years, 0.648 at 5 years; anti-PD-L1 cohort was 0.59). The high-risk group had advanced pathological stages and higher TMN stages in both TCGA-COAD cohort and GSE39582 cohort. The high-risk group had the higher infiltration of immunosuppressive cells, the expressions of immune checkpoint molecules, the IC50 values of chemotherapy drugs, and TP53 mutation rate (P < 0.05). In addition, patients with high TMB had worse prognosis (P < 0.05). Furthermore, the Imvigor210 also showed patients with high-risk scores had poor prognosis (platinum-treated cohort: P = 0.0032; non-platinum-treated cohort: P = 0.00017). Microorganisms are closely related to the tumor microenvironment to influence the progression and immune response of CRC via stimulating T cells through LPS-related genes. The TALRGs signature contributed to predict the prognosis and immunotherapy of CRC, and became new therapeutic targets and biomarkers of CRC.

A study on the Application of Virtual Reality Technology in Children's Rehabilitation Therapy

This article discusses the value of virtual reality (VR) technology in paediatric rehabilitation therapy, which can create immersive, interactive and imaginative virtual environments and provide children with safe and interesting therapeutic modalities to effectively improve their cognitive, motor, speech and emotional problems. The article analyses the application of VR technology in different scenarios, such as attention training, motor training, verbal expression training and emotion recognition training, and points out that the advantages of VR technology are to increase the interest of treatment, enhance the patient's participation, and achieve personalised treatment. However, VR technology also has challenges such as technical limitations, evaluation difficulties and high equipment costs. With the continuous development and improvement of the technology, VR technology is expected to overcome these challenges and play a greater role in the field of children's rehabilitation therapy, bringing hope for the rehabilitation of more children.

An Experimental Protocol for the Boyden Chamber Invasion Assay With Absorbance Readout.

The phenomenon of cell invasion is an essential step in angiogenesis, embryonic development, immune responses, and cancer metastasis. In the course of cancer progression, the ability of neoplastic cells to degrade the basement membrane and penetrate neighboring tissue (or blood vessels and lymph nodes) is an early event of the metastatic cascade. The Boyden chamber assay is one of the most prevalent methods implemented to measure the pro- or anti-invasive effects of drugs, investigate signaling pathways that modulate cell invasion, and characterize the role of extracellular matrix proteins in metastasis. However, the traditional protocol of the Boyden chamber assay has some technical challenges and limitations. One such challenge is that the endpoint of the assay involves photographing and counting stained cells (in multiple fields) on porous filters. This process is very arduous, requires multiple observers, and is very time-consuming. Our improved protocol for the Boyden chamber assay involves lysis of the dye-stained cells and reading the absorbance using an ELISA reader to mitigate this challenge. We believe that our improved Boyden chamber methodology offers a standardized, high-throughput format to evaluate the efficacy of various drugs and test compounds in influencing cellular invasion in normal and diseased states. We believe that our protocol will be useful for researchers working in the fields of immunology, vascular biology, drug discovery, cancer biology, and developmental biology. Key features • Measurement of tumor invasion using human cancer cells. • Ability to measure the pro-invasive/anti-invasive activity of small molecules and biological modifiers. • Measurement of chemotaxis, chemokines, trafficking of immune cells, and proteolytic activity of matrix metalloproteinases, lysosomal hydrolysates, collagenases, and plasminogen activators in physiological and pathological conditions. • Investigation of the role of extracellular matrix proteins in the crosstalk between endothelial, epithelial, muscle, or neuronal cells and their adjacent stroma.