Successful Application Research Articles

(127) Is Artificial Intelligence Ready to Help Urologists?

Abstract Introduction ChatGPT is a language model developed by OpenAI. It's a variant of the GPT (Generative Pretrained Transformer) architecture, a transformer-based neural network used for natural language processing tasks. The model has been trained on a massive corpus of text data and can generate human-like responses to topics and questions. ChatGPT can be used for chatbots, conversation agents, and language translation. The integration of AI into urology has the potential to improve patient outcomes, increase efficiency, and reduce costs, ultimately leading to a better healthcare experience for patients. Objective The objective of this study is to assess the current state of artificial intelligence (AI) when it comes to urologic problems and to determine whether it is sufficiently developed and prepared to provide useful assistance to urologists in their clinical practice. This study aims to assess the accuracy, reliability, and potential benefits of one AI language model (ChatGPT) in efficiently answering common patient questions, providing patients with accurate educational information, minimizing patient harm, and guiding patients towards appropriate management. By exhibiting ChatGPT's capabilities and limitations in the context of urology, the study aims to demonstrate how AI can be helpful in everyday urological practices. Methods ChatGPT (chat.openai.com) was presented with typical clinical scenarios and patient questions. Results AI wrote the introduction answering “What is ChatGPT?” and “How can AI help urologists?”. For the conclusion, AI was prompted with “Is artificial intelligence ready to help urologists?”. Figures summarize the answers to common patient questions given by ChatGPT. Conclusions The use of AI in the field of urology is still in its early stages and more research is needed to determine its full potential. However, there have already been some promising developments and successful applications. For example, AI algorithms have been used to accurately diagnose prostate cancer, predict patient outcomes, and improve surgical planning. While AI has the potential to revolutionize the field of urology, it is important to note that AI is not a substitute for human expertise and judgment. AI algorithms can provide valuable insights and assistance, but the ultimate decision making and treatment planning should still be done by experienced urologists. Additionally, it is important to ensure that AI systems are developed and used in a way that respects patient privacy and data security, and that the outcomes of AI-assisted decision making are transparent and explainable. In summary, AI has the potential to greatly benefit the field of urology. Still, it is important to approach its use with caution and continue evaluating its impact as more research is conducted. Disclosure No.

New Horizons in Understanding and Developing Treatments for Human Reproductive Physiology

This comprehensive review delves into the cutting-edge advancements and emerging trends in the field of human reproductive physiology. It aims to shed light on the intricate processes governing human reproduction and the novel therapeutic approaches being developed to address various reproductive health issues. The review begins with a detailed exploration of the fundamental aspects of human reproductive systems, including hormonal regulation, gametogenesis, and the genetic underpinnings of reproductive functions. Significant attention is given to recent breakthroughs in treating infertility, highlighting new findings in both male and female reproductive health. The review discusses the evolution and current state of assisted reproductive technologies (ART), such as IVF, and their increasingly successful application in overcoming infertility. Furthermore, it explores the potential of personalized medicine in tailoring treatments to individual reproductive disorders, considering genetic, environmental, and lifestyle factors. Innovations in contraceptive technologies and their implications for reproductive health management are also examined. The review extends into the realm of reproductive genetics, emphasizing how genomic studies have enhanced our understanding of reproductive disorders and congenital anomalies. Ethical, social, and legal considerations surrounding the latest reproductive technologies are critically analyzed, recognizing the complex interplay between scientific advancements and societal norms. The review concludes by identifying future research directions, underscoring the importance of interdisciplinary approaches in advancing our understanding and treatment of human reproductive physiology. This article serves as a vital resource for researchers, clinicians, and policymakers, offering a comprehensive overview of the current landscape and future prospects in human reproductive health and medicine.

Solar roads – a new potential renewable energy for Great Britain

ABSTRACT Global warming has become a much more realised issue and an immediate threat, accelerating due to the anthropogenic carbon release associated with escalating energy demand. Consequently, pressures are building up to reduce anthropogenic carbon footprint by employing renewable energy resources, among which solar energy is the main. Solar roads are a new and innovative concept as they do not require land to be specifically allocated. This is because the road infrastructure already exists. Therefore, this emerging technique of embedding solar panels into pavements and roadways is becoming a more attractive proposition. However, a hurdle to its successful application is the lack of knowledge regarding its feasibility and viability. This study aims to evaluate the potential of solar roads to inform future feasibility and viability studies in varying contexts and implications. Within the scope of this study, the case of Great Britain is considered to evaluate the potential of how much energy can be generated via the solar roads technology that can be embedded in the country’s road infrastructure. A mathematical exercise is performed in which calculations are executed to develop a basic numerical model of the potential. Therefore, the study is quantitative, and the factors considered include seasonal changes, average daily traffic covering roads, tree and building shades, road types and sizes, solar irradiance, solar panel types, and alike. It is estimated that solar roads may supply up to 96.42% of the UK's total electricity, which is a substantially promising potential.

New perspectives on ‘Breathomics’: metabolomic profiling of non-volatile organic compounds in exhaled breath using DI-FT-ICR-MS

Breath analysis offers tremendous potential for diagnostic approaches, since it allows for easy and non-invasive sample collection. “Breathomics” as one major research field comprehensively analyses the metabolomic profile of exhaled breath providing insights into various (patho)physiological processes. Recent research, however, primarily focuses on volatile compounds. This is the first study that evaluates the non-volatile organic compounds (nVOCs) in breath following an untargeted metabolomic approach. Herein, we developed an innovative method utilizing a filter-based device for metabolite extraction. Breath samples of 101 healthy volunteers (female n = 50) were analysed using DI-FT-ICR-MS and biostatistically evaluated. The characterisation of the non-volatile core breathome identified more than 1100 metabolites including various amino acids, organic and fatty acids and conjugates thereof, carbohydrates as well as diverse hydrophilic and lipophilic nVOCs. The data shows gender-specific differences in metabolic patterns with 570 significant metabolites. Male and female metabolomic profiles of breath were distinguished by a random forest approach with an out-of-bag error of 0.0099. Additionally, the study examines how oral contraceptives and various lifestyle factors, like alcohol consumption, affect the non-volatile breathome. In conclusion, the successful application of a filter-based device combined with metabolomics-analyses delineate a non-volatile breathprint laying the foundation for discovering clinical biomarkers in exhaled breath.

Preresidency research output among US neurological surgery residents.

Research productivity is often used to evaluate candidates for neurosurgery residency. Official annual reports describe the mean total number of research products of successful applicants for each match cycle; however, the average number of indexed publications, the highest-valued research product, is not reported separately from other research products. The primary objectives of this study were to describe the distribution of preresidency indexed publication quantity among successful neurosurgery applicants from 2017 to 2021 and determine whether any change in publication quantity across application cycles existed. Secondary objectives included determining the rate at which the average publication quantity is increasing across application cycles, whether this increase is driven by high-output applicants alone, and if a performance ceiling has been reached. US doctor of medicine seniors applying to the senior author's institution between 2017 and 2021 and who successfully matched into any US neurosurgery program were included. Publication quantities were extracted using Scopus. Additional variables were extracted from residency applications. Mean (SD) and median (IQR) publication quantities were used to describe the distribution and compare across years. Applicants were ranked by descending publication count and divided into quartiles. Averages within each quartile were compared with respective quartiles across years. Averages of the top 10% most productive applicants were compared across years to determine if a performance ceiling existed. Overall, 93.2% of matched applicants were captured. The mean and median total numbers of publications for applicants who matched from 2017 to 2021 were 5.6 ± 8.3 and 3.0 (1.0, 7.0), respectively. The mean and median numbers of publications increased from 3.7 ± 5.3 and 2.0 (0.0, 5.0) in 2016-2017 to 8.1 ± 10.0 and 5.0 (2.0, 11.0) in 2020-2021 (p < 0.001). The distribution of publication quantity was right-skewed. Multivariable analysis determined the application year to be independently and positively correlated with publication quantity (β 1.07 [95% CI 0.71-1.42], p < 0.001). All quartiles observed an increased average number of publications across years (p < 0.001). The mean and median numbers for the top 10% increased from 15.8 ± 8.7 and 13.0 (10.8, 15.5) in 2016-2017, respectively, to 31.3 ± 16.0 and 25.0 (21.0, 35.5) in 2020-2021 (p < 0.001). Indexed publications account for a small portion of the total research products that successful neurosurgery candidates list on applications. A high number of publications is not necessary for candidates to match, with approximately 50% of all applicants who successfully matched having ≤ 5 publications and 25% having ≤ 2 publications. The average preresidency publication quantity has been increasing yearly among neurosurgery applicants. This increase was present across the applicant pool. Additionally, no performance ceiling was observed.

The Advancement and Obstacles in Improving the Stability of Nanocarriers for Precision Drug Delivery in the Field of Nanomedicine.

Nanocarriers have emerged as a promising class of nanoscale materials in the fields of drug delivery and biomedical applications. Their unique properties, such as high surface area- tovolume ratios and enhanced permeability and retention effects, enable targeted delivery of therapeutic agents to specific tissues or cells. However, the inherent instability of nanocarriers poses significant challenges to their successful application. This review highlights the importance of nanocarrier stability in biomedical applications and its impact on biocompatibility, targeted drug delivery, long shelf life, drug delivery performance, therapeutic efficacy, reduced side effects, prolonged circulation time, and targeted delivery. Enhancing nanocarrier stability requires careful design, engineering, and optimization of physical and chemical parameters. Various strategies and cutting-edge techniques employed to improve nanocarrier stability are explored, with a focus on their applications in drug delivery. By understanding the advances and challenges in nanocarrier stability, this review aims to contribute to the development and implementation of nanocarrier- based therapies in clinical settings, advancing the field of nanomedicine.

Ultrasimple size encoded microfluidic chip for rapid simultaneous multiplex detection of DNA sequences

Simultaneous multiplexed analysis can provide comprehensive information for disease diagnosis. However, the current multiplex methods rely on sophisticated barcode technology, which hinders its wider application. In this study, an ultrasimple size encoding method is proposed for multiplex detection using a wedge-shaped microfluidic chip. Driving by negative pressure, microparticles are naturally arranged in distinct stripes based on their sizes within the chip. This size encoding method demonstrates a high level of precision, allowing for accuracy in distinguishing 3–5 sizes of microparticles with a remarkable accuracy rate of up to 99%, even the microparticles with a size difference as small as 0.5 μm. The entire size encoding process is completed in less than 5 min, making it ultrasimple, reliable, and easy to operate. To evaluate the function of this size encoding microfluidic chip, three commonly co-infectious viruses’ nucleic acid sequences (including complementary DNA sequences of HIV and HCV, and DNA sequence of HBV) are employed for multiplex detection. Results indicate that all three DNA sequences can be sensitively detected without any cross-interference. This size-encoding microfluidic chip-based multiplex detection method is simple, rapid, and high-resolution, its successful application in serum samples renders it highly promising for potential clinical promotion.

Community Detection via Autoencoder-Like Nonnegative Tensor Decomposition.

Community detection aims at partitioning a network into several densely connected subgraphs. Recently, nonnegative matrix factorization (NMF) has been widely adopted in many successful community detection applications. However, most existing NMF-based community detection algorithms neglect the multihop network topology and the extreme sparsity of adjacency matrices. To resolve them, we propose a novel conception of adjacency tensor, which extends adjacency matrix to multihop cases. Then, we develop a novel tensor Tucker decomposition-based community detection method-autoencoder-like nonnegative tensor decomposition (ANTD), leveraging the constructed adjacency tensor. Distinct from simply applying tensor decomposition on the constructed adjacency tensor, which only works as a decoder, ANTD also introduces an encoder component to constitute an autoencoder-like architecture, which can further enhance the quality of the detected communities. We also develop an efficient alternative updating algorithm with convergence guarantee to optimize ANTD, and theoretically analyze the algorithm complexity. Moreover, we also study a graph regularized variant of ANTD. Extensive experiments on real-world benchmark networks by comparing 27 state-of-the-art methods, validate the effectiveness, efficiency, and robustness of our proposed methods.

Artificial Intelligence in the Diagnosis of Colorectal Cancer: A Literature Review.

The aim of this review is to explore the role of artificial intelligence in the diagnosis of colorectal cancer, how it impacts CRC morbidity and mortality, and why its role in clinical medicine is limited. A targeted, non-systematic review of the published literature relating to colorectal cancer diagnosis was performed with PubMed databases that were scouted to help provide a more defined understanding of the recent advances regarding artificial intelligence and their impact on colorectal-related morbidity and mortality. Articles were included if deemed relevant and including information associated with the keywords. The advancements in artificial intelligence have been significant in facilitating an earlier diagnosis of CRC. In this review, we focused on evaluating genomic biomarkers, the integration of instruments with artificial intelligence, MR and hyperspectral imaging, and the architecture of neural networks. We found that these neural networks seem practical and yield positive results in initial testing. Furthermore, we explored the use of deep-learning-based majority voting methods, such as bag of words and PAHLI, in improving diagnostic accuracy in colorectal cancer detection. Alongside this, the autonomous and expansive learning ability of artificial intelligence, coupled with its ability to extract increasingly complex features from images or videos without human reliance, highlight its impact in the diagnostic sector. Despite this, as most of the research involves a small sample of patients, a diversification of patient data is needed to enhance cohort stratification for a more sensitive and specific neural model. We also examined the successful application of artificial intelligence in predicting microsatellite instability, showcasing its potential in stratifying patients for targeted therapies. Since its commencement in colorectal cancer, artificial intelligence has revealed a multitude of functionalities and augmentations in the diagnostic sector of CRC. Given its early implementation, its clinical application remains a fair way away, but with steady research dedicated to improving neural architecture and expanding its applicational range, there is hope that these advanced neural software could directly impact the early diagnosis of CRC. The true promise of artificial intelligence, extending beyond the medical sector, lies in its potential to significantly influence the future landscape of CRC's morbidity and mortality.

Nanopore sequencing with T2T-CHM13 for accurate detection and preventing the transmission of structural rearrangements in highly repetitive heterochromatin regions in human embryos.

Structural rearrangements in highly repetitive heterochromatin regions can result in miscarriage or foetal malformations; however, detecting and preventing the transmission of these rearrangements has been challenging. Recently, the completion of sequencing of the complete human genome (T2T-CHM13) has made it possible to accurately characterise structural rearrangements in these regions. We developed a method based on T2T-CHM13 and nanopore sequencing to detect and block structural rearrangements in highly repetitive heterochromatin sequences. T2T-CHM13-based "Mapping Allele with Resolved Carrier Status" was performed for couples who carry structural rearrangements in heterochromatin regions. Using nanopore sequencing and the T2T-CHM13 reference genome, the precise breakpoints of inversions and translocations close to the centromere were detected and haplotypes were constructed using flanking single-nucleotide polymorphisms (SNPs). Haplotype linkage analysis was then performed by comparing consistent parental SNPs with embryonic SNPs to determine whether the embryos carried hereditary inversions or balanced translocations. Based on copy number variation and haplotype linkage analysis, we transplanted normal embryos, which were further verified by an amniotic fluid test. To validate this approach, we used nanopore sequencing of families with inversions and reciprocal translocations close to the centromere. Using the T2T-CHM13 reference genome, we accurately detected inversions and translocations in centromeres, constructed haplotypes and prevented the transmission of structural rearrangements in the offspring. This study represents the first successful application of T2T-CHM13 in human reproduction and provides a feasible protocol for detecting and preventing the transmission of structural rearrangements of heterochromatin in embryos.

Revolutionizing Kidney Transplantation: Connecting Machine Learning and Artificial Intelligence with Next-Generation Healthcare—From Algorithms to Allografts

This review explores the integration of artificial intelligence (AI) and machine learning (ML) into kidney transplantation (KT), set against the backdrop of a significant donor organ shortage and the evolution of ‘Next-Generation Healthcare’. Its purpose is to evaluate how AI and ML can enhance the transplantation process, from donor selection to postoperative patient care. Our methodology involved a comprehensive review of current research, focusing on the application of AI and ML in various stages of KT. This included an analysis of donor–recipient matching, predictive modeling, and the improvement in postoperative care. The results indicated that AI and ML significantly improve the efficiency and success rates of KT. They aid in better donor–recipient matching, reduce organ rejection, and enhance postoperative monitoring and patient care. Predictive modeling, based on extensive data analysis, has been particularly effective in identifying suitable organ matches and anticipating postoperative complications. In conclusion, this review discusses the transformative impact of AI and ML in KT, offering more precise, personalized, and effective healthcare solutions. Their integration into this field addresses critical issues like organ shortages and post-transplant complications. However, the successful application of these technologies requires careful consideration of their ethical, privacy, and training aspects in healthcare settings.

Analysis of Potential Environmental Risks in the Hydraulic Fracturing Operation in the “La Luna” Formation in Colombia

This article presents an in-depth analysis of potential environmental risks associated with hydraulic fracturing operations within the “La Luna” formation in Colombia. Using the Conesa methodology, it assesses the environmental impacts of unconventional reservoir production in Colombia, including water usage, chemical additives, air emissions, and the potential for groundwater contamination. This study incorporates comprehensive data on geological characteristics, operational procedures, and environmental conditions specific to the region. This analysis highlights the need for a proactive approach to managing potential environmental risks associated with hydraulic fracturing in Colombia. The findings underscore the importance of implementing robust regulatory measures, comprehensive monitoring systems, and the industry’s best practices to mitigate and prevent adverse environmental impacts. This research contributes to the ongoing global dialogue on the environmental implications of hydraulic fracturing in regions with sensitive ecological conditions. The environmental impacts of unconventional reservoirs are widely recognized as a general consensus; however, the absence of applied studies with the rigor of an explicit methodology in Andean countries highlights the need for specific research in this region. This article addresses this gap by proposing a detailed and structured methodology to assess and mitigate environmental impacts in unconventional reservoirs. Emphasizing the importance of knowing the characteristics of reservoir fluids, this research highlights that this critical information is only revealed by drilling exploration wells and PVT (pressure, volume, and temperature) analysis. Obtaining this data is crucial for shaping specific mitigation measures, thus allowing for the formulation of a robust environmental management plan tailored to the conditions of the reservoirs in the Andean region. This precise and contextualized approach contributes to closing the knowledge gap and promotes more sustainable practices in the exploitation of unconventional reservoirs in this specific geographical context. The methodology used in this study proved its effectiveness by accurately quantifying the risks associated with each of the environmental alterations inherent to the hydraulic fracturing process in the La Luna formation. The results obtained allowed for the identification of the critical points most susceptible to environmental impacts, serving as a solid basis for the elaboration of an environmental management plan. This strategic approach not only enabled the delineation of specific mitigation measures but also facilitated the selection of the most appropriate locations for the implementation of the plan, maximizing the effectiveness of corrective actions. It is noteworthy that the successful application of the Conesa methodology in this unconventional reservoir context evidences the versatility and applicability of this approach, consolidating it as a valuable tool for environmental assessment and effective planning in the hydraulic fracturing industry.

Assessment of process-induced cracks in hot-working operations using crack susceptibility index based on plastic instability criteria

A processing map representing the efficiency of power dissipation as a function of the temperature and strain rate has been used as a guide to depict the hot workability of metallic alloys. However, it has limitations in indicating the local positions where process-induced cracks may occur during hot-working operations. A process-induced crack is formed during hot deformation because the plastic instability associated with crack formation is more favorable than normal flow under a given set of process conditions. This paper proposes a novel approach to assess local cracks using the crack susceptibility index (CSI), which is based on Ziegler's plastic instability criteria. This solution provides useful information regarding the effect of each process parameter on the workability required for high-temperature processes. Lowering the CSI value by controlling process variables, such as the temperature and strain rate, is recommended for a design free from process-induced cracks. More importantly, a coded finite element subroutine coupled with the CSI can help visualize local positions where cracks may be produced during a hot-working operation. For experimental validation, a Gleeble test was performed on Ti6Al4V specimens containing four notches with different depths. The successful application of the CSI to the process design of shop-floor unmanned aircraft components helped confirm the applicability and validity of this study.

Face-Inception-Net for Recognition

Face recognition in general scenarios has been saturated in recent years, but there is still room to enhance model performance in extreme scenarios and fairness situations. Inspired by the successful application of Transformer and ConvNet in computer vision, we propose a FIN-Block, which gives a more flexible composition paradigm for building a novel pure convolution model and provides a foundation for constructing a new framework for general face recognition in both extreme scenarios and fairness situations. FIN-Block-A uses a combination of stacked large-size convolution kernels and parallel convolution branches to ensure a large spatial receptive field while improving the module’s deep feature embedding and extraction capabilities. FIN-Block-B takes advantage of stacked orthogonal convolution kernels and parallel branches to balance model size and performance. By applying FIN-Block with an adapted convolution kernel size in different stages, we built a reasonable and novel framework Face-Inception-Net, and the performance of the model is highly competitive with ConvNeXt and InceptionNeXt. The models were trained on CASIA-WebFace and MS-wo-RFW databases and evaluated on 14 mainstream benchmarks, including LFW, extreme scene, and fairness test sets. The proposed Face-Inception-Net achieved the highest average TAR@FAR0.001 of 95.9% in all used benchmarks, fully demonstrating effectiveness and generality in various scenarios.

Extracellular Vesicles: A New Star for Gene Drug Delivery.

Recently, gene therapy has become a subject of considerable research and has been widely evaluated in various disease models. Though it is considered as a stand-alone agent for COVID-19 vaccination, gene therapy is still suffering from the following drawbacks during its translation from the bench to the bedside: the high sensitivity of exogenous nucleic acids to enzymatic degradation; the severe side effects induced either by exogenous nucleic acids or components in the formulation; and the difficulty to cross the barriers before reaching the therapeutic target. Therefore, for the successful application of gene therapy, a safe and reliable transport vector is urgently needed. Extracellular vesicles (EVs) are the ideal candidate for the delivery of gene drugs owing to their low immunogenicity, good biocompatibility and low toxicity. To better understand the properties of EVs and their advantages as gene drug delivery vehicles, this review covers from the origin of EVs to the methods of EVs generation, as well as the common methods of isolation and purification in research, with their pros and cons discussed. Meanwhile, the engineering of EVs for gene drugs is also highlighted. In addition, this paper also presents the progress in the EVs-mediated delivery of microRNAs, small interfering RNAs, messenger RNAs, plasmids, and antisense oligonucleotides. We believe this review will provide a theoretical basis for the development of gene drugs.

Tool‐tip vibration prediction based on a novel convolutional enhanced transformer

AbstractSuperior surface finish remains a fundamental criterion in precision machining operations, and tool‐tip vibration is an important factor that significantly influences the quality of the machined surface. Physics‐based models heavily rely on assumptions for model simplification when applied to complex high‐end systems. However, these assumptions may come at the cost of compromising the model's accuracy. In contrast, data‐driven techniques have emerged as an attractive alternative for tasks such as prediction and complex system analysis. To exploit the advantages of data‐driven models, this study introduces a novel convolutional enhanced transformer model for tool‐tip vibration prediction, referred to as CeT‐TV. The effectiveness of this model is demonstrated through its successful application in ultra‐precision fly‐cutting (UPFC) operations. Two distinct variants of the model, namely, guided and nonguided CeT‐TV, were developed and rigorously tested on a data set custom‐tailored for UPFC applications. The results reveal that the guided CeT‐TV model exhibits outstanding performance, characterized by the lowest mean absolute error and root mean square error values. Additionally, the model demonstrates excellent agreement between the predicted values and the actual measurements, thus underlining its efficiency and potential for predicting the tool‐tip vibration in the context of UPFC.

Coupled Stable Isotope Tracing and Sulfamic Acid Reduction (SIT-SAR) Method to Determine the Ammonia and Nitrite Oxidation Rates in Water and Sediments.

Herein, a method was developed to measure the ammonia oxidation rate (Ra) and the nitrite oxidation rate (Rn) of water and sediment samples using a coupled stable isotope tracing and sulfamic acid reduction (SIT-SAR) method. 15NH4+ was used as a tracer to determine the ammonia oxidation rates (Ra) by calculating the concentrations of produced 15NO2- and 15NO3- during incubation, while 15NO2- was used as a tracer to determine the nitrite oxidation rates (Rn) by calculating the increase of 15NO3- during incubation. 15NO2- was chemically reduced to 29N2 with 15 mmol·L-1 sulfamic acid (SA). 15NO3- was first reduced to 15NO2- with a zinc-cadmium reducing agent, and then 15NO2- was subsequently reduced to 29N2 with SA. The produced 29N2 was measured by a membrane inlet mass spectrometer (MIMS). Under optimized experimental conditions, this method provides a sensitive (detection limit: 0.5 μmol·L-1) and precise (relative standard deviation: 4.80% for 15NO2-, 3.82% for 15NO3-) approach to quantify the concentrations of 15NO2- (0.5-150 μmol·L-1) and 15NO3- (0.5-120 μmol·L-1) in water and sediment samples over a wide range of salinities (0-30‰) with excellent calibration curves (R2 ≥ 0.999). This method was a successful application to estuarine water and sediments along the salinity gradient. Overall, the SIT-SAR method provided a rapid, accurate, and cost-effective means to determine Ra and Rn simultaneously.

Modified Essix Twin Block Appliance: Successful Application in Adult Patient- A Case Report

Twin-block appliance has proven its efficiency in adolescents. William Clark has given this appliance to non-growing patients also. Here we gave the appliance to a 20-year-old male. Appliance fabrication was done with a thermoformed sheet and acrylic. With this appliance, we achieved dento-alveolar and skeletal changes in a 20-year-old male patient. Generally, twin block has disadvantages like lower incisor proclination, poor periodontal health issues, and patient hesitance to wear the appliance. In this design, we have overcome the issues mentioned above. Soft tissue profile has also improved. Total treatment for appliance therapy was 8 months.

An Explainable Prediction for Dietary-Related Diseases via Language Models.

Our study harnesses the power of natural language processing (NLP) to explore the relationship between dietary patterns and metabolic health outcomes among Korean adults using data from the Seventh Korea National Health and Nutrition Examination Survey (KNHANES VII). Using Latent Dirichlet Allocation (LDA) analysis, we identified three distinct dietary patterns: "Traditional and Staple", "Communal and Festive", and "Westernized and Convenience-Oriented". These patterns reflect the diversity of dietary preferences in Korea and reveal the cultural and social dimensions influencing eating habits and their potential implications for public health, particularly concerning obesity and metabolic disorders. Integrating NLP-based indices, including sentiment scores and the identified dietary patterns, into our predictive models significantly enhanced the accuracy of obesity and dyslipidemia predictions. This improvement was consistent across various machine learning techniques-XGBoost, LightGBM, and CatBoost-demonstrating the efficacy of NLP methodologies in refining disease prediction models. Our findings underscore the critical role of dietary patterns as indicators of metabolic diseases. The successful application of NLP techniques offers a novel approach to public health and nutritional epidemiology, providing a deeper understanding of the diet-disease nexus. This study contributes to the evolving field of personalized nutrition and emphasizes the potential of leveraging advanced computational tools to inform targeted nutritional interventions and public health strategies aimed at mitigating the prevalence of metabolic disorders in the Korean population.

Multi-centers experience using therapeutic plasma exchange for corticosteroid/tocilizumab-refractory cytokine release syndrome following CAR-T therapy

The chimeric antigen receptor T (CAR-T) cell therapy significantly enhances the prognosis of various hematologic malignancies; however, the systemic expansion of CAR-T cells also gives rise to severe cytokine release syndrome (CRS), and immune effector cell-associated neurotoxicity syndrome (ICANS). Despite the successful application of corticosteroids and tocilizumab in alleviating severe CRS in most patients, there are still individuals who experience life-threatening CRS without responding to the aforementioned therapies. In our retrospective cohort, we conducted an analysis of clinical and laboratory parameters, including inflammatory cytokines, in 17 patients from three centers who underwent therapeutic plasma exchange (TPE) for refractory CRS with or without ICANS following CAR-T products treatment. Our findings demonstrate a significant improvement in both clinical symptoms and laboratory parameters subsequent to TPE treatment. The rapid decrease in temperature and levels of inflammatory indexes indicates the remarkable scavenging efficacy of TPE against cytokine storm following CAR-T therapy. In conclusion, TPE may serve as a valuable and safe adjunct to corticosteroids and tocilizumab in the management of severe CRS resulting from CAR-T cell infusion. We eagerly await further prospective studies to validate this finding.