World journal of methodology最新文献

Inflammatory bowel disease (IBD) is a progressive multifactorial inflammatory disease of the gut. The cause of IBD is yet unknown. Some researchers have shown that genetic factors, environmental factors, and the gut microbiome are significant considerations. Our gut contains gut virome and gut bacteria, which vary among individuals due to some factors. The gut virome is a substantial component of the microbiome. This editorial explores the emerging role of gut virome in IBD.

Accurate prediction of nurse demand plays a crucial role in efficiently planning the healthcare workforce, ensuring appropriate staffing levels, and providing high-quality care to patients. The intricacy and variety of contemporary healthcare systems and a growing patient populace call for advanced forecasting models. Factors like technological advancements, novel treatment protocols, and the increasing prevalence of chronic illnesses have diminished the efficacy of traditional estimation approaches. Novel forecasting methodologies, including time-series analysis, machine learning, and simulation-based techniques, have been developed to tackle these challenges. Time-series analysis recognizes patterns from past data, whereas machine learning uses extensive datasets to uncover concealed trends. Simulation models are employed to assess diverse scenarios, assisting in proactive adjustments to staffing. These techniques offer distinct advantages, such as the identification of seasonal patterns, the management of large datasets, and the ability to test various assumptions. By integrating these sophisticated models into workforce planning, organizations can optimize staffing, reduce financial waste, and elevate the standard of patient care. As the healthcare field progresses, the utilization of these predictive models will be pivotal for fostering adaptable and resilient workforce management.

Background: Recently, the identification of cell apoptosis induced by natural products has become research hotspot and frontier in the biopharmaceutical and food industries under the umbrella of global green development worldwide. Traditionally, cell apoptosis is identified using morphological, biochemical, and cell cycle experiments, which is time consuming, and experimental materials are not from the same group, and it is very hard to ensure the identity and veracity of results of former and latter experiments.

Aim: To establish a selective, instant, and practical protocol to identify cell apoptosis induced by natural products.

Methods: A one transient cell processing procedure (OTCPP) was used to detect human colorectal cancer LoVo cell apoptosis after treatment with Pinus massoniana bark extract (PMBE) at the morphological, biochemical, and cell cycle levels. The methods used included treatment with DNA gel electrophoresis, fluorescence microscopy, and flow cytometry.

Results: In PMBE-treated LoVo cells, we observed a DNA ladder on gel electrophoresis and fluorescence microscopy revealed "nuclear shrinkage, chromatin condensation or fragmentation". In addition, flow cytometry showed an "obvious apoptosis curve". Thus OTCPP achieved synchronous detection of the morphology, biochemistry, cell cycle, and the DNA content of the cells.

Conclusion: OTCPP can quickly identify apoptosis and measure the apoptosis rate, thereby unifying qualitative and quantitative analysis.

Clinical inertia (CI) is common in clinical practice. Sexual health issues are common in society, and CI is ubiquitous in sexual medicine practice. CI influences all aspects of healthcare, including prevention, diagnosis, and treatment. In this short review, we briefly describe the various aspects of CI in sexual medicine practice and ways to tackle them.

Inflammatory bowel disease (IBD), encompassing Crohn's disease and ulcerative colitis, is a chronic condition marked by recurring gastrointestinal inflammation. While immune, genetic, and environmental factors are well-studied, the gut virome has received less attention. This editorial highlights the work which investigates the gut virome's role in IBD and its interactions with the bacterial microbiome and host immune system. The gut virome consists of bacteriophages, eukaryotic viruses, and endogenous retroviruses. Among these, Caudovirales bacteriophages are predominant and influence bacterial communities via lysogenic and lytic cycles. Eukaryotic viruses infect host cells directly, while endogenous retroviruses impact gene regulation and immune responses. In IBD, the virome shows distinct alterations, including an increased abundance of Caudovirales phages and reduced Microviridae diversity, suggesting a pro-inflammatory viral environment. Dysbiosis, chronic inflammation, and aberrant immune responses contribute to these changes by disrupting microbial communities and modifying virome composition. Phages affect bacterial dynamics through lysis, lysogeny, and horizontal gene transfer, shaping microbial adaptability and resilience. Understanding these interactions is crucial for identifying novel therapeutic targets and restoring microbial balance in IBD.

This analytical research paper explores the transformative impact of artificial intelligence (AI) in orthodontics, with a focus on its objectives: Identifying current applications, evaluating benefits, addressing challenges, and projecting future developments. AI, a subset of computer science designed to simulate human intelligence, has seen rapid integration into orthodontic practice. The paper examines AI technologies such as machine learning, deep learning, natural language processing, computer vision, and robotics, which are increasingly used to analyze patient data, assist with diagnosis and treatment planning, automate routine tasks, and improve patient communication. AI systems offer precise malocclusion diagnoses, predict treatment outcomes, and customize treatment plans by leveraging dental imagery. They also streamline image analysis, improve diagnostic accuracy, and enhance patient engagement through personalized communication. The objectives include evaluating the benefits of AI in terms of efficiency, accuracy, and personalized care, while acknowledging the challenges like data quality, algorithm transparency, and practical implementation. Despite these hurdles, AI presents promising prospects in advanced imaging, predictive analytics, and clinical decision-making. In conclusion, AI holds the potential to revolutionize orthodontic practices by improving operational efficiency, diagnostic precision and patient outcomes. With collaborative efforts to overcome challenges, AI could play a pivotal role in advancing orthodontic care.

Background: The primary issue in managing edentulous patients is the severely resorbed mandibular ridge, particularly in older individuals with diminished adaptive capacities. This compromised situation leads to the fabrication of inadequate dentures that lack retention and stability, potentially causing psychosocial issues.

Aim: To determine the difference in retentive capacity between three attachment systems in implant-retained overdentures.

Methods: Three edentulous mandibular models were fabricated using heat-cured polymethacrylate resin, with two implant replicas placed in the intra-foraminal region of each model. 30 acrylic resin mandibular overdentures were fabricated with provisions for three different overdenture attachment systems: A prefabricated ball/O-ring attachment, a locator attachment system, and an equator attachment system. Each model was subjected to 15000 pulls using a universal testing machine to remove the overdenture from the acrylic model and the force data were recorded.

Results: The ball/O-ring attachment system demonstrated superior retentive capacity for 15 years, while the locator and equator attachment systems maintained excellent retentive capacity for 5 years.

Conclusion: The ball/O-ring attachment system outperformed better than the other two attachment systems regarding retentive capacity. The locator and equator attachment systems presented sufficient retentive abilities until 15000 cycles. After 7500 cycles, significant differences in retentive force between the systems evolved.

Background: Knowledge-based systems (KBS) are software applications based on a knowledge database and an inference engine. Various experimental KBS for computer-assisted medical diagnosis and treatment were started to be used since 70s (VisualDx, GIDEON, DXPlain, CADUCEUS, Internist-I, Mycin etc.).

Aim: To present in detail the "Electronic Pediatrician (EPed)", a medical non-machine learning artificial intelligence (nml-AI) KBS in its prototype version created by the corresponding author (with database written in Romanian) that offers a physiopathology-based differential and positive diagnosis and treatment of ill children.

Methods: EPed specifically focuses on the physiopathological reasoning of pediatric clinical cases. EPed has currently reached its prototype version 2.0, being able to diagnose 302 physiopathological macro-links (briefly named "clusters") and 269 pediatric diseases: Some examples of diagnosis and a previous testing of EPed on a group of 34 patients are also presented in this paper.

Results: The prototype EPed can currently diagnose 269 pediatric infectious and non-infectious diseases (based on 302 clusters), including the most frequent respiratory/digestive/renal/central nervous system infections, but also many other non-infectious pediatric diseases like autoimmune, oncological, genetical diseases and even intoxications, plus some important surgical pathologies.

Conclusion: EPed is the first and only physiopathology-based nml-AI KBS focused on general pediatrics and is the first and only pediatric Romanian KBS addressed to medical professionals. Furthermore, EPed is the first and only nml-AI KBS that offers not only both a physiopathology-based differential and positive disease diagnosis, but also identifies possible physiopathological "clusters" that may explain the signs and symptoms of any child-patient and may help treating that patient physiopathologically (until a final diagnosis is found), thus encouraging and developing the physiopathological reasoning of any clinician.

The mitochondrial DNA copy number (mtDNAcn) plays a vital role in cellular energy metabolism and mitochondrial health. As mitochondria are responsible for adenosine triphosphate production through oxidative phosphorylation, maintaining an appropriate mtDNAcn level is vital for the overall cellular function. Alterations in mtDNAcn have been linked to various diseases, including neurodegenerative disorders, metabolic conditions, and cancers, making it an important biomarker for understanding the disease pathogenesis. The accurate estimation of mtDNAcn is essential for clinical applications. Quantitative polymerase chain reaction and next-generation sequencing are commonly employed techniques with distinct advantages and limitations. Clinically, mtDNAcn serves as a valuable indicator for early diagnosis, disease progression, and treatment response. For instance, in oncology, elevated mtDNAcn levels in blood samples are associated with tumor aggressiveness and can aid in monitoring treatment efficacy. In neurodegenerative diseases such as Alzheimer's and Parkinson's, altered mtDNAcn patterns provide insights into disease mechanisms and progression. Understanding and estimating mtDNAcn are critical for advancing diagnostic and therapeutic strategies in various medical fields. As research continues to uncover the implications of mtDNAcn alterations, its potential as a clinical biomarker is likely to expand, thereby enhancing our ability to diagnose and manage complex diseases.

Conventional echocardiography can sometimes pose a challenge to diagnosis due to sub-optimal images. Ultrasound contrast agents (UCAs) have been shown to drastically enhance imaging quality, particularly depicting the left ventricular endocardial borders. Their use during echocardiography has become a valuable tool in non-invasive diagnostics. UCAs provide higher-quality images that may ultimately reduce the length of hospital stays and improve patient care. The higher cost associated with UCAs in many situations has been an impediment to frequent use. However, when used as an initial diagnostic test, UCA during rest echocardiogram is more cost-effective than the traditional diagnostic approach, which frequently includes multiple tests and imaging studies to make an accurate diagnosis. They can be easily performed across multiple patient settings and provide optimal images that allow clinicians to make sound medical decisions. This consequently allows for better diagnostic accuracies and improvement in patient care.