Diagnostics最新文献

Type 2 diabetes mellitus is a complex metabolic disorder characterized by insulin resistance and, subsequently, decreased insulin secretion. This condition is closely linked to obesity, a major risk factor that boosts the development of chronic systemic inflammation, which, in turn, is recognized for its crucial role in the onset of insulin resistance. Under conditions of obesity, adipose tissue, particularly visceral fat, becomes an active endocrine organ that releases a wide range of pro-inflammatory mediators, including cytokines, chemokines, and adipokines. These mediators, along with cluster of differentiation (CD) markers, contribute to the maintenance of systemic low-grade inflammation, promote cellular signaling and facilitate the infiltration of inflammatory cells into tissues. Emerging studies have indicated the accumulation of a new cell population in the adipose tissue in these conditions, known as myeloid-derived suppressor cells (MDSCs). These cells possess the ability to suppress the immune system, impacting obesity-related chronic inflammation. Given the limited literature addressing the role of MDSCs in the context of type 2 diabetes, this article aims to explore the complex interaction between inflammation, obesity, and MDSC activity. Identifying and understanding the role of these immature cells is essential not only for improving the management of type 2 diabetes but also for the potential development of targeted therapeutic strategies aimed at both glycemic control and the reduction in associated inflammation.

Background: Knee osteoarthritis (OA) is a prevalent degenerative joint disease significantly impacting global health. Early and accurate diagnosis is crucial for effective management, but traditional methods often rely on subjective assessments. This study evaluates the efficacy of a deep learning model implemented through a no-code AI platform for diagnosing and grading knee OA from plain radiographs. Methods: We utilized the Osteoarthritis Initiative (OAI) dataset, comprising knee X-ray data from 1526 patients. The data were split into training (47.0%), validation (26.5%), and test (26.5%) sets. We employed a ResNet101 model on the DEEP:PHI no-code AI platform for image analysis. The model was trained to classify knee OA into five grades (0-4) based on the Kellgren-Lawrence scale. Results: Our AI model demonstrated high accuracy in distinguishing between different OA grades, with particular strength in early-stage detection. The model achieved optimal performance at 20 epochs, suggesting efficient learning dynamics. Grad-CAM visualizations were used to enhance the interpretability of the model's decision-making process. Conclusions: This study demonstrates the potential of AI, implemented through a no-code platform, to accurately diagnose and grade knee OA from radiographs. The use of a no-code AI platform such as DEEP:PHI represents a step towards democratizing AI in healthcare, enabling the rapid development and deployment of sophisticated medical AI applications without extensive coding expertise. This approach could significantly enhance the early detection and management of knee OA, potentially improving patient outcomes and streamlining clinical workflows.

Digital high-speed videomicroscopy (DHSV) is a crucial tool for evaluating ciliary function in children suspected of primary ciliary dyskinesia (PCD). However, until now, samples are taken without anesthesia due to uncertainty about its effect on ciliary function and DHSV interpretation. This study aimed to investigate the impact of general anesthesia on ciliary functional analysis by DHSV in a series of three patients listed for ENT surgeries, which could improve diagnostic procedures for pediatric patients. Patient 1 (7-year-old girl) underwent adenotonsillectomy and tympanostomy placement tube, while patients 2 (17-month-old boy) and 3 (15-month-old girl) underwent adenoidectomy and tympanostomy placement tube. All patients underwent nasal brushing before general anesthesia (control sample). Experimental samples were taken in the contralateral nostril at the time of equilibration of the anesthetic agents (sevoflurane, propofol, sufentanil). Ciliary beat frequency and pattern were measured using digital high-speed videomicroscopy. Our findings highlighted the variability of respiratory ciliary function under general anesthesia among individuals. Our results emphasize the need for caution when interpreting ciliary function data obtained during general anesthesia. Further research with larger cohorts is warranted for validation.

Background: Dental care has been transformed by neural networks, introducing advanced methods for improving patient outcomes. By leveraging technological innovation, dental informatics aims to enhance treatment and diagnostic processes. Early diagnosis of dental problems is crucial, as it can substantially reduce dental disease incidence by ensuring timely and appropriate treatment. The use of artificial intelligence (AI) within dental informatics is a pivotal tool that has applications across all dental specialties. This systematic literature review aims to comprehensively summarize existing research on AI implementation in dentistry. It explores various techniques used for detecting oral features such as teeth, fillings, caries, prostheses, crowns, implants, and endodontic treatments. AI plays a vital role in the diagnosis of dental diseases by enabling precise and quick identification of issues that may be difficult to detect through traditional methods. Its ability to analyze large volumes of data enhances diagnostic accuracy and efficiency, leading to better patient outcomes.

Methods: An extensive search was conducted across a number of databases, including Science Direct, PubMed (MEDLINE), arXiv.org, MDPI, Nature, Web of Science, Google Scholar, Scopus, and Wiley Online Library.

Results: The studies included in this review employed a wide range of neural networks, showcasing their versatility in detecting the dental categories mentioned above. Additionally, the use of diverse datasets underscores the adaptability of these AI models to different clinical scenarios. This study highlights the compatibility, robustness, and heterogeneity among the reviewed studies. This indicates that AI technologies can be effectively integrated into current dental practices. The review also discusses potential challenges and future directions for AI in dentistry. It emphasizes the need for further research to optimize these technologies for broader clinical applications.

Conclusions: By providing a detailed overview of AI's role in dentistry, this review aims to inform practitioners and researchers about the current capabilities and future potential of AI-driven dental care, ultimately contributing to improved patient outcomes and more efficient dental practices.

A 54-year-old patient presented in our clinic with pressure in the upper abdomen, dyspnea and abdominal distension. The clinical examination showed pleural effusion, ascites and an enlarged axillary lymph node on the right side. In gynecological sonography ascites, an ovarian cyst and peritoneal carcinosis in the pouch of Douglas were detected, which were potentially indicative of ovarian cancer. A staging laparoscopy was performed to confirm the diagnosis of ovarian cancer and to evaluate operability. Intraoperatively white milky ascites, white-yellow marbling of the liver and white stipple bedding on the diaphragm and liver were detected. The ovaries and the fallopian tubes were tumorously enlarged. Biopsies were taken from the right fimbrial funnel, the liver around the falciform ligament and the diaphragm. Histology of all abdominal biopsies and the axillary lymph node revealed high lymphatic infiltration matching a stage III B-cell-lymphoma. The patient was transferred to the hemato-oncological department for further therapy. Six cycles of cytostatic therapy with R-CHOP (rituximab, cyclophosphamide, hydroxydaunorubicin, vincristine sulfate, prednisone) were initiated. The patient is doing well and in stable disease 6 months after completion of cytotoxic therapy. This case report presents a rare case of manifestation of an extra nodal B-cell-lymphoma with abdominal presentation that mimicked ovarian cancer.

Aortic stenosis (AS) is associated with the development of replacement myocardial fibrosis/scar. Given the dose-dependent relationship between scar and clinical outcomes after aortic valve replacement (AVR) surgery, scar quantity may serve as an important risk-stratification tool to aid decision-making on the optimal timing of AVR. Scar is non-invasively assessed and quantified by cardiovascular magnetic resonance (CMR) imaging. Several quantification techniques exist, and consensus on the optimal technique is lacking. These techniques range from a visual manual method to fully automated ones. This review describes the different scar quantification techniques used and highlights their strengths and shortfalls within the context of AS. The two most commonly used techniques in AS include the semi-automated signal threshold versus reference mean (STRM) and full-width half-maximum (FWHM) techniques. The accuracy and reproducibility of these techniques may be hindered in AS by the coexistence of diffuse interstitial fibrosis and the presence of relatively small, non-bright scars. The validation of these techniques against histology, which is the current gold standard for scar quantification in AS, is limited. Based on the best current evidence, the STRM method using a threshold of three standard deviations above the mean signal intensity of remote myocardium is recommended. The high reproducibility of the FWHM technique in non-AS cohorts has been shown and merits further evaluation within the context of AS. Future directions include the use of quantitative T1 mapping for the detection and quantification of scar, as well as the development of serum biomarkers that reflect the fibrotic status of the myocardium in AS.

Pregnancy is a vulnerable period of time during which pregnant people are prone to infections like COVID-19, which can increase risks for both the mother and fetus. These infections may lead to complications such as preterm birth, developmental delays, and congenital abnormalities. While COVID-19 poses additional risks like placental dysfunction and neonatal infections, studies on long-term effects remain limited. Ongoing research and monitoring are essential to understand and mitigate potential cognitive and developmental challenges in children born to mothers infected with COVID-19. This review aims to guide clinicians in managing these risks throughout childhood. Maternal COVID-19 infection during pregnancy can have significant implications for fetal development, even if the newborn is not infected at birth. The release of inflammatory cytokines may cross the placental barrier, potentially disrupting fetal brain development and increasing the risk of long-term cognitive and behavioral issues, such as ADHD or autism. Placental dysfunction, caused by inflammation or thrombosis, can lead to intrauterine growth restriction (IUGR), preterm birth, or hypoxia, affecting both neurological and respiratory health in newborns. Furthermore, a compromised fetal immune system can increase susceptibility to autoimmune conditions and infections. The early diagnosis and management of infections during pregnancy are crucial in mitigating risks to both the mother and fetus. Swift intervention can prevent complications like preterm birth and long-term developmental challenges, ensuring better health outcomes for both the mother and child. Long-term monitoring of children born to mothers infected with COVID-19 is necessary to understand the full extent of the virus's impact. This review evaluates the long-term systemic effects of maternal COVID-19 infection during pregnancy on fetuses, newborns, and children, focusing beyond vertical transmission. It highlights the broader impacts on fetal development, offering insights to help clinicians manage potential issues that may arise later in life.

Asthma is a chronic inflammatory process that leads to airway narrowing, causing breath loss followed by spasms, wheezing, and shortness of breath. Within the asthmatic lungs, interaction among various immune cells and structural cells plays a significant role in orchestrating an inflammatory response in which eosinophils hold central importance. In these settings, allergens or other environmental exposures commonly drive the immune response to recruit eosinophils to the airways. The appearance of eosinophils in the airways indicates a dynamic interplay of various cell types within lung tissue and does not represent a passive effect of inflammation. The cellular cross-talk causes the persistence of eosinophilic inflammation, and if left untreated, it results in long-term damage to the airway structure and function. Further exacerbation of the condition occurs because of this. We discuss how this complex interplay of eosinophils, immune, and structural cells within the airway microenvironment leads to the distinct pathophysiological features in asthma, the variability in disease severity, and the response to biological treatments.

Background: Prostate cancer is one of the most prevalent cancers in the male population. To determine the aggressiveness of suspected lesions precisely, predictive models are increasingly being developed using quantitative MRI measurements, and particularly the ADC value. This study aimed to determine whether ADC values could be used to establish the aggressiveness of prostate cancer.

Methods: A retrospective single-center study included 398 patients with prostate cancer who underwent a multiparametric MRI prior to radical prostatectomy. DWI ADC values were measured (µm²/s) using b values of 50 and 1000. The dominant lesion best visualized on MRI was analyzed. The ADC values of the index lesion and reference tissue were compared to tumor aggressivity according to the Gleason grade groups based on radical prostatectomy results. Statistical analysis was performed using the Mann-Whitney U test, Kruskal-Wallis H test, Spearman's rank correlation, and ROC curves.

Results: A very strong negative correlation (rs = -0.846, p < 0.001) between ADC and GS was found. ROC analysis revealed an AUC of 0.958 and an ADC threshold value of 758 µm²/s in clinically significant prostate cancer diagnoses using the absolute ADC value, with no advantage of using the ADC ratio over the absolute ADC value being identified.

Conclusion: DWI ADC values and the calculated ADC ratio have a significant inverse correlation with GS. The findings indicate a strong capability in determining prostate cancer aggressiveness, as well as the possibility of assisting with assigning PI-RADS categories using ADC as quantitative metrics.

Background: Sentinel lymph node biopsy (SLNB) has become a method more and more frequently used in loco-regional breast cancer in the initial stages. Starting from the first report on the technical feasibility of the sentinel node method in breast cancer, published by Krag (1993) and Giuliano (1994), the method underwent numerous improvements and was also largely used worldwide. Methods: This article is a prospective study that took place at the "SJUPBT Surgery Clinic Timisoara" over a period of 1 year between July 2023 and July 2024, during which 137 underwent sentinel lymph node biopsy (SLNB) based on the current guidelines. For the identification of sentinel lymph nodes, we used various methods, including single traces and also a dual tracer and triple tracer. Results: Breast density represents a predictive biomarker for the identification rate of a sentinel node, being directly correlated with BMI (above 30 kg/m²) and with an age of above 50 years. The classification of the patients according to breast density represents an important criterion given that an adipose breast density (Tabar-Gram I-II) represents a lower IR of SLN compared with a density of the fibro-nodular type (Tabar-Gram III-V). We did not obtain any statistically significant data for the linear correlations between IR and the molecular profile, whether referring to the luminal subtypes (Luminal A and Luminal B) or to the non-luminal ones (HER2+ and TNBC), with p > 0.05, 0.201 [0.88, 0.167]; z = 1.82.