6 articles
Follicular lymphoma (FL) is a slow-growing B-cell lymphoma with a generally favorable prognosis. Nevertheless, its clinical course is heterogeneous, with a significant subset of patients experiencing early progression or histological transformation into diffuse large B-cell lymphoma (DLBCL), both considered to be high-risk events associated with treatment resistance and markedly inferior outcomes. Importantly, clinical risk factors have limited value in predicting these complications. This review outlines the key biologic features of FL, discussing how the novel molecular biology approaches can explain the clinical heterogeneity and high-risk disease evolution of FL.
A focused literature review was conducted using the PubMed/MEDLINE database to identify studies on follicular lymphoma and its histological transformation to diffuse large B-cell lymphoma. Priority was given to original research or review articles investigating genetic, epigenetic, transcriptional, or microenvironmental determinants of FL.
Evidence from early cytogenetic and DNA sequencing studies established BCL2 deregulation as an initiating lesion in FL, with further genetic alterations in epigenetic regulators like KMT2D, EZH2, CREBBP/EP300 occurring early on and persisting throughout the disease course. Studies of transformed FL samples indicate that aggressive evolution is associated with acquisition of additional genetic lesions, such as those affecting the cell cycle regulators CDKN2A/2B and TP53. More recently, integrated genomic, transcriptomic and spatial resolved techniques have demonstrated substantial transcriptional heterogeneity within individual genetic subclones, suggesting that the genotype alone does not determine the phenotype of the malignant cells and supporting a pathogenetic model in which clinical trajectories reflect the combined effects of genomic evolution, transcriptional cell state, and tumor-microenvironment crosstalk. Important findings, including greater infiltration with LAG3+CD8+ T cells in cases of histological transformation to DLBCL and upregulation of transcriptional programs that promote stromal expansion and B-cell receptor signaling in cases of early FL relapse, indicate that integrated profiling represents a promising avenue for identifying the biomarkers and treatment targets that are specific to high-risk disease.
Continued research concentrated on multiomic profiling of both malignant and non-malignant tumor compartments is essential in order to reveal the mechanisms of FL heterogeneity and translate these data into practical biomarkers and therapeutic strategies.
Inflammation is a state driven by pathogenic stimuli. Trauma is one of the causes of acute onset of the inflammatory pathway. Multiple proteases are capable of inducing distant multiple organ lesions (lungs, brain or spinal cord, heart, kidney, liver and systemic vessel endothelium). The onset of corresponding syndromes will complicate the clinical course of that particular patient. These molecules are potential biomarkers in trauma patients.
There were reviewed the PubMed, Elsevier, ResearchGate, Google Scholar, Cochrane Library, medRxiv databases using the keywords “proteases”, “antiproteases” and “trauma”. A total of 114 relevant sources were included. An additional74 papers were selected. Overall there 188 literature sources were reviewed.
There are six classes of proteases: aspartic, glutamic, metalloproteases, cysteine, serine, and threonine proteases of which the glutamic ones are not found in mammals. Multiple processes that involve protein degradation are the fundamental mechanisms through which they mediate tissue and organ destruction after trauma-mediated inflammation. Certain inhibitors of the aforementioned proteases are of importance in these processes – they are vital in the prevention of pathophysiological processes such as fibrosis, although in the case of trauma due to their depletion there is high activity of the proteases system. The release of the protease/antiprotease system is mediated through by leukocytes, thrombocytes, myocytes and endothelium.
In this literature review there was described a high variety of protease and antiproteases. There is an increased complexity for the potential treatment of the distant lesions, thus the necessity for symptomatic treatment is foremost in order to diminish the lesions of the acute phase.
Aesthetic and functional considerations have always been the main concerns in the orthodontic treatment of dentoalveolar malocclusions. The objective of this study was to assess the effectiveness of the use of wide and extra-wide archwires in reducing treatment time, compared with conventional archwire therapy.
A retrospective cohort study was performed. A total of 180 patients aged between 14 and 36 years old were enrolled and divided into two groups: a classical treatment group, including standard NiTi/SS archwires (n=100), and an alternative treatment group with wide and extra-wide CuNiTi/TA/TMA/SS archwires (n=80). The treatment period, estimated in months, was considered the primary outcome. As secondary outcomes, inter-canine, inter-premolar, and inter-molar widths were estimated.
The duration of treatment in the Alternative group was 20.2 ± 4.4 months, with a median of 19.0 months (range: 12–38 months), while the Classic group showed a mean treatment duration of 28.9 ± 5.2 months, a median of 30.0 months (range: 14–39 months), with a mean difference of 8.7 months. Statistical analysis revealed a significant difference in treatment duration between the two protocols (Mann–Whitney U = 863, p < 0.001), with a large effect size (rrb = −0.78; 95% CI: −0.84 to −0.71), indicating practical relevance.
Regarding secondary outcomes, transverse maxillary measurements, including inter-canine, inter-premolar, and inter-molar widths, showed similar changes before and after treatment in both groups. The available data did not provide sufficient evidence to reject the null hypothesis about the differences between the groups for these parameters, with no clinically meaningful differences.
The application of wide and extra-wide archwires represents an effective orthodontic approach associated with reduced treatment duration, while preserving satisfactory occlusal stability and ensuring a favorable level of patient comfort.
Thrombosis is a frequently underdiagnosed condition associated with high mortality in neglected cases. Many factors, including geoheliophysical and biochemical ones, are responsible for thrombosis modulation. Routine investigations may sometimes be inconsistent and, thus, unreliable in a clinical setting.
Data were collected from patients treated in the Department of Vascular Surgery at the ‘Timofei Moșneaga’ Republican Clinical Hospital, Chișinău, Republic of Moldova. A total of 1,865 patients were initially included in the study. After applying rigorous inclusion and exclusion criteria, 263 eligible patients were identified, and their complete blood counts and biochemical reports were retrospectively analyzed.
The analysis revealed increased mean values for absolute polymorphonuclear neutrophils, absolute monocytes, erythrocyte sedimentation rate (ESR), and glucose. The median values of these indicators, except for absolute polymorphonuclear neutrophils and ESR in female patients, were also elevated above normal ranges. Significant Pearson and Spearman correlations were identified among the analyzed indicators, and a binary logistic regression model was constructed using the most statistically significant variables.
Usual mathematical models that outline thrombosis consider deep vein thrombosis without a sustainable arterial assessment. The sensitivity of our model is lower than that of the D-dimer, while the specificity is almost the same. Platelets and clotting tests are well-known, reliable indicators; however, novel contemporary augmentations to these may, in turn, increase the predictive capability of our model if applied. This study has its limitations due to the lack of variance in the variance inflation factors (VIF), preventing the evaluation of multicollinearity among the included biomarkers.
The mathematical model developed in this study shows potential for further clinical application; however, additional research, validation, and the incorporation of non-biochemical indicators may be necessary to enhance its predictive accuracy.
Currently, extensive research has shown that almost all published prediction models are poorly studied and have significant limitations, leading to their predictive performance often being overestimated. Additionally, there is still no universally accepted scoring system, primarily due to the need for adaptation to heterogeneous patient samples (including patient numbers, clinical profiles, and risk factors) and/or ongoing differences in the organization of healthcare systems across various countries.
This is a narrative literature review. A bibliographic search was conducted in the PubMed, Hinari, SpringerLink, National Center for Biotechnology Information, and Medline databases. Articles published between 2000 and 2024 were selected based on keyword combinations such as "artificial intelligence", "prediction model", "algorithm", "machine learning", and "COVID-19". Information on machine learning predictive models was selected and processed to identify characteristics that can be used to predict diagnosis, severity, length of hospital stay, ICU admission, treatment, vaccination, and mortality in COVID-19 patients. After processing the data according to the search criteria, 125 full-text articles were identified. The final bibliography includes 52 relevant sources, which were considered representative of the literature on this synthesis article topic.
Artificial intelligence techniques are increasingly being used to predict outcomes in COVID-19 patients, particularly in estimating mortality among individuals infected with SARS-CoV-2, which can rapidly and effectively support clinical decision-making. According to the analysis of multiple studies, strong predictors of mortality in COVID-19 patients include advanced age, male gender, comorbidities, reduced levels of calcium, albumin, red blood cells, and oxygen saturation, as well as lymphopenia, elevated blood urea nitrogen, creatinine, lactate dehydrogenase, D-dimers, neutrophils, interleukin-6, procalcitonin, bilirubin, ferritin, aspartate aminotransferase, and troponin.
Artificial intelligence techniques provide potential advantages over conventional assessment methods. The information obtained from machine learning and deep learning algorithms, including easily accessible and interpretable data, can assist healthcare workers in making accurate decisions for the appropriate and timely care of COVID- 19 patients. This can improve patient outcomes, reduce the burden on healthcare systems, and ultimately decrease mortality rates.
Type 2 DM accounts for over 95% of all diabetes cases worldwide and represents an important and independent cardio-metabolic risk factor. This is the first national study (Epidemiological study of widespread endocrine pathologies (diabetes, obesity) in the Republic of Moldova and its management strategy) that analyzes the prevalence of type 2 diabetes mellitus (DM), prediabetes, obesity, and dyslipidemia in the adult population of Moldova.
This is an epidemiological cross-sectional study with cluster random sampling. A face-to-face interview was conducted with the participants using a pre-tested semi-structured questionnaire. All biochemical tests were performed in a certified laboratory. Statistical analysis used Spearman’s correlation test, chi-square, and Wilcoxon tests. The Research Ethics Committee of Nicolae Testemițanu State University of Medicine and Pharmacy (Minutes 3 from December 28, 2020) approved this study.
728 individuals were enrolled, of which 2.5% had unknown DM. Advanced age, obesity, and dyslipidemia were influencing factors for diabetes. 21.4% of participants had prediabetes, with a higher prevalence in men than in women (28.3% versus 18.9%). Only 23.2% of men and 30.4% of women had a BMI within the normal range. Abdominal circumference (AC) values greater than 102 cm and 88 cm in men and women, respectively, were determined in 39.4% of men and 53.8% of women.
Our study showed an increased prevalence of carbohydrate metabolism disorders, including prediabetes, as well as a high prevalence of abdominal obesity. Persons with unknown diabetes mellitus have been identified.