Paired Analysis Research Articles

Treatment Response of Donor Specific Antibodies and Forced Expiratory Volume in Lung Transplant Recipients With Antibody Mediated Rejection

Antibody-mediated rejection (AMR) is an evolving diagnosis in lung transplantation. The presence of anti-human leukocyte antigen (HLA) donor-specific antibodies (DSAs) does not always correlate with clinical picture, leading to variation in treatment. This study sought to examine anti-HLA DSA response and lung allograft stabilization following AMR treatment. A single-center, retrospective case series was conducted in adult lung transplant recipients treated for clinical and subclinical AMR. The primary outcome was anti-HLA DSA reduction (≥ 25% decrease in mean fluorescence intensity [MFI]). The secondary outcome was forced expiratory volume (FEV1) stabilization (≤ 10% decline) at peak FEV1 and at 6-months post-treatment. Fifteen bilateral lung transplant recipients were included. Eight (53%) patients achieved the primary outcome with median MFI reduction of -56.7% (interquartile range [IQR] = -41.3 to -69.5). Statistical significance was found on matched pairs analysis between 3 and 6 months post-treatment for anti-HLA DSA reduction. Of the subjects with available data, 7 of 9 (78%) patients had FEV1 stabilization from diagnosis to peak FEV1, and 5 of 7 (71%) patients had stabilization from diagnosis to 6 months post-treatment. A statistically significant decline was found from peak FEV1 post-treatment to 6 months post-treatment (-0.4 L ± 0.2, P = .05). Univariate analysis did not identify predictors affecting anti-HLA DSA response. Anti-HLA DSA response was achieved in approximately half the cohort. A statistically significant decline in FEV1 was seen from peak FEV1 post-treatment but stabilized in most patients by 6 months. These results highlight the difficulty of DSA management and recovering lung function once lost, however, the finding of FEV1 stabilization after treatment is notable.

DiBiMT: A Gold Evaluation Benchmark for Studying Lexical Ambiguity in Machine Translation

Abstract Despite the remarkable progress made in the field of Machine Translation (MT), current systems still struggle when translating ambiguous words, especially when these express infrequent meanings. In order to investigate and analyze the impact of lexical ambiguity on automatic translations, several tasks and evaluation benchmarks have been proposed over the course of the last few years. However, work in this research direction suffers from critical shortcomings. Indeed, existing evaluation datasets are not entirely manually curated, which significantly compromises their reliability. Furthermore, current literature fails to provide detailed insights into the nature of the errors produced by models translating ambiguous words, lacking a thorough manual analysis across languages. With a view to overcoming these limitations, we propose Disambiguation Biases in MT (DiBiMT), an entirely manually curated evaluation benchmark for investigating disambiguation biases in eight language combinations and assessing the ability of both commercial and non-commercial systems to handle ambiguous words. We also examine and detail the errors produced by models in this scenario by carrying out a manual error analysis in all language pairs. Additionally, we perform an extensive array of experiments aimed at studying the behavior of models when dealing with ambiguous words. Finally, we show the ineffectiveness of standard MT evaluation settings for assessing the disambiguation capabilities of systems and highlight the need for additional efforts in this research direction and ad-hoc testbeds such as DiBiMT. Our benchmark is available at: https://nlp.uniroma1.it/dibimt/.

Plasma ferritin, C-reactive protein, and adenosine deaminase levels in tuberculous lymphadenitis and pleuritis and their role in monitoring treatment response

BackgroundWe aimed to assess the plasma levels of ferritin, C-reactive protein (CRP), and adenosine deaminase (ADA) at baseline and their utility as biomarkers to monitor response to treatment in extrapulmonary tuberculosis (EPTB) patients.MethodsProspective measurements of ferritin, CRP, and ADA were done in unstimulated plasma samples of 92 EPTB (49 TB lymphadenitis and 43 TB pleuritis) patients registered for anti-TB treatment. Blood samples were taken at the start, 2, and 6 months of treatment, plasma levels of ferritin and CRP were measured by the enzyme-linked immunosorbent assay and ADA levels by kinetic chemistry method at each time point. Data was analyzed using SPSS version 22. Non-parametric tests were used for paired analysis and two groups’ comparison. Spearman’s rank test was used for correlation analysis. A Chi-square test was used for categorical variables. A p-value < 0.05 was considered statistically significant.ResultsBefore the start of treatment, plasma levels of ferritin were raised in 13% and 45%, CRP in 21% and 64%, and ADA in 70% and 60% of TB lymphadenitis and pleuritis cases respectively. Levels of all three biomarkers with raised values at baseline decreased significantly with treatment at both 2 and 6 months in all patients. [Ferritin (2 months p = 0.001, 6 months p < 0.001), CRP (2 months p < 0.001, 6 months p < 0.001), ADA (2 months p = 0.039, 6 months p < 0.004)]. Plasma levels of ferritin (median 300 ng/ml range = 145–758 ng/ml) and CRP (median 11.73 mg/L, range = 10.45–17.84 mg/L) were significantly higher in TB pleuritis patients, while the levels of ADA were not significantly different among the two groups. Biosignatures generated by different combinations showed that a combination of all three biomarkers could predict treatment response in 83% and 100% of all patients at 2 and 6 months of treatment respectively.ConclusionA combination of serum ferritin, CRP, and ADA shows a promising role in monitoring response to treatment in TB lymphadenitis and TB pleuritis patients. Similar studies in larger cohorts are needed to establish a definite role of these biomarkers in EPTB patients.

THE IMPACT OF NEUROSCIENCE-BASED MINDFULNESS TRAINING ON MENTAL HEALTH IN FUTURE PSYCHOLOGISTS

Purpose. This study investigates the effectiveness of a neuroscience-based mindfulness training program on emotional states, self-compassion traits and well-being status among psychology students during an 8-week intervention. The work focuses on developing emotional regulation, decreasing stress and depression, and increasing self-compassion and mindfulness — all of which are crucial skills for professional practice. Methods. This pilot study included fifty psychology students (M = 23.5, SD = 2.8) who were assessed using the DASS-21 for stress, anxiety, depression and Self-Compassion Scale for self-kindness, mindfulness and related traits before and after a special training program along with the BBC-SWB questionnaire for psychological, physical and subjective well-being. Statistical analyses such as paired t-tests, correlation analysis and hierarchical regression were used to assess the intervention's effectiveness and explore predictive relationships among variables. Results. The program was associated with significant reductions in anxiety, stress, and depression and increases in self-kindness, mindfulness, and psychological well-being. Given that emotional states and self-compassion traits function as mutually reinforcing factors, correlations provided evidence based on the holistic benefits of the program. Hierarchical regression analysis showed that both pre-intervention anxiety (β = 0.45, p &lt; 0.001) and self-kindness (β = 0.35, p = 0.05) significantly predicted psychological well-being after the intervention. These variables combined explained 52% of the outcome variance (R² = 0.52, p &lt; 0.001). Mindfulness explained an additional 17% variance beyond all other variables combined, affirming its role as a transformational variable within the model. Conclusions. This mindfulness training, based on neuroscience principles and research findings, is designed to decrease psychological suffering and enhance psychological resilience and self-compassion. The results call for incorporating such training within psychology educational programs, thus enhancing personal health and professional competence. Longitudinal studies investigating the long-term effects of mindfulness practices and their use in a broader range of populations are warranted for future research.

Paired analysis of host and pathogen genomes identifies determinants of human tuberculosis

Infectious disease is the result of interactions between host and pathogen and can depend on genetic variations in both. We conduct a genome-to-genome study of paired human and Mycobacterium tuberculosis genomes from a cohort of 1556 tuberculosis patients in Lima, Peru. We identify an association between a human intronic variant (rs3130660, OR = 10.06, 95%CI: 4.87 − 20.77, P = 7.92 × 10−8) in the FLOT1 gene and a subclavaluee of Mtb Lineage 2. In a human macrophage infection model, we observe hosts with the rs3130660-A allele exhibited stronger interferon gene signatures. The interacting strains have altered redox states due to a thioredoxin reductase mutation. We investigate this association in a 2020 cohort of 699 patients recruited during the COVID-19 pandemic. While the prevalence of the interacting strain almost doubled between 2010 and 2020, its infection is not associated with rs3130660 in this recent cohort. These findings suggest a complex interplay among host, pathogen, and environmental factors in tuberculosis dynamics.

Soliton resolution for the generalized complex short pulse equation with the weighted Sobolev initial data

In this work, the Cauchy problem for the generalized complex short pulse equation with initial conditions in the weighted Sobolev space H(R) is studied by using the Riemann-Hilbert method and the ∂‾-steepest descent method. Based on the spectral analysis of the Lax pair, the solution of the Cauchy problem can be expressed as solution of a Riemann-Hilbert problem, which is transformed into a solvable model after a series of deformations. Finally, the long-time asymptotics and soliton resolution of the generalized complex short pulse equation in the soliton region are obtained by resorting to the ∂‾-steepest descent method. The results also indicate that the N-soliton solutions of the generalized complex short pulse equation are asymptotically stable.

The relationship between indoor airborne culturable bacteria with passenger flow in 132 traffic stations during 2019–2020, China

This study aims to provide technical support for the development of a rational administration strategy for indoor environment and passenger flow of stations. The analysis was conducted monitor data of long distance bus stations and train/high speed railway stations from the National project. The monitor and surveyed data included indoor airborne culturable bacteria (IAB), passenger flow, area and height of stations. Data analysis involved the use of paired non-parametric tests, correlation analysis, and mixed linear regression methods. A total of 132 pairs of stations were examined in this study. Additionally, significant variations were observed in the monitoring results for the IAB and passenger flow between 2019 and 2020 (p < 0.05). Specifically, the median values of these two factors in 2020 decreased by 50.52% and 48.33%, respectively, compared to 2019. (p < 0.05). A positive relationship between the daily average passenger flow and the IAB in long distance bus stations and train stations, particularly pronounced with medium sized waiting rooms, which located in subtropical cities with a general level of economic development. The mixed effect regression analysis revealed a significant association between passenger flow (OR = 1.564, 1.288–1.898), per capita volume (OR = 0.856, 0.733–0.998), and per capita area (OR = 0.806, 0.678–0.956) with the IAB (p < 0.05) just in long distance bus stations. It is crucial to regulate passenger flow and per capita area in the waiting room of long distance bus stations to mitigate the spread of airborne culturable bacteria during infectious disease epidemics.

Applying the Improved Set Pair Analysis Method to Flood Season Staging in Tropical Island Rivers: A Case Study of the Hainan Island Rivers in China

The seasonality of floods is a key factor affecting riparian agriculture. Flood season staging is the main means of identifying the seasonality of floods. In the process of staging the flood season, set pair analysis is a widely used method. However, the set pair analysis method (SPAM) cannot take into account the differences in and volatility of the staging indicators, and at the same time, the SPAM cannot provide corresponding staging schemes according to different scenarios. To address these problems, the improved set pair analysis method (ISPAM) is proposed. Kernel density estimation (KDE) is used to calculate the interval of the staging indicators to express their volatility. Based on the interval theory, the deviation method is improved, and the weights of the staging indicators are calculated to reflect the differences in different staging indicators. The theoretical correlation coefficient can be calculated by combining the weights and interval indicators and fitting the empirical connection coefficient corresponding to each time period. Finally, the ISPAM is established under different confidence levels to derive staging schemes under different scenarios. Based on the daily average precipitation flow data from 1961 to 2022 in the Nandujiang middle basin and surrounding areas in tropical island regions, the staging effect of the ISPAM was verified and compared using the SPAM, Fisher optimal segmentation method, and improved set pair analysis method without considering differences in the indicator weights (ISPAM-WCDIIW), and the improved set pair analysis method without considering indicator fluctuations (ISPAM-WCIF). According to the evaluation results from the silhouette coefficient method, it can be concluded that compared with the SPAM and ISPAM-WCIF, the ISPAM provided the optimal staging scheme for 100% of the years in the test set (2011–2022). Compared with the Fisher optimal segmentation method, the optimal staging scheme for more than 83% of the years (2011, 2013–2015, and 2017–2022) in the test set was provided by the ISPAM. Although the ISPAM-WCDIIW, like the ISPAM, can provide optimal staging schemes, the ISPAM-WCDIIW could not provide an exact staging scheme for more than 55% of the scenarios (the ISPAM-WCDIIW could not provide an exact staging scheme in scenarios (0.7, 0.6), (0.8, 0.6), (0.8, 0.9), (0.95, 0.6), and (0.95, 0.8)). The results show that the ISPAM model is more reasonable and credible compared with the SPAM, Fisher optimal segmentation method, ISPAM-WCDIIW, and ISPAM-WCIF. The purpose of this study is to provide a reference for flood season staging research during flood seasons.

Identification of co-localised transcription factors based on paired motifs analysis.

The interaction of transcription factors (TFs) with DNA precisely regulates gene transcription. In mammalian cells, thousands of TFs often interact with DNA cis-regulatory elements in a combinatorial manner rather than act alone. The identification of cooperativity between TFs can help to explore the mechanism of transcriptional regulation. However, little is known about the cooperative patterns of TFs in the genome. To identify which TFs prefer co-localisation, the authors conducted a paired motif analysis in the accessible regions of the human genome based on the Poisson background model. Especially, the authors distinguish the cooperative binding TFs and the competitive binding TFs according to the distance between TF motifs. In the K562 cell line, the authors find that TFs from a same family are always competing the same binding sites, such as FOS_JUN family, whereas KLF family TFs show significant cooperative binding in the adjacency region. Furthermore, the comparative analysis across 16 human cell lines indicates that most TF combination patterns are conserved, but there are still some cell-line-specific patterns. Finally, in human prostate cancer cells (PC-3) and human prostate normal cells (RWPE-2), the authors investigate the specific TF combination patterns in the disease cell and normal cell. The results show that the cooperative binding TF pairs shared by PC-3 and RWPE-2 account for over 90%. Simultaneously, the authors also identify 26 specific TF combination pairs in PC-3 cancer cells.

Steganography Algorithms in Computed Tomography (CT) Scan Images

Steganography is a technique which allows to hide information or data inside a medium without giving an idea to people that the hidden data even existed. In the medical field, there are cases where patient’s medical images breached along with their sensitive and personal data because of poor security. This problem leads to a wanting of a good solution to have the medical image with secured patient data. Here is where the image steganography takes place to hide the patient data inside the medical images. This study is conducted to determine the effective image steganography algorithm to be used in the medical field to embed the patient information inside their medical images specifically in Computed Tomography (CT) scan images. The image steganography algorithms that will be used in this study are Least Significant Bit (LSB), Exploiting Modification Direction (EMD) and Discrete Wavelet Transform (DWT). The flow of the image steganography technique will be studied and analyzed and after that, the algorithms will be implemented with different file formats which are Portable Network Graphics (PNG), Bitmap (BMP) and Joint Photographic Experts Group (JPG) and different image sizes which are 1024x1024 and 2048x2048 pixels. After that, the results are evaluated with performance metrics which are Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Structural Similarity Index Measure (SSIM) and Pairs Analysis. The results are then compared to determine the best image steganography algorithm along with the image file format which gives the highest robustness, visual quality, security and imperceptibility. The research result displays the EMD algorithm as the best paired with BMP file format in image steganography where it resulted in producing highest average of SSIM and PSNR which is 1.0 and 67.421 respectively and lowest MSE average of 0.012 with no traces in pairs analysis.

Quality Evaluation of Effective Abrasive Grains Micro-Edge Honing Based on Trapezoidal Fuzzy Analytic Hierarchy Process and Set Pair Analysis

Studying the factors affecting machining accuracy, surface quality, and machining efficiency in the powerful honing machining process system, analyzing the basic law between various errors and machining quality, exploring the method of evaluating the quality of honing, and improving the machining quality and transmission performance of hardened gears has important engineering application value. Firstly, this paper establishes an effective abrasive grains micro-edge honing quality evaluation model, proposes a method based on the Trapezoidal Fuzzy Analytic Hierarchy Process (Tra-FAHP) and Set Pair Analysis (SPA) to comprehensively evaluate the quality of the honing process, and obtains the influence weights of each factor on the quality of honing. Secondly, the paper analyzes the influence rules of three types of abrasive grain sizes on helix error, tooth pitch error, tooth profile error, surface roughness, and honing efficiency. Finally, the correctness of the established comprehensive evaluation model of honing quality was verified with the threshold method and weights. The research results show that the model can correctly evaluate the quality of hardened gear honing and can be applied to studying the influence of abrasive grain micro-edge honing on machining characteristics.

Pulsatile Vascular Afterload Measurement Improves Prediction of Right-Sided Heart Dysfunction in Undifferentiated and Left-Sided Pulmonary Hypertension.

The association between raised pulmonary artery pressures, pulmonary vascular resistance (PVR), and right-sided heart dysfunction is well recognized. The added value of pulsatile indices, specifically pulmonary arterial compliance (PAC) and characteristic impedance, remains unclear. This study aimed to identify the optimal vascular afterload model for predicting right-sided heart dysfunction in newly diagnosed pulmonary hypertension (PH). This was a prospective cohort study of 86 patients with suspected PH (60±16 years; 66 women) who underwent right-sided heart catheterization and cardiac magnetic resonance imaging. Right ventricular end-diastolic volume index (RVEDVi) and right atrial (RA) volume index were measured using cardiac magnetic resonance imaging. Vascular resistance, PAC, and characteristic impedance were measured using pulmonary artery pressure from right-sided heart catheterization and pulmonary artery flow from cardiac magnetic resonance imaging. Optimal model fit was determined by the lowest Akaike Information Criterion value. A total of 58% of patients had a right ventricular ejection fraction <50%, 63% had an increased RVEDVi, and 56% had an increased RA volume index. Overall, PVR was the best predictor of right ventricular ejection fraction <50% (P<0.01), increased RVEDVi (P<0.01), and RA volume index (P=0.02). However, for patients with a PVR <240 dynes.s.cm-5, PACclinical outperformed PVR in predicting right ventricular ejection fraction <50% (P=0.02) and increased RVEDVi (P=0.05). A 3-component model (PVR, PACclinical, and characteristic impedance) best predicted RA dilatation (P=0.05). In paired analysis, alternative methods of PAC quantification better predicted increased RVEDVi (PACdecay P=0.02) and RA volume index (PACarea P=0.02). PACclinical <2.3 mL/mm Hg optimally predicted right-sided heart dysfunction (area under the curve, 0.768) and increased the AUC for PVR in undifferentiated/left-sided PH (0.89 versus 0.72). Pulsatile vascular afterload measurement improves right-sided heart dysfunction prediction in undifferentiated/left-sided PH. More accurate PAC measurement methods could further enhance prognostic accuracy, while a PVR ≥240 dynes.s.cm-5 remains a valuable threshold for precapillary PH.

Pneumonitis after normofractionated radioimmunotherapy: a method for dosimetric evaluation

BackgroundPost-Therapy-Pneumonitis (PTP) is a critical side effect of both, thoracic radio(chemo)therapy (R(C)T) and immune checkpoint inhibition (ICI). However, disease characteristics and patient-specific risk factors of PTP after combined R(C)T + ICI are less understood. Given that RT-triggered PTP is strongly dependent on the volume and dose of RT [1], driven by inflammatory mechanisms, we hypothesize that combination therapy of R(C)T with ICI influences the dose-volume-effect correlation for PTP. This study focuses on the development of a method for evaluation of alterations of dosimetric parameters for PTP after R(C)T with and without ICI.Methods and materialsPTP volumes were delineated on the follow-up diagnostic Computed Tomography (CT) and deformably matched to the planning CT for patients with PTP after thoracic R(C)T + ICI or R(C)T. Dose data was converted to 2-Gy equivalent doses (EQD2) and dosimetrically analyzed. Dosimetric and volumetric parameters of the segmented PTP volumes were analyzed. The method was exemplarily tested on an internal patient cohort including 90 patients having received thoracic R(C)T + ICI (39) and R(C)T (51). Thirtytwo patients with PTP were identified for further analysis. Additional data on previous chemotherapy, RT, smoking status and pulmonary co-morbidity were conducted. A matched pair analysis with regard to planning target volumes (PTV) was conducted for curative intended (definitive) and palliative patient cohorts individually.ResultsThe presented method was able to quantify and compare the dosimetric parameters of PTP for the different therapies. For our study group, no significant differences between R(C)T + ICI and R(C)T only was observed. However, the dosimetric analysis revealed large volumetric fractions (55%) of the PTP volumes to be located outside of high dose (EQD2 < 40 Gy) regions for R(C)T + ICI. There was a non-significant trend towards increased area under the curve of the dose volume histogram (AUC) values for R(C)T + ICI compared to R(C)T only (3743.6 Gy∙% vs. 2848.8 Gy∙%; p-value = 0.171). In contrast to the data for the palliative intended treatment group, for definitive R(C)T + ICI, data tended towards increased volumes with higher doses.ConclusionsThe proposed method was capable to quantify dosimetric differences in the dose-volume-effect relationship of PTP for patients with R(C)T + ICI and patients with R(C)T only. In this exploratory analysis, no significant dosimetric differences within PTP volumes for the different groups could be observed. However, our observations suggest, that for safe application of thoracic R(C)T + ICI, further careful investigation of dosimetric prescription and analysis concepts with larger and conformer study groups is recommendable.

Associations Between Physical Activity and Symptoms of Mental Health Disorders in Adolescence: Evidence From the Longitudinal Swedish Twin Register

PurposeThe transition phase between childhood and adolescence is critical, given the observation of increased mental health issues during this period, coupled with the absence of efficient intervention tools. This study aims to examine a possible effect of physical activity on mental health symptoms in adolescents during this transitional age. MethodsWe used data from a large-scale population-based Swedish twin sample of 14,083 individuals to investigate cross-sectional and longitudinal associations between physical activity and mental health problems during childhood and adolescence. We examine different aspects of mental health symptoms, including internalizing and externalizing symptoms. We applied generalized estimating equations to investigate the associations and explored potential familial confounding using within-twin pair analysis. ResultsWe found high physical activity intensity and frequency to be associated with better mental health throughout childhood and adolescence, cross-sectionally and longitudinally. Compared to those who reported low, those reporting high physical activity intensity and frequency in both childhood and/or adolescence had significantly lower symptoms of mental health at age 15. This association was statistically significant for both internalizing and externalizing symptoms. The within-twin pair analysis showed that familial confounding factors can largely explain the association with the externalizing scale, but not the internalizing scale. DiscussionPhysical activity in childhood and adolescence may play an essential role in reducing the risk of developing mental health problems, especially internalizing symptoms. Implementing acceptable, low-risk, and cost-effective interventions to promote physical activity could effectively safeguard young individuals from these symptoms.

Changes in the tumor microenvironment in recurrent head and neck squamous cell carcinoma and its implication on efficacy of immune checkpoint inhibitors

Little is known about changes in the abundance of tumor-infiltrating lymphocytes (TILs) and immune phenotype (IP) in recurrent and/or metastatic (R/M) head and neck squamous cell carcinoma (HNSCC). We aimed to compare the TILs and IP between initial and recurrent HNSCCs using paired analysis. Thirty-seven patients who experienced recurrence after surgical resection and received treatment with immune checkpoint inhibitors (ICIs) between June 2014 and June 2023 were included. Changes in intratumoral TIL (iTILs), stromal TIL (sTILs), and IPs were subjected to paired analysis between the initial and recurrent tumors. We investigated their relationship with the outcomes of ICIs. The density of iTIL and sTIL in the recurrent tumors was significantly lower compared to initial tumors. IP was significantly different; the proportion of desert IP was higher in recurrent tumors (83.8% vs. 35.1%, P < 0.001). Increased sTIL was a favorable indicator for overall response to ICIs and progression-free survival. Our findings suggest TILs decrease during recurrence compared with the initial tumor, resulting in a transition toward desert IP. Therefore, careful evaluation of TIL density in both initial and recurrent tumors is recommended when using ICIs in patients with R/M HNSCC.

Sc2GWAS: a comprehensive platform linking single cell and GWAS traits of human.

Identifying cell populations associated with risk variants is essential for uncovering cell-specific mechanisms that drive disease development and progression. Integrating genome-wide association studies (GWAS) with single-cell RNA sequencing (scRNA-seq) has become an effective strategy for detecting trait-cell relationships. The accumulation of trait-related single cell data has led to an urgent need for its comprehensively processing. To address this, we developed sc2GWAS (https://bio.liclab.net/sc2GWAS/), which aims to document large-scale GWAS trait-cell regulatory pairs at single-cell resolution and provide comprehensive annotations and enrichment analyses for these related pairs. The current version of sc2GWAS curates a total of 15078310 candidate trait-cell pairs from>6300000 individual cells, offering a valuable resource for exploring complex regulatory relationships between traits and cells. We applied strict quality control measures on both scRNA-seq data and GWAS data, ensuring the reliability and accuracy of the datasets for the identification of trait-relevant cells and genes. In addition, sc2GWAS provides ranked lists of trait-relevant genes and extensive (epi) genetic annotations, making it a valuable resource for downstream analyses. We demonstrate the utility of the platform by investigating Alzheimer's disease, where we identified significant associations between the disease and microglial cells, with the APOE gene emerging as particularly significant. This platform facilitates detailed research into complex trait-celland trait-gene interactions, we anticipate that sc2GWAS will become a comprehensive and valuable platform for exploring GWAS trait-cell regulatory mechanisms.

Spatial multiomic landscape of the human placenta at molecular resolution.

Successful pregnancy relies directly on the placenta's complex, dynamic, gene-regulatory networks. Disruption of this vast collection of intercellular and intracellular programs leads to pregnancy complications and developmental defects. In the present study, we generated a comprehensive, spatially resolved, multimodal cell census elucidating the molecular architecture of the first trimester human placenta. We utilized paired single-nucleus (sn)ATAC (assay for transposase accessible chromatin) sequencing and RNA sequencing (RNA-seq), spatial snATAC-seq and RNA-seq, and in situ sequencing and hybridization mapping of transcriptomes at molecular resolution to spatially reconstruct the joint epigenomic and transcriptomic regulatory landscape. Paired analyses unraveled intricate tumor-like gene expression and transcription factor motif programs potentially sustaining the placenta in a hostile uterine environment; further investigation of gene-linked cis-regulatory elements revealed heightened regulatory complexity that may govern trophoblast differentiation and placental disease risk. Complementary spatial mapping techniques decoded these programs within the placental villous core and extravillous trophoblast cell column architecture while simultaneously revealing niche-establishing transcriptional elements and cell-cell communication. Finally, we computationally imputed genome-wide, multiomic single-cell profiles and spatially characterized the placental chromatin accessibility landscape. This spatially resolved, single-cell multiomic framework of the first trimester human placenta serves as a blueprint for future studies on early placental development and pregnancy.

Detection of diagnostic somatic copy number alterations from cerebrospinal fluid cell-free DNA in brain tumor patients

The gold standard for precise diagnostic classification of brain tumors requires tissue sampling, which carries relevant procedural risks. Brain biopsies often have limited sensitivity and fail to address tumor heterogeneity, because small tissue parts are being examined. This study aims to explore the detection and quantification of diagnostically relevant somatic copy number aberrations (SCNAs) in cell-free DNA (cfDNA) extracted from cerebrospinal fluid (CSF) in a real-world cohort of patients with defined brain tumor subtypes. A total of 33 CSF samples were collected from 30 patients for cfDNA extraction. Shallow whole-genome sequencing was conducted on CSF samples containing > 3ng of cfDNA and corresponding tissue DNA from nine patients. The sequencing cohort encompassed 26 samples of 23 patients, comprising 12 with confirmed CNS cancer as compared to 11 patients with either ambiguous CNS lesions (n = 5) or non-cancer CNS lesions (n = 6). After mapping and quality filtering SCNAs were called by depth-of-coverage analyses with a binning of 5.5 Mbp. SCNAs were exclusively identified in CSF cfDNA from brain tumor patients (10/12, 83%). In tumor patients, SCNAs were detectable in cfDNA from all patients with, but also in five of seven patients without tumor cells detected by CSF cytopathology. A substantial number of shared SCNAs were traceable between tissue and CSF in matched pair analyses. Additionally, some SCNAs unique to either CSF or tissue indicating spatial heterogeneity or tumor evolution. Also, diagnostically relevant genomic alterations as well as essential and desirable SCNAs as implemented in the current WHO classification of CNS tumors for certain primary brain tumor subtypes were traceable. In summary, this minimally invasive cfDNA-based LB approach employing shallow whole genome sequencing demonstrates potential for providing a molecularly informed diagnosis of CNS cancers, mapping tumor heterogeneity, tracking tumor evolution, and surveilling tumor patients. Further prospective trials are warranted.

Explainable Machine Learning Models Using Robust Cancer Biomarkers Identification from Paired Differential Gene Expression.

In oncology, there is a critical need for robust biomarkers that can be easily translated into the clinic. We introduce a novel approach using paired differential gene expression analysis for biological feature selection in machine learning models, enhancing robustness and interpretability while accounting for patient variability. This method compares primary tumor tissue with the same patient's healthy tissue, improving gene selection by eliminating individual-specific artifacts. A focus on carcinoma was selected due to its prevalence and the availability of the data; we aim to identify biomarkers involved in general carcinoma progression, including less-researched types. Our findings identified 27 pivotal genes that can distinguish between healthy and carcinoma tissue, even in unseen carcinoma types. Additionally, the panel could precisely identify the tissue-of-origin in the eight carcinoma types used in the discovery phase. Notably, in a proof of concept, the model accurately identified the primary tissue origin in metastatic samples despite limited sample availability. Functional annotation reveals these genes' involvement in cancer hallmarks, detecting subtle variations across carcinoma types. We propose paired differential gene expression analysis as a reference method for the discovering of robust biomarkers.

Effect of Omega-3 Fatty Acid Supplementation on Periodontal Inflammation and Pocket Depth Reduction

ABSTRACT Background: Chronic inflammation of the tooth’s supporting tissues is known as periodontal disease. It causes pockets to develop, attachments to break, and eventually the loss of teeth. Because of their well-known anti-inflammatory qualities, omega-3 fatty acids have been recommended as an additional therapy for periodontal inflammation. Materials and Methods: A randomized controlled study of 60 individuals suffering from persistent periodontitis was carried out. Two groups of participants were randomly assigned: The test group (n = 30) got conventional periodontal treatment with omega-3 fatty acid supplementation (1000 mg/day), while the control group (n = 30) received normal periodontal therapy alone. Measurements of periodontal parameters were made at baseline, 3 months, and 6 months after therapy, including bleeding on probing (BOP) and pocket depth. Paired t-tests and analysis of variance were used in the statistical analysis to determine the significance of the group differences. Results: Participants in the test group showed a greater reduction in probing pocket depth (PPD) and BOP at both 3 and 6 months. Specifically, the mean PPD decreased from 5.4 ± 0.7 mm to 3.0 ± 0.4 mm, and BOP reduced from 71.2 ± 14.5% to 28.3 ± 9.2% in the test group, with both outcomes showing statistically significant differences compared to the control group (P &lt; 0.01). Conclusion: In individuals with chronic periodontitis, omega-3 fatty acid supplementation dramatically improves the decrease of periodontal pocket depth and inflammation when administered as an adjuvant to regular periodontal treatment.