bluuebunny/crossref_metadata_2025_split huggingface dataset:
Development of a machine-learning model for therapeutic efficacy prediction of preoperative treatment for esophageal cancer using single nucleotide variants of autophagy-related genes
Authors: Miyawaki Yutaka , Hirasaki Masataka , Kamakura Yasuo , Kawasaki Tomonori , Baba Yasutaka , Sato Tetsuya , Yamasaki Satoshi , Fukushima Hisayo , Uranishi Kousuke , Makino Yoshinori , Sato Hiroshi , Hamaguchi Tetsuya 
Published: September 2024

AbstractNeoadjuvant chemotherapy with cisplatin + 5-fluorouracil followed by radical surgery is the standard treatment for stage II and III esophageal cancers. Although, a more potent regimen comprising cisplatin + 5-fluorouracil with docetaxel, has shown superiority in overall survival compared to the cisplatin + 5-fluorouracil regimen, it involves worsening of Grade 3 or higher adverse events due to docetaxel. Based on these reports, this study aimed to construct a prognostic system for cisplatin + 5-fluorouracil regimens, particularly for locally advanced cancers, to guide selection of neoadjuvant chemotherapy. Biopsy specimens from 82 patients who underwent a cisplatin + 5-fluorouracil regimen plus radical surgery at Saitama Medical University International Medical Center between May 2012 and June 2020 were analyzed. Variants in 56 autophagy- and esophageal cancer-related genes were identified using targeted enrichment sequencing. Overall, 13 single nucleotide variants, including eight non-synonymous group single nucleotide variants predicting recurrence were identified using Fisher’s exact test with recurrence as a two-group event, which showed a significant difference (p < 0.05). Additionally, machine learning was used to predict recurrence using 21 features, including eight patient backgrounds. The results showed that the Naive Bayes was highly reliable with an accuracy of 0.88 and Area Under the Curve of 0.9. Thus, we constructed a machine learning model to predict recurrence in patients with esophageal cancer treated with a cisplatin + 5-fluorouracil regimen. We believe that our results will provide useful guidance for the selection of neoadjuvant adjuvant chemotherapy, including the avoidance of docetaxel.
AlteredArabidopsis thalianasugar metabolism affects exudation, immune responses, and plant-microbe interactions
Authors: Siffert Alexandra , Sasse Joëlle 
Published: September 2024

AbstractSugars are critical for plant growth, development, and environmental interactions. They have multiple roles as nutrients for plants, associated beneficial and pathogenic microbes, and as signaling compounds for immunity. We characterize the interconnectedness of these functions by analyzing sugar metabolism and transporter mutant lines. We find that in these lines, root-derived compounds, exudates, are significantly altered in comparison with wild-type not only for carbohydrates, but also for lipids, organic acids, and defense compounds. Quantification of sugar exudation reveals more carbon release during the day than at night, altered sugar exudation in mutant lines, and an opposite exudation pattern with elevated exudation at night forpgm1, a line deficient in starch synthesis. Sugar levels in exudates and tissues did not correlate, suggesting a controlled mode of exudation for sugars. Altered sugar levels have functional consequences: mutant lines exhibit increased resistance against the pathogenPseudomonas syringaeand harbor altered numbers of microbes on roots. Day- and nighttime exudates of mutant lines impact the growth of single microbes such as an inability to grow forBacillus subtilis. Exogenous sugar alters the production of reactive oxygen species in a plant development-dependent manner with opposite effects at 9 days and 14 days. An RNAseq experiment reveals candidate genes potentially involved in this regulation. Our data highlight that sugar metabolism is intricately linked with other metabolite pathways. Alteration of single genes in central carbon metabolism profoundly alters plant immune responses and plant-microbe interactions.
CUP-SHAPED COTYLEDON2 activatesMIR319Ctranscription and promotes cell proliferation in Arabidopsis leaf primordia
Authors: Shankar Naveen , Gupta Abhishek , Roy Somsree , Mane Vishwadeep , Hamant Olivier , Nath Utpal 
Published: September 2024

AbstractThe microRNA miR319 regulates leaf shape and size in diverse plant species by reducing the level of the target transcripts that encode JAW-TCP proteins, the transcription factors (TF) which commit the proliferating pavement cells to differentiation. Expression ofMIR319C, one of the three miR319-producing genes in Arabidopsis, is expressed throughout the incipient leaf primordia, and the expression domain gets restricted to the base at later stages, partly due to the direct action of JAW-TCPs. However, the factors that activate and maintainMIR319Cexpression in leaf primordia are yet unknown. Here, we report the CUP-SHAPED COTYLEDON2 (CUC2) transcription factor as a direct activator ofMIR319Ctranscription. In a yeast one-hybrid (Y1H) screen, we identified several NAC domain TFs as potential regulators ofMIR319C. Subsequent ex vivo binding and transactivation assays suggested that CUC2 may bind to distal promoter region ofMIR319C. Mutants with compromisedCUC2andMIR319Cactivities exhibited smaller leaf areas and reduced pavement cell numbers due to early cell proliferation-to-differentiation transition. Morphometric analysis of higher orderCUC2andMIR319loss-of-function mutants highlighted the crucial role of theCUC2-MIR319module in promoting leaf cell proliferation and indicated functional redundancy among the threeMIR319genes in regulating JAW-TCP levels during leaf growth. Additionally, the phenotypes of mutants with alteredCUC2andMIR319/JAW-TCPactivities demonstrated thatCUC2enhances leaf size through theMIR319C-JAW-TCPpathway. Overall, our findings uncovered a novel role for CUC2 in promoting cell proliferation by activatingMIR319Ctranscription in the leaf primordia.
GENETIC DIVERSITY OFT. B. RHODESIENSESERUM RESISTANCE ASSOCIATED GENE IN MALAWIAN ISOLATES
Authors: Nambala Peter , Claucus Calorine , Noyes Harry , MacLeod Annette , Namulondo Joyce , Nyangiri Oscar , Musaya Janelisa , Matovu Enock , Alibu Pius Vincent , Nerima Barbara , Chammudzi Priscilla , Mulindwa Julius 
Published: September 2024

AbstractBackgroundHuman African Trypanosomiasis (HAT) is a health burden in most remote areas of Sub-Saharan Africa. Only 2 species of the Trypanosome parasites, namely,T. b. rhodesienseandT. b. gambiensecan establish infection in humans whereas other trypanosome parasites are lysed by human serum APOL-1 protein. The mechanism ofT. b. gambienseresistance to APOL-1 activity is complex and involves several parasite factors. On the other hand,T. b. rhodesienseevades the lytic activity of APOL-1 by intracellular expression of aSerum Resistance Associated(SRA) gene that binds to APOL-1 when uptaken by the parasite thereby disabling APOL-1 from causing cellular membrane rupture. APOL-1 has 2 variants, namely, APOL-1 G1 and APOL-1 G2 with the later having mutations on the SRA binding sites which restores APOL-1 lytic activity in parasite lysis assays. This phenomenon remains elusive in clinical setting as limited data is available. In the present study we investigated the genetic diversity ofT. b. rhodesienseSRA gene and APOL-1 genotypes in Malawian r-HAT clinical phenotypes.MethodsT. b. rhodesienseSRA gene from Malawi endemic HAT samples (n= 77) as well as from Zambia and Uganda (n= 13) was amplified by PCR and PCR products were commercially sequenced. APOL-1 variants were identified by restriction fragment length polymorphism (RFLP) after a PCR amplification (n= 61).Results and conclusionSequencing data revealed a heterozygosity of the SRA gene within MalawiT. b. rhodesienseisolates. Malawian SRA gene was genetically different from some isolates in Uganda and Zambia. Contrary to the current understanding that APOL-1 G2 variants are immune toT. b. rhodesienseinfection, severe cases of r-HAT in G2 individuals were identified. This study has brought new insight in understanding the determinants of r-HAT severity.
Predicting 30-Day Hospital Readmission in Medicare Patients: Insights from an LSTM Deep Learning Model
Authors: Li Xintao , Liu Sibei 
Published: September 2024

AbstractBackgroundReadmissions among Medicare beneficiaries are a major problem for the US healthcare system from a perspective of both healthcare operations and patient caregiving outcomes. Our study analyzes Medicare hospital readmissions using LSTM networks with feature engineering to assess feature contributions.DesignThe 21002 senior patient admission data from MIMIC-III clinical database at Beth Israel Deaconess Medical Center.is utilized in the study We selected variables from admission-level data, inpatient medical history and patient demography. The baseline model is a logistic-regression model based on the LACE index, and the LSTM model is designed to capture temporal dynamic in the data from admission-level and patient-level data. We leveraged Area Under the Curve metric, precision and recall to evaluate the model’s performance.ResultsThe LSTM model outperformed the logistic regression baseline, accurately leveraging temporal features to predict readmission. The major features were the Charlson Comorbidity Index, hospital length of stay, the hospital admissions over the past 6 months or the number of medications before discharge, while demographic variables were less impactfulLimitationsThe use of a single-center database from the MIMIC-III database limits the generalizability of the findings. Additionally, the exclusion for specific chronic conditions and external factors limit the model’s ability to capture the complexities of chronic diseases.ConclusionsThis work suggests that LSTM networks offers a more promising approach to improve Medicare patient readmission prediction. It captures temporal interactions in patient databases, enhancing current prediction models for healthcare providers.ImplicationsAdoption of predictive models into clinical practice may be more effective in identifying Medicare patients to provide early and targeted interventions to improve patient outcomes.HighlightsImproved Prediction:Our LSTM model outperforms the logistic regression model with LACE index in predicting Medicare patient readmissions.Feature Contribution:Feature engineering ranks variables base on the impact, deprioritizing the importance of patient demographic variables, highlighting the importance of patients’ chronic diseases in leading hospitalizations and guiding targeted interventions to prevent senior hospital readmissions for healthcare providers.Effective Use of Data:Our LSTM model incorporates with time-series data from MIMIC-III database to enhance the accuracy of all-cause hospital readmission predictions, especially for the high-risk patients.Actionable Insights:The result demonstrates the utilization of deep learning model in healthcare decision-making to reduce hospital readmissions for seniors.
eYoung men’s experiences of violence and poverty and the relationship to sexually transmissible HIV: a cross sectional study from rural South Africa
Authors: Gibbs Andrew , Chirwa Esnat , Harling Guy , Chimbindi Natsayi , Dreyer Jaco , Herbst Carina , Okesola Nonhlanhla , Behuhuma Osee , Mthiyane Nondumiso , Baisley Kathy , Zuma Thembelihle , Smit Theresa , McGrath Nuala , Sherr Lorraine , Seeley Janet , Shahmanesh Maryam 
Published: September 2024

AbstractBackgroundYoung (ages 18-35 years) men are inadequately engaged in HIV prevention and treatment globally, including in South Africa, increasing the likelihood of them having sexually transmissible HIV (i.e. living with HIV but with high viral loads). We sought to understand how men’s experiences of poverty and violence, impacted on transmissible HIV, directly or indirectly via mental health and substance misuse.SettingRural communities in northern KwaZulu-Natal, South Africa.MethodsCross-sectional population-based random selection (September 2018-June 2019), assessing transmissible HIV (living with HIV and viral load ≥400 copies/mL) via dried blood spots, and socio-demographic data. Structural equation models (SEM), assessed direct and indirect pathways from food insecurity and violence experience to transmissible-HIV, with mediators common mental disorders, alcohol use, gender inequitable attitudes and perceptions of life chances.Results2,086 young (ages 18-36 years) men and 8.6%(n=178) men had transmissible HIV. In SEM no direct pathways between food insecurity, or violence experience, and transmissible HIV. Poor mental health and alcohol use mediated the relationship between violence experience and food insecurity and transmissible HIV. Life chances also mediated the food insecurity to transmissible HIV pathway.ConclusionsThere was a high level of transmissible HIV in a representative sample of young men. The analysis highlights the need to address both the proximate ‘drivers’ poor mental health and substance misuse, as well as the social contexts shaping these among young men, namely poverty and violence experience. Building holistic interventions that adequately engage these multiple challenges is critical for improving HIV among young men.
Evaluating the kidney disease progression using a comprehensive patient profiling algorithm: A hybrid clustering approach
Authors: Al-Mamun Mohammad A. , Jeun Ki Jin , Brothers Todd , Asare Ernest , Shawwa Khaled , Ahmed Imtiaz 
Published: September 2024

AbstractBackgroundAmong 35.5 million U.S. adults with chronic kidney disease (CKD), more than 557,000 are on dialysis with incurred cost ranges from $97,373 to $102,206 per patient per year. Acute kidney injury (AKI) can lead to an approximate ninefold increased risk for developing CKD. Significant knowledge gaps exist in understanding AKI to CKD progression. We aimed to develop and test a hybrid clustering algorithm to investigate the clinical phenotypes driving AKI to CKD progression.MethodsThis retrospective observational study utilized data from 90,602 patient electronic health records (EHR) from 2010 to 2022. We classified AKI into three groups: Hospital Acquired AKI (HA-AKI), Community Acquired AKI (CA-AKI), and No-AKI. We developed a custom phenotypic disease and procedure network and a complementary variable clustering to examine risk factors among three groups. The algorithm identified top three matched clusters.ResultsAmong 58,606 CKD patients, AKI group had a higher prevalence of heart failure (21.1%) and Type 2 Diabetes (45.3%). The No-AKI group had a higher comorbidity burden compared to AKI group, with average comorbidities of 2.84 vs. 2.04; p < 0.05; 74.6% vs. 53.6%. Multiple risk factors were identified in both AKI cohorts including long-term opiate analgesic use, atelectasis, history of ischemic heart disease, and lactic acidosis. The comorbidity network in HA-AKI patients was more complex compared to the No-AKI group with higher number of nodes (64 vs. 55) and edges (645 vs. 520). The HA-AKI cohort had several conditions with higher degree and betweenness centrality including high cholesterol (34, 91.10), chronic pain (33, 103.38), tricuspid insufficiency (38, 113.37), osteoarthritis (34, 56.14), and removal of GI tract components (37, 68.66) compared to the CA-AKI cohort.ConclusionOur proposed custom patient profiling algorithm identifies AKI phenotypes based on comorbidities and medical procedures, offering a promising approach to identify early risk factors for CKD using large EHR data.
Evolving cartilage strain with pain progression and gait: a longitudinal study post-ACL reconstruction at six and twelve months
Authors: Miller Emily Y. , Lowe Timothy , Zhu Hongtian , Lee Woowon , Argote Pablo F. , Dresdner Danielle , Kelly James , Frank Rachel M. , McCarty Eric , Bravman Jonathan , Stokes Daniel , Emery Nancy C. , Neu Corey P. 
Published: September 2024

AbstractBackgroundAnterior cruciate ligament (ACL) injuries are prevalent musculoskeletal conditions often resulting in long-term degenerative outcomes such as osteoarthritis (OA). Despite surgical advances in ACL reconstruction, a significant number of patients develop OA within ten years post-surgery, providing a patient population that may present early markers of cartilage degeneration detectable using noninvasive imaging.PurposeThis study aims to investigate the temporal evolution of cartilage strain and relaxometry post-ACL reconstruction using displacement under applied loading MRI and quantitative MRI. Specifically, we examined the correlations between MRI metrics and pain, as well as knee loading patterns during gait, to identify early candidate markers of cartilage degeneration.Materials and MethodsTwenty-five participants (female/male = 15/10; average age = 25.6 yrs) undergoing ACL reconstruction were enrolled in a prospective longitudinal cohort study between 2022 and 2023. MRI scans were conducted at 6- and 12-months post-surgery, assessing T2, T2*, and T1ρ relaxometry values, and intratissue cartilage strain. Changes in pain were evaluated using standard outcome scores, and gait analysis assessed the knee adduction moment (KAM). Regressions were performed to evaluate relationships between MRI metrics in cartilage contact regions, patient-reported pain, and knee loading metrics.ResultsIncreases in axial and transverse strains in the tibial cartilage were significantly correlated with increased pain, while decreases in shear strain were associated with increased pain. Changes in strain metrics were also significantly related to KAM at12 months.ConclusionsChanges in cartilage strain and relaxometry are related to heightened pain and altered knee loading patterns, indicating potential early markers of osteoarthritis progression. These findings underscore the importance of using advanced MRI for early monitoring in ACL-reconstructed patients to optimize treatment outcomes, while also highlighting KAM as a modifiable intervention through gait retraining that may positively impact the evolution of cartilage health and patient pain.Key ResultsIncreased axial and transverse strains in the tibial cartilage from 6 to 12 months post-ACL reconstruction were significantly correlated with increased pain, suggesting evolving changes in cartilage biomechanical properties over time.Decreases in shear strain in inner femoral and central tibial cartilage regions were linked to increased pain, indicating alterations in joint loading patterns.Decreases in shear strain in the inner femoral cartilage were significantly associated with decreased 12-month knee adduction moment (KAM), a surrogate for medial cartilage knee loading during walking.
Accounting for partner notification in epidemiological birth-death models
Authors: Zhukova Anna , Gascuel Olivier 
Published: September 2024

AbstractPhylodynamics bridges the gap between classical epidemiology and pathogen genome sequence data by estimating epidemiological parameters from time-scaled pathogen phylogenetic trees. The models used in phylodynamics typically assume that the sampling procedure is independent between infected individuals. However, this assumption does not hold for many epidemics, in particular for such sexually transmitted infections as HIV-1, for which partner notification schemes are included in health policies of many countries.We developed an extension of phylodynamic multi-type birth-death (MTBD) models with partner notification (PN), and a simulator to generate trees under MTBD and MTBD-PN models. We proposed a non-parametric test for detecting partner notification in pathogen phylogenetic trees. Its application to simulated data showed that it is both highly specific and sensitive. For the simplest representative of the MTBD-PN family, the BD-PN model, we solved the differential equations and proposed a closed form solution for the likelihood function. We implemented it in a program, which estimates the model parameters and their confidence intervals from phylogenetic trees. It performed accurate estimations on simulated data, and detected partner notification in HIV-1 B epidemics in Zurich and the UK. Importantly, we showed that not accounting for partner notification when it is present leads to bias in parameter estimation with the BD model, while BD-PN parameter estimator performs well both in presence and in absence of partner notification.Our PN test, MTBD-PN tree simulator and BD-PN parameter estimator are freely available atgithub.com/evolbioinfo/treesimulatorandgithub.com/evolbioinfo/bdpn.Author summaryPhylodynamic models can estimate epidemiological parameters such as the number of secondary infectionsRefrom pathogen phylogenetic trees (i.e., genealogies, inferred from pathogen genomic sequences). These models do not account for partner notification (contact tracing), and instead assume that detection of new cases is independent between infected individuals. However, especially for sexually transmitted infections, partner notification plays an important role and is included in health policies of many countries.We developed a phylodynamic model accounting for partner notification and a test for the detection of partner notification in pathogen phylogenetic trees. Our test and epidemiological parameter estimator showed good performance both on simulated and real data. We detected the presence of partner notification in the HIV-1 B epidemics in Zurich and the UK, and corrected the previous parameter estimates made without accounting for partner notification.
Efficacy of Molnupiravir in reducing the risk of severe outcome in patients with SARS-CoV-2 infection: a real-life full-matched case-control study (SAVALO Study)
Authors: Gentile Ivan , Scotto Riccardo , Scirocco Maria Michela , Di Brizzi Francesco , Cuccurullo Federica , Silvitelli Maria , Ametrano Luigi , Alfè Francesco Antimo , Pietroluongo Daria , Irace Irene , Chiariello Mariarosaria , De Felice Noemi , Severino Simone , Viceconte Giulio , Schiano Moriello Nicola , Maraolo Alberto Enrico , Buonomo Antonio Riccardo , Giaccone Agnese ,   Federico II COVID team
Published: September 2024

AbstractIntroductionMolnupiravir (MNP) is an orally administered prodrug which prevents disease progression in patients at high risk for severe COVID-19. We conducted a real-life case-control study on a cohort of outpatients, with Omicron SARS-CoV-2 infection to assess the effectiveness of MNP in reducing the occurrence of hospital admission, admission in intensive care unit (ICU) and death at day 28.Materials and methodsCases were enrolled among SARS-CoV-2 positive subjects that sought medical care during the first five days of symptoms from January 1st, 2022, to December 31st, 2022, and received MNP. Control participants were selected from a regional database among those who tested positive during the study period and did not receive antiviral treatment for SARS-CoV-2.Results1382 patients were included (cases: 146, controls: 1236). Vaccinated patients showed lower risk of mortality and composite outcome (at least one among hospital admission, admission in ICU and all-cause death) compared to unvaccinated ones (0.6% vs 7.8%, p<0.001 and 2% vs 7.8%, p=0.001 respectively). After full-matching propensity score, MNP-treated subjects showed a lower incidence of composite outcome, while no effect was observed on the single outcomes. In the subgroup analysis according to the vaccination status, MNP proved effective in preventing all the outcomes among unvaccinated patients, while showed to reduce the risk of ICU admission both in vaccinated and unvaccinated patients.ConclusionsTreatment with MNP proved effective in reducing the risk of composite outcome among outpatients with SARS-CoV-2 infection. The beneficial effect of MNP treatment in reducing progression is more pronounced in unvaccinated patients.