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Corrigendum to “Building clinically actionable models for predicting mechanical complications in postoperatively well-aligned adult spinal deformity patients using XGBoost algorithm” [Inform Med Unlocked 37 (2023) 101191]

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Informatics in Medicine Unlocked 2023年 41卷

作者： Baris Balaban Caglar Yilgor Altug Yucekul Tais Zulemyan Ibrahim Obeid Javier Pizones Frank Kleinstueck Francisco Javier Sanchez Perez-Grueso Ferran Pellise Ahmet Alanay Osman Ugur Sezerman Department of Bioinformatics and Biostatistics Acibadem University Istanbul Turkey Department of Orthopedics and Traumatology Acibadem University School of Medicine Istanbul Turkey Comprehensive Spine Center Acibadem Maslak Hospital Istanbul Turkey Clinique du Dos Elsan Jean Villar Private Hospital Bordeaux France Spine Surgery Unit Hospital Universitario La Paz Madrid Spain Spine Center Division Department of Orthopedics and Neurosurgery Schulthess Klinik Switzerland Spine Surgery Unit Hospital Universitari Vall d'Hebron Barcelona Spain

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Insights into the solution structure of the actin-binding tail domain of metavinculin by small angle X-ray scattering and molecular dynamics simulations

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International journal of biological macromolecules 2025年 144376页

作者： Zeynep Aslıhan Durer Hande İpek İnce Zeynep Şevval Düvenci Emel Timuçin Tobias Gräwert Oya Orun Beki Kan Zehra Sayers School of Medicine Department of Biophysics Acibadem Mehmet Ali Aydinlar University Istanbul Turkey Faculty of Pharmacy Department of Biochemistry Acibadem Mehmet Ali Aydinlar University Istanbul Turkey. Electronic address: zeynep.durer@acibadem.edu.tr. Institute of Health Sciences Department of Biophysics Marmara University Istanbul Turkey. Institute of Health Sciences Department of Biostatistics and Bioinformatics Acibadem Mehmet Ali Aydinlar University Istanbul Turkey. Institute of Health Sciences Department of Biostatistics and Bioinformatics Acibadem Mehmet Ali Aydinlar University Istanbul Turkey School of Medicine Department of Biostatistics and Medical Informatics Acibadem Mehmet Ali Aydinlar University Istanbul Turkey. European Molecular Biology Laboratory - Hamburg Unit Biological Small Angle Scattering Group Hamburg Germany. Institute of Health Sciences Department of Biophysics Marmara University Istanbul Turkey School of Medicine Department of Biophysics Marmara University Istanbul Turkey. School of Medicine Department of Biophysics Acibadem Mehmet Ali Aydinlar University Istanbul Turkey. Faculty of Engineering and Natural Sciences Molecular Biology Genetics and Bioengineering Program Sabanci University İstanbul Turkey.

Vinculin is a ubiquitously expressed focal adhesion protein that plays an important role in cell-matrix and cell-to-cell junctions. Metavinculin, a muscle-specific splice variant of vinculin, contains a 68-amino acid disordered insert region in its actin binding tail domain (MVt). Mutations in this insert are linked to cardiomyopathies. This study investigates the solution structures and structural ensembles of wild-type (WT) and two mutant MVts, ΔLeu954 and R975W, which have been associated with cardiomyopathies, using small-angle X-ray scattering (SAXS) and molecular dynamics (MD) simulations. SAXS analyses revealed subtle differences in the estimated maximum dimensions and corroborated the elongated shape of the MVts. Quantitative comparisons of SAXS profiles indicated similarity between the WT and ΔLeu954, whereas R975W exhibited differences in the small-angle region. MD simulations demonstrated reduced conformational flexibility and greater packing of the insert in WT compared to mutants. Notably, a salt-bridge observed between R975 and D928 in a WT simulation provides a structural basis for the destabilization caused by the R975W mutation. These findings provide insights into the structure and dynamics of WT and mutant MVt, reflecting the promise of SAXS combined with MD simulations to elucidate the structural properties of proteins with structural disorder.

关键词： Metavinculin Molecular dynamics simulations Mutation SAXS Tail domain

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Efficacy of immune checkpoint inhibitors (ICIs) in PD-L1 negative Non-Small Cell Lung Cancer (NSCLC) – A meta -analysis based on reconstructed individual participant data

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Lung Cancer 2025年 205卷

作者： Maria Gemelli Diego Cortinovis Giorgia Carola Laura Moretti Francesca Piazza Stefano Calza Riccardo Ricotta Salvatore Grisanti Matteo Rota Medical Oncology Unit Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) MultiMedica 20099 Milan Italy Division of Medical Oncology Fondazione IRCCS San Gerardo dei Tintori Via GB Pergolesi 33 20900 Monza Italy School of Medicine and Surgery University of Milano Bicocca 20126 Milan Italy Department of Medical and Surgical Specialties Radiological Sciences and Public Health University of Brescia 25123 Brescia Italy Unit of Biostatistics and Bioinformatics Department of Molecular and Translational Medicine University of Brescia 25121 Brescia Italy Department of Medical Oncology-ASST Spedali Civili di Brescia 25123 Brescia Italy

Background despite advancements in NSCLC treatment, PD-L1 negative patients remain a therapeutic challenge. This meta -analysis evaluates the efficacy of immune checkpoint inhibitors (ICIs) and their combinations in PD-L1 negative NSCLC. Materials and Methods Major databases (PubMed, Embase, Cochrane Library) were searched using terms such as “PD-L1 negative,” “NSCLC,” “chemotherapy,” and “immunotherapy.” Phase I-III clinical trials comparing ICIs-based treatments in PD-L1 negative NSCLC were included, excluding non-peer-reviewed studies. Data extraction followed PRISMA guidelines, with independent review by two researchers. Random-effects models were used to pool data, assessing overall survival (OS), progression-free survival (PFS), and objective response rate (ORR). Results This analysis included 43 trials with 7039 patients. Immunotherapy alone yielded an ORR of 11 %, while immunotherapy combined with chemotherapy achieved the highest ORR (48 %). Combination immunotherapy alone and chemotherapy alone showed comparable ORRs (23 % vs. 22 %). Median PFS was 2.3 months for immunotherapy alone, 6.8 months for immunotherapy plus chemotherapy, and 5.7 months for combo immunotherapy. OS was 10.1 months for immunotherapy alone, 15.6 months for immunotherapy plus chemotherapy, and 17.6 months for combo immunotherapy. Network meta -analysis highlighted pembrolizumab with platinum-based chemotherapy as the most effective, achieving the highest ORR and a 45 % PFS improvement compared to chemotherapy alone. Conclusions Combination therapies, particularly ICIs with chemotherapy, significantly improve ORR, PFS, and OS in PD-L1 negative NSCLC, underscoring their potential as optimal strategies for this population.

关键词： Chemotherapy Immunotherapy Non– Small Cell Lung Cancer PD-L1 negative

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Inflammatory/Immune Adverse Events in Chronic Myeloid Leukemia Patients during Treatment with Bosutinib

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Blood 2024年 144卷 6595-6595页

作者： Elena Agostani Elena Tassistro Laura Antolini Carlo Gambacorti-Passerini Department of Medicine and Surgery University of Milano-Bicocca Monza Italy Hematology Department Fondazione IRCCS San Gerardo dei Tintori Lecco Italy Biostatistical and Clinical Epidemiology Fondazione IRCCS San Gerardo dei Tintori Monza Italy Bicocca Center of Bioinformatics Biostatistics and Bioimaging University of Milano-Bicocca Monza Italy Department of Medicine and Surgery University Milano-Bicocca Monza Italy Hematology Division and Bone Marrow Unit Fondazione IRCCS San Gerardo dei Tintori Monza Italy

Introduction Bosutinib, a third-generation tyrosine kinase inhibitor (TKI), is effective in treating Chronic Myeloid Leukemia (CML) patients who are resistant or intolerant to previous TKIs. Unlike other TKIs, bosutinib lacks inhibition of c-KIT and PDGFR, potentially contributing to its unique tolerability profile. It targets Bcr/Abl and SRC kinases, particularly Lyn, which plays a crucial role in immune response, particularly in myeloid cells and B lymphocytes, where it is involved in signaling cascades essential for self-tolerance and anergy. The susceptibility of Lyn -/- mice to lupus-like autoimmune disorders and the deregulation of Lyn- dependent pathways in patient with lupus were previously shown. The study aims to assess the time-adjusted rate (TAR) of inflammatory/immune-related adverse events (irAEs) in bosutinib-treated patients. Methods This retro-prospective observational cohort study was conducted involving 60 CML patients treated with bosutinib at San Gerardo Hospital from 2006 to 2020, with a minimum follow-up of three months, as part of bosutinib registration trials. The study also included 10 imatinib-treated patients enrolled as a control group in the same clinical studies. Data on adverse events (AEs) were collected and classified according to CTCAE terminology (v5.0). Patient characteristics were described using median and interquartile ranges for continuous variables and frequencies and percentages for categorical variables. The distribution of irAEs was displayed using a bar plot. The TAR of first and repeated AEs was calculated along with 95% confidence intervals (CI) using an exponential model. The cumulative incidence probability of the first AE was estimated using the Kaplan-Meier method. Analyses were performed with Stata 15 software. Results Of the 60 bosutinib-treated patients, 34 (56.6%) were male and almost all the patients were caucasian (98.3%) with a median age of 62.8 years. The median follow-up duration was 47.9 months (IQR 38.4

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Microbial diversity investigation using 16S metagenomics in Tunisian patients with systemic lupus erythematosus

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Revista Argentina de Microbiologia 2025年

作者： Messadi, Amira Sayhi, Sameh Ghedira, Kais Zaouaoui, Chadia Arfaoui, Bilel Khouikhi, Soumaya Rebai, Maha Guediche, Nour El Houda Abdelhafidh, Nadia Louzir, Bassem Faida, Ajili Research Unit of Autoimmune Disease UR17DN02 Military Hospital of Tunis Tunis Tunisia Internal Medicine Department Military Hospital of Tunis Tunisia University Tunis EL Manar Tunis Tunisia Laboratory of Bioinformatics Biomathematics and Biostatistics LR20IPT09 Pasteur Institute of Tunis University of Tunis El Manar Tunis Tunisia Endocrinology Department Military Hospital of Tunis Tunis Tunisia LR Biotechnology and Bio-Geo Resources Valorization (LR11ES31) University of Manouba Tunisia

Systemic lupus erythematosus (SLE) is a multisystemic autoimmune disease associated with significant morbidity and mortality. It is characterized by a loss of self-immune tolerance and autoantibody production, leading to multiple organ damage. Emerging investigations have confirmed the role of gut microbiota dysbiosis in patients with SLE, although the underlying mechanisms remain unclear to date. In this study, we aim to investigate the bacterial profile of SLE including phylum/class/genus relative abundance and diversity, to compare them with healthy controls and to study the correlation of relative abundance of different patterns with clinical/biological parameters. In this case–control study, the bacterial profile was investigated in 7 SLE patients and 7 healthy controls using 16S metagenomics clustering. The present study reported a low abundance of the class Bacilli (0.58% in SLE vs 1.26% in the controls), the genus Lactobacillus (0.43% vs 0.74%), as well as a higher abundance of the genera Gammaproteobacteria (2.37% vs 0.77%) and Escherichia–Shigella (2.04% vs 0.51%) in SLE samples compared to the controls (p < 0.05). We also found an association between the class Betaproteobacteria (4.42% vs 1.57%) and the genus Faecalibacterium (11.34% vs 3.35%) and renal manifestations (p < 0.05). The phylum Actinobacteria (0.21% vs 3.8%, p = 0.036) and the genus Bifidobacterium levels were lower in active SLE compared to the healthy controls. This study is the first report on the gut microbiota of SLE and the first case–control study in Tunisia and North Africa. We obtained a particular profile of bacterial gut microbiota for the SLE group. We found a specific clustering when compared to the healthy controls. © 2025 The Author(s)

关键词： 16S metagenomics bioinformatics Case–control studies Microbiome diversity Systemic lupus erythematous

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Timing and Variability of Maternal Hyperglycemia in Insulin-Dependent Diabetes, Long-Term Effects on Offspring Obesity—The TEAM Study

Pregnancy

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Pregnancy 2025年第4期1卷

作者： Katherine Bowers Krishna Bhoopathy Rhonda Szczesniak Shelley Ehrlich Lawrence M. Dolan Heidi Kalkwarf Suzanne Summer Emily Smith Mekibib Altaye Nicholas J. Ollberding Patrick Catalano Menachem Miodovnik Jane C. Khoury Division of Biostatistics and Epidemiology Cincinnati Children's Hospital Medical Center Cincinnati Ohio USA Department of Pediatrics University of Cincinnati College of Medicine Cincinnati Ohio USA Division of Biostatistics and Bioinformatics Environmental & Public Health Sciences University of Cincinnati College of Medicine Cincinnati Ohio USA Division of Endocrinology Cincinnati Children's Hospital Medical Center Cincinnati Ohio USA Division of Gastroenterology Cincinnati Children's Hospital Medical Center Cincinnati Ohio USA Schubert Research Clinic Cincinnati Children's Hospital Medical Center Cincinnati Ohio USA Reproductive Endocrinology Unit Massachusetts General Hospital Harvard Medical School Boston Massachusetts USA Division of Maternal-Fetal Medicine Department of Obstetrics and Gynecology Inova Health System Fairfax Virginia USA

Objective Limited data are available to describe the long-term implications of pre-pregnancy diabetes on offspring body composition in adulthood. The objective of this study was to examine the association between maternal glucose control and variation throughout pregnancy and long-term obesity in offspring. A second objective was to identify the critical windows of gestational exposure most related to the development of long-term obesity. Research Design and Methods This cohort study included offspring of women with insulin-dependent diabetes (IDDM) who participated in the Diabetes in Pregnancy Program Project Grant (PPG) between 1978 and 1995 to evaluate the long-term implications of in utero glucose exposure. Offspring completed a comprehensive study visit which included measures of height and weight, and measures from dual-energy X-ray absorptiometry. The exposure was maternal glucose control as measured by glycohemoglobin A 1 concentration (where HbA 1 was represented as standard deviation units from the mean for the laboratory HBA 1 SD), and blood glucose level and variation across pregnancy, and by trimester, characterized using functional principal components (fPC). Outcomes were adult offspring BMI (kg/m 2 ), as well as visceral and whole-body fat. Results A total of 161 offspring completed a study visit. Mean offspring age at follow-up was 32.3 years (± 4.6), 50.3% were female and 17.4% identified as Black. After adjustment for covariates (maternal age, education, family history of diabetes, diabetes severity, and BMI at last menstrual period), third-trimester maternal HbA 1 SD was significantly associated with offspring adiposity measures including BMI, BMI ≥ 30 kg/m 2 , and visceral and whole-body fat mass and percentage. The third-trimester fPC representing variability of glucose demonstrated a consistent positive association with obesity outcomes but did not reach statistical significance in adjusted models. Conclusions Our findings corroborate that the

关键词： longitudinal follow-up offspring obesity prepregnancy diabetes

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AI drives the assessment of lung cancer microenvironment composition

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Journal of Pathology Informatics 2024年 15卷 100400-100400页

作者： Gallo, Enzo Guardiani, Davide Betti, Martina Arteni, Brindusa Ana Maria Di Martino, Simona Baldinelli, Sara Daralioti, Theodora Merenda, Elisabetta Ascione, Andrea Visca, Paolo Pescarmona, Edoardo Lavitrano, Marialuisa Nisticò, Paola Ciliberto, Gennaro Pallocca, Matteo Department of Pathology IRCCS Regina Elena National Cancer Institute Rome Italy Biostatistics Bioinformatics and Clinical Trial Center IRCCS Regina Elena National Cancer Institute Rome Italy Department of Computer Control and Management Engineering La Sapienza University of Rome Rome Italy UOC Anatomy Pathology Biobank IRCCS Regina Elena National Cancer Institute Istituti Fisioterapici Ospitalieri IFO Rome Italy Department of Radiological Oncological and Pathological Sciences Sapienza University of Rome Policlinico Umberto I Rome Italy Department of Experimental Medicine Sapienza University of Rome Policlinico Umberto I Rome Italy School of Medicine and Surgery University of Milano-Bicocca Monza 20900 Italy Tumor Immunology and Immunotherapy Unit IRCCS Regina Elena National Cancer Institute Rome Italy Scientific Direction IRCCS Regina Elena National Cancer Institute Rome Italy Institute of Experimental Endocrinology and Oncology National Research Council Naples Italy

Purpose: The abundance and distribution of tumor-infiltrating lymphocytes (TILs) as well as that of other components of the tumor microenvironment is of particular importance for predicting response to immunotherapy in lung cancer (LC). We describe here a pilot study employing artificial intelligence (AI) in the assessment of TILs and other cell populations, intending to reduce the inter- or intra-observer variability that commonly characterizes this evaluation. Design: We developed a machine learning-based classifier to detect tumor, immune, and stromal cells on hematoxylin and eosin-stained sections, using the open-source framework QuPath. We evaluated the quantity of the aforementioned three cell populations among 37 LC whole slide images regions of interest, comparing the assessments made by five pathologists, both before and after using graphical predictions made by AI, for a total of 1110 quantitative measurements. Results: Our findings indicate noteworthy variations in score distribution among pathologists and between individual pathologists and AI. The AI-guided pathologist's evaluations resulted in reduction of significant discrepancies across pathologists: three comparisons showed a loss of significance (p > 0.05), whereas other four showed a reduction in significance (p > 0.01). Conclusions: We show that employing a machine learning approach in cell population quantification reduces inter- and intra-observer variability, improving reproducibility and facilitating its use in further validation studies. © 2024 The Authors

关键词： Computer-aided tool Digital pathology Lung cancer Machine learning NSCLC Pathology image QuPath Tumor-infiltrating lymphocytes Whole slide images

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Correction: Risk factors associated with post-weaning diarrhoea in Austrian piglet-producing farms

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Porcine Health Management 2023年第1期9卷 1-1页

作者： René Renzhammer Lukas Schwarz Andrea Ladinig Sebastian Vetter Annemarie Käsbohrer Marlies Dolezal Department for Farm Animals and Veterinary Public Health University Clinic for Swine University of Veterinary Medicine Veterinärplatz 1 1210 Vienna Austria Unit of Veterinary Public Health and Epidemiology Institute of Food Safety Food Technology and Veterinary Public Health University of Veterinary Medicine Veterinärplatz 1 1210 Vienna Austria Platform for Bioinformatics and Biostatistics Department of Biomedical Sciences University of Veterinary Medicine Veterinärplatz 1 1210 Vienna Austria

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Sample size estimation for comparing dynamic treatment regimens in a SMART: a Monte Carlo-based approach and case study with longitudinal overdispersed count outcomes

arXiv

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arXiv 2021年

作者： Yap, Jamie Dziak, John J. Maiti, Raju Lynch, Kevin McKay, James R. Chakraborty, Bibhas Nahum-Shani, Inbal Institute for Social Research Ann ArborMI United States Bennett Pierce Prevention Research Center Pennsylvania State University PA United States Economic Research Unit Indian Statistical Institute Kolkata India Department of Psychiatry University of Pennsylvania PA United States Philadephia Veterans Affairs Medical Center PA United States Centre for Quantitative Medicine Duke-NUS Medical School Singapore Department of Statistics and Data Science National University of Singapore Singapore Department of Biostatistics and Bioinformatics Duke University DurhamNC United States

Dynamic treatment regimens (DTRs), also known as treatment algorithms or adaptive interventions, play an increasingly important role in many health domains. DTRs are motivated to address the unique and changing needs of individuals by delivering the type of treatment needed, when needed, while minimizing unnecessary treatment. Practically, a DTR is a sequence of decision rules that specify, for each of several points in time, how available information about the individual’s status and progress should be used in practice to decide which treatment (e.g., type or intensity) to deliver. The sequential multiple assignment randomized trial (SMART) is an experimental design widely used to empirically inform the development of DTRs. Sample size planning resources for SMARTs have been developed for continuous, binary, and survival outcomes. However, an important gap exists in sample size estimation methodology for SMARTs with longitudinal count outcomes. Further, in many health domains, count data are overdispersed – having variance greater than their mean. We propose a Monte Carlo-based approach to sample size estimation applicable to many types of longitudinal outcomes and provide a case study with longitudinal overdispersed count outcomes. A SMART for engaging alcohol and cocaine-dependent patients in treatment is used as motivation. © 2021, CC BY.

关键词： Patient treatment

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The Immune-Silenced Dialogue In Endometrial Cancer Tumor Microenvironment Escape: Could The Volume Be Tuned Up By The Fetal-Maternal Immune Tolerance Cross-Talk?

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International Journal of Gynecological Cancer 2025年第2期35卷

作者： Valentina Bruno Margherita Ferretti Martina Betti Stefano Scalera Simona Di Martino Ana Maria Arteni Brindusa Ludovica Ciuffreda Francesca De Nicola Alessandra Logoteta Emanuela Mancini Ermelinda Baiocco Cristina Vincenzoni Giuseppe Cutillo Livia Ronchetti Brunella Zizolfi Mariantonia Carosi Giulia Piaggio Maurizio Fanciulli Attilio Di Spiezio Sardo Enrico Vizza Dept. of Experimental Clinical Oncology Gynecologic Oncology Unit IRCCS- Regina Elena National Cancer Institute Rome Italy Dept. of Research Diagnosis and Innovative Technologies UOSD SAFU IRCCS- Regina Elena National Cancer Institute Rome Italy Biostatistics Bioinformatics and Clinical Trial Center IRCCS- Regina Elena National Cancer Institute Rome Italy Dept. of Research Diagnosis and Innovative Technologies Anatomy Pathology Unit IRCCS- Regina Elena National Cancer Institute Rome Italy Department of Biomedicine and Prevention Biotechnologies and Translational Medicine Tor Vergata University Rome Italy Department of Public Health University of Naples Federico II Naples Italy

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