bluuebunny/crossref_metadata_2025_split huggingface dataset:
Long-term 3-year follow-up of mosunetuzumab in relapsed or refractory follicular lymphoma after ≥2 prior therapies
Authors: Sehn Laurie H. , Bartlett Nancy L. , Matasar Matthew J. , Schuster Stephen J. , Assouline Sarit E. , Giri Pratyush , Kuruvilla John , Shadman Mazyar , Cheah Chan Yoon , Dietrich Sascha , Fay Keith , Ku Matthew , Nastoupil Loretta J. , Wei Michael C. , Yin Shen , To Iris , Kaufman Derrick , Kwan Antonia , Penuel Elicia , Bolen Christopher R. , Budde Lihua E. 
Published: October 2024

Abstract
Mosunetuzumab, a CD20×CD3 T-cell engaging bispecific antibody, redirects T cells to eliminate malignant B cells. We present updated efficacy and safety data of a pivotal phase 1/2 study after a median follow-up of 37.4 months in 90 patients with relapsed/refractory (R/R) follicular lymphoma (FL) and ≥2 prior lines of therapy treated with fixed-duration mosunetuzumab. Investigator-assessed complete response (CR) rate and objective response rate were 60.0% (95% confidence interval [CI], 49.1-70.2) and 77.8% (95% CI, 67.8-85.9), respectively. Among 70 responders, median duration of response was 35.9 months (95% CI, 20.7 to not estimable [NE]). Among 54 patients who achieved CR, 49 remained in CR at the end of treatment; median duration of CR was not reached (NR; 95% CI, 33.0 to NE); Kaplan-Meier-estimated 30-month remission rate was 72.4% (95% CI, 59.2-85.6). Estimated 36-month overall survival (OS) rate was 82.4% (95% CI, 73.8-91.0); median OS was NR (95% CI, NE to NE). Median progression-free survival was 24.0 months (95% CI, 12.0 to NE). Median time to CD19+ B-cell recovery was 18.4 months (95% CI, 12.8-25.0) after 8 cycles of mosunetuzumab treatment. No new cytokine release syndrome events or fatal, serious, or grade ≥3 adverse events were reported. With extended follow-up, mosunetuzumab demonstrated high response rates, durable remissions, and manageable safety with no long-term concerns. This supports outpatient mosunetuzumab administration as an off-the-shelf, fixed-duration, safe, and effective treatment for patients with R/R FL, including those with high-risk disease. This trial was registered at www.clinicaltrials.gov as #NCT02500407.
Compressive Mechanical and Heat Conduction Properties of AlSi10Mg Gradient Metamaterials Fabricated via Laser Powder Bed Fusion
Authors: Sun Qidong , Zhi Geng , Zhou Sheng , Tao Ran , Qi Junfeng 
Published: October 2024

AbstractMetamaterials are defined as artificially designed micro-architectures with unusual physical properties, including optical, electromagnetic, mechanical, and thermal characteristics. This study investigates the compressive mechanical and heat transfer properties of AlSi10Mg gradient metamaterials fabricated by Laser Powder Bed Fusion (LPBF). The morphology of the AlSi10Mg metamaterials was examined using an ultrahigh-resolution microscope. Quasi-static uniaxial compression tests were conducted at room temperature, with deformation behavior captured through camera recordings. The findings indicate that the proposed gradient metamaterial exhibits superior compressive strength properties and energy absorption capacity. The Gradient-SplitP structure demonstrated better compressive performance compared to other strut-based structures, including Gradient-Gyroid and Gradient-Lidinoid structures. With an apparent density of 0.796, the Gradient-SplitP structure exhibited an outstanding energy absorption capacity, reaching an impressive 23.57 MJ/m3. In addition, heat conductivity tests were performed to assess the thermal resistance of these structures with different cell configurations. The gradient metamaterials exhibited higher thermal resistance and lower thermal conductivity. Consequently, the designed gradient metamaterials can be considered valuable in various applications, such as thermal management, load-bearing, and energy absorption components.
LLpowershap: logistic loss-based automated Shapley values feature selection method
Authors: Madakkatel Iqbal , Hyppönen Elina 
Published: October 2024

Abstract
Background
Shapley values have been used extensively in machine learning, not only to explain black box machine learning models, but among other tasks, also to conduct model debugging, sensitivity and fairness analyses and to select important features for robust modelling and for further follow-up analyses. Shapley values satisfy certain axioms that promote fairness in distributing contributions of features toward prediction or reducing error, after accounting for non-linear relationships and interactions when complex machine learning models are employed. Recently, feature selection methods using predictive Shapley values and p-values have been introduced, including powershap.

Methods
We present a novel feature selection method, LLpowershap, that takes forward these recent advances by employing loss-based Shapley values to identify informative features with minimal noise among the selected sets of features. We also enhance the calculation of p-values and power to identify informative features and to estimate number of iterations of model development and testing.

Results
Our simulation results show that LLpowershap not only identifies higher number of informative features but outputs fewer noise features compared to other state-of-the-art feature selection methods. Benchmarking results on four real-world datasets demonstrate higher or comparable predictive performance of LLpowershap compared to other Shapley based wrapper methods, or filter methods. LLpowershap is also ranked the best in mean ranking among the seven feature selection methods tested on the benchmark datasets.

Conclusion
Our results demonstrate that LLpowershap is a viable wrapper feature selection method that can be used for feature selection in large biomedical datasets and other settings.
Successful ability to stay at home - an interview study exploring multiple diagnosed older persons and their relatives’ experiences
Authors: Gustafsson Lena-Karin , Bondesson Anna , Pettersson Tina , Östlund Gunnel 
Published: October 2024

Abstract
Background
Society places increased demands on regions and municipalities to jointly carry out activities for multi-diagnosed older persons with extensive coordination needs. Interprofessional collaboration is reported as an important success factor for the overall health care of this group of patients. This project focuses on older persons with multiple diagnoses and their relatives’ own experiences of what is most important for safety and security in their homes. The aim of the study was: to illuminate the meaning of success for the ability to stay at home as experienced by older persons with multiple diagnoses and their relatives.

Methods
The project had a descriptive explorative design with a phenomenological hermeneutic approach based on analysis of 14 in-depth interviews with older people and their relatives.

Findings
Own resources were identified such as belief in the future, spiritual belief, social network, having loved ones and pets. Technical aids were seen as helpful resources, working as indoor and outdoor security safeguards. These resources included having good telephone contact with social and professional networks as well as other forms of personal equipment such as a personal alarm. The professional network was a resource, acting as support when the patient’s own abilities were not sufficient. Finally, having personnel who had the time and interest to listen was seen as crucial to experience safety.

Conclusions
The main reason for being able to continue homecare was the person’s self-care system, their personal, social, and technical resources. Professional care development should anchor team work to the patient’s own system of self and informal care.
A conceptual health state diagram for modelling the transmission of a (re)emerging infectious respiratory disease in a human population
Authors: Avramov Marc , Gabriele-Rivet Vanessa , Milwid Rachael M. , Ng Victoria , Ogden Nicholas H. , Hongoh Valerie 
Published: October 2024

AbstractMathematical modelling of (re)emerging infectious respiratory diseases among humans poses multiple challenges for modellers, which can arise as a result of limited data and surveillance, uncertainty in the natural history of the disease, as well as public health and individual responses to outbreaks. Here, we propose a COVID-19-inspired health state diagram (HSD) to serve as a foundational framework for conceptualising the modelling process for (re)emerging respiratory diseases, and public health responses, in the early stages of their emergence. The HSD aims to serve as a starting point for reflection on the structure and parameterisation of a transmission model to assess the impact of the (re)emerging disease and the capacity of public health interventions to control transmission. We also explore the adaptability of the HSD to different (re)emerging diseases using the characteristics of three respiratory diseases of historical public health importance. We outline key questions to contemplate when applying and adapting this HSD to (re)emerging infectious diseases and provide reflections on adapting the framework for public health-related interventions.
Longitudinal assessment of the impact of prevalent diabetes on hospital admissions and mortality in the general population: a prospective population-based study with 19 years of follow-up
Authors: Johansson Madeleine , Åkesson Anna , Nilsson Peter M. , Melander Olle 
Published: October 2024

Abstract
Background
Hospitalization indicates the presence of severe disease and constitutes a leading cost in health care. We aimed to prospectively assess if prevalent diabetes mellitus contributes to excess all-cause and cause-specific hospital admissions and mortality at the population level.

Methods
We used a Swedish prospective population-based cohort, including 25,642 individuals of whom 4.2% had prevalent diabetes at baseline (mean age 61.2 ± 6.8 years, age range 44.8–73.4 years). We compared the number of hospitalizations and mortality classified according to the main chapters of the 10th revision of the International Classification of Diseases (ICD-10) during follow-up using nationwide inpatient registries, comparing individuals with and without prevalent diabetes, using multivariate adjusted negative binomial regression (incidence rate ratio, IRR) and Cox regression, respectively.

Results
During a median follow-up of 19 years, 18,904 subjects were hospitalized at least once [median 3 (IQR 2–6)] and 6767 (26.4%) individuals died. Overall, subjects with diabetes were hospitalized (IRR 1.83, p < 0.001) more often, and had a higher incidence rate of hospital admissions due to endocrine diseases (IRR 14.6, p < 0.001), dermatological diseases (IRR 3.7, p < 0.001), injuries and poisoning (IRR 2.7, p < 0.001), infectious diseases (IRR 2.5, p < 0.001), psychiatric diseases (IRR 2.0, p < 0.001), but also cardiovascular, hematological, genitourinary, neurologic and respiratory diseases compared with non-diabetic individuals. No difference was observed for hospital admissions due to cancer or musculoskeletal disorders. All-cause mortality was higher (HR 1.77, p < 0.001) in individuals with diabetes, with disease-specific mortality being significant only for cardiovascular and endocrine disease-related death.

Conclusions
At the population level, prevalent diabetes increased the hospitalization burden longitudinally due to diseases of most of the ICD-10 main chapters, except for cancer and musculoskeletal disorders. These novel findings challenge the current view on the spectrum of prevalent diabetes-related conditions and may have implications for screening and treatment strategies in diabetes.

Permission of graphical illustrations
Source: Pixabay.com. No permission or acknowledgement is required.
Comparative evaluation of surface roughness and bacterial adhesion on two bioactive cements: an in-vitro study
Authors: Dey Pallabi , Suprabha Baranya Shrikrishna , Suman Ethel , Natarajan Srikant , Shenoy Ramya , Rao Arathi 
Published: October 2024

Abstract
Background
Dental restorative materials are recognized as artificial niches that facilitate the adherence and accumulation of oral microorganisms. To mitigate oral diseases and extend the lifespan of restorations, it is advantageous to use dental materials that exhibit low susceptibility to bacterial adhesion.

Objective
To evaluate and compare bacterial adhesion on two bioactive restorative materials, a glass hybrid restorative, and an alkasite with a nanohybrid resin composite as a positive control. The secondary objectives were to compare the surface roughness (SR) of the materials and determine the correlation between the bacterial adhesion and the SR.

Materials and methods
The samples consisted of 33 polished discs of each material: Group A: Tetric® N-Ceram (nanohybrid resin composite), Group B: Equia Forte™ HT Fil (glass hybrid restorative) and Group C: Cention N® (alkasite). Streptococcus mutans cultures were inoculated and after 24-hours of incubation, bacterial adhesion was measured by measuring optical density (OD) and number of colony forming units (CFUs). After 96-hours incubation, the bacterial cell count was determined using scanning electron microscopy (SEM). SR was assessed using surface profilometer.

Results
Alkasite had significantly lower OD and CFUs (p < 0.001 and p = 0.015 respectively). According to the SEM analysis, the glass hybrid restorative had lower mean bacterial cell count with no significant difference between the groups. The nanohybrid composite had the smoothest surface that was significantly lower than the alkasite and glass hybrid restorative (p = 0.002). None of the groups demonstrated a correlation between bacterial adhesion and SR.

Conclusion
Alkasite impedes bacterial adhesion better than the glass hybrid restorative and nanohybrid composite, while smoother surfaces are achieved with the nanohybrid composite.
Registrars as teachers: a qualitative study exploring the experiences of Australian general practice registrars in teaching roles
Authors: Fisher Katie , Patsan Irena , Klein Linda , Turner Alexandria , Runciman Dave , Fielding Alison , Tran Michael , Pond Dimity , Guppy Michelle , Starling Chris , D’Amore Angelo , Davey Andrew , Magin Parker 
Published: October 2024

Abstract
Background
GP registrars (specialist vocational trainees in general practice) are interested in teaching, and there are considerable benefits to teaching during training. There are, however, significant barriers for registrars as teachers, including inadequate funding, time pressures, and limited teacher training. Current evidence does not include medical educator (ME) perspectives or compare teaching settings (e.g. university vs. in-practice). Further evidence is needed to inform programs supporting registrar teaching roles.
This project aimed to explore the experiences of Australian GP registrars as teachers in different contexts and from multiple stakeholder perspectives.

Methods
A qualitative study with GP registrars, GP supervisors, MEs, and medical students was conducted. Participants were registrars and new (within 12 months) Fellows with teaching experience during training, supervisors who supervised a registrar in the preceding 12 months, Royal Australian College of General Practitioner MEs, and medical students with experiences of being taught by registrars. Recruitment was open to participants nationwide and sampling was purposive, aiming for a maximum variation sample. Data collection was performed via videoconference and analysed using reflexive thematic analysis.

Findings
Interviews were conducted with 15 registrars, 10 supervisors, and one ME. Two focus groups involved four MEs and five medical students respectively. Registrar participants taught in a variety of settings, including in-practice, universities, hospitals, and at educational workshop days. Three had experience in GP academic posts and one as a registrar ME. There were four major themes. 1) Near-peer teaching by registrars is valuable - both for medical students and registrars. 2) Teaching makes you a better GP - participants noted the transferability of teaching skills to clinical practice. 3) The importance of the teaching context
– this was identified as an important determinant for registrars in teaching roles. 4) Registrar teaching strengthens the GP workforce – participants noted that teaching could elevate general practice as a specialty and increase interest in GP training.

Conclusions
Study participants saw teaching as a core skill for GPs, with transferability to their clinical practice. Registrar participants wanted greater promotion and support for teaching opportunities that counted towards attainment of Fellowship. These findings have implications for teaching practices, MEs, universities, and training providers.
TECRR: a benchmark dataset of radiological reports for BI-RADS classification with machine learning, deep learning, and large language model baselines
Authors: Hussain Sadam , Naseem Usman , Ali Mansoor , Avendaño Avalos Daly Betzabeth , Cardona-Huerta Servando , Bosques Palomo Beatriz Alejandra , Tamez-Peña Jose Gerardo 
Published: October 2024

Abstract
Background
Recently, machine learning (ML), deep learning (DL), and natural language processing (NLP) have provided promising results in the free-form radiological reports’ classification in the respective medical domain. In order to classify radiological reports properly, a high-quality annotated and curated dataset is required. Currently, no publicly available breast imaging-based radiological dataset exists for the classification of Breast Imaging Reporting and Data System (BI-RADS) categories and breast density scores, as characterized by the American College of Radiology (ACR). To tackle this problem, we construct and annotate a breast imaging-based radiological reports dataset and its benchmark results.
The dataset was originally in Spanish. Board-certified radiologists collected and annotated it according to the BI-RADS lexicon and categories at the Breast Radiology department, TecSalud Hospitals Monterrey, Mexico. Initially, it was translated into English language using Google Translate. Afterwards, it was preprocessed by removing duplicates and missing values. After preprocessing, the final dataset consists of 5046 unique reports from 5046 patients with an average age of 53 years and 100% women. Furthermore, we used word-level NLP-based embedding techniques, term frequency-inverse document frequency (TF-IDF) and word2vec to extract semantic and syntactic information. We also compared the performance of ML, DL and large language models (LLMs) classifiers for BI-RADS category classification.

Results
The final breast imaging-based radiological reports dataset contains 5046 unique reports. We compared K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), Adaptive Boosting (AdaBoost), Gradient-Boosting (GB), Extreme Gradient Boosting (XGB), Long Short-Term Memory (LSTM), Bidirectional Encoder Representations from Transformers (BERT) and Biomedical Generative Pre-trained Transformer (BioGPT) classifiers. It is observed that the BioGPT classifier with preprocessed data performed 6% better with a mean sensitivity of 0.60 (95% confidence interval (CI), 0.391-0.812) compared to the second best performing classifier BERT, which achieved mean sensitivity of 0.54 (95% CI, 0.477-0.607).

Conclusion
In this work, we propose a curated and annotated benchmark dataset that can be used for BI-RADS and breast density category classification. We also provide baseline results of most ML, DL and LLMs models for BI-RADS classification that can be used as a starting point for future investigation. The main objective of this investigation is to provide a repository for the investigators who wish to enter the field to push the boundaries further.
Obesity cardiomyopathy could contribute to sudden cardiac death: a Japanese epidemiological morphological study
Authors: Kaimori Ryo , Nishida Haruto , Tamura Mari , Kuroki Kohji , Murata Kumi , Kawamura Kazuhiro , Mori Shinjiro , Daa Tsutomu 
Published: October 2024

Abstract
Background
We aimed to clarify the existence and pathological features of obesity cardiomyopathy (OCM) in Japan using our series of autopsy cases.

Methods
In this retrospective autopsy study, OCM was defined as cardiac hypertrophy (≥ 400 g in men, ≥ 320 g in women) of unknown aetiology in individuals with obesity (body mass index [BMI] ≥ 25 kg/m2 according to the Japanese definition of obesity). We compared cases of OCM with those with obesity without cardiac hypertrophy (OB) and normal weight without cardiac hypertrophy (normal control). Macroscopically, heart weight and cardiac parameters, including epicardial adipose tissue, were measured. Fibrosis, cardiomyocyte diameter, and adipose tissue infiltration were analysed microscopically.

Results
Of the 294 cases, we identified 19 cases of OCM (6.5%) and compared them with the OB and normal control groups. Patients with OCM were slightly younger than non-OCM patients (p = 0.081). The median heart weight was significantly heavier in OCM cases than in OB cases (435 g, interquartile range [IQR] 408–515 g vs. 360 g, IQR 341–385 g). Macroscopically, OCM hearts had a “globoid” appearance with a thickened right ventricular outflow tract. Some OCM cases showed focal interstitial fibrosis in the left ventricle. Approximately half the OCM cases were diagnosed with sudden cardiac death (SCD), with significant differences.

Conclusions
The prevalence of OCM may be higher than expected in Japan, and this may be a specific pathological finding. Given that approximately half the cases of OCM were due to SCD, OCM may cause SCD, emphasizing the need to recognise and diagnose OCM.