bluuebunny/crossref_metadata_2025_split huggingface dataset:
Diagnostic value of dual-layer spectral detector CT parameters for differentiating high- from low-grade bladder cancer
Authors: Chen Li , Xu Lili , Zhang Xiaoxiao , Zhang Jiahui , Bai Xin , Peng Qianyu , Guo Erjia , Lu Xiaomei , Yu Shenghui , Jin Zhengyu , Zhang Gumuyang , Xie Yi , Xue Huadan , Sun Hao 
Published: January 2025

Abstract
Objectives
This study aimed to investigate the diagnostic value of spectral parameters of dual-layer spectral detector computed tomography (DLCT) in distinguishing between low- and high-grade bladder cancer (BCa).

Methods
This single-center retrospective study included pathologically confirmed BCa patients who underwent preoperative contrast-enhanced DLCT. Patients were divided into low- and high-grade groups based on pathology. We measured and calculated the following spectral CT parameters: iodine density (ID), normalized ID (NID), arterial enhancement fraction (AEF), extracellular volume (ECV) fraction, virtual non-contrast (VNC), slope of the attenuation curve, and Z effective (Zeff). Univariate and multivariable logistic regression analyses were used to determine the best predictive factors in differentiating between low- and high-grade BCa. We used receiver operating characteristic curve analysis to assess diagnostic performance and decision curve analysis to determine the net benefit.

Results
The study included 64 patients (mean age, 64 ± 11.0 years; 46 men), of whom 42 had high-grade BCa and 22 had low-grade BCa. Univariate analysis revealed that differences in ID and NID in the corticomedullary phase, AEF, ECV, VNC, and Zeff images were statistically significant (p = 0.001–0.048). Multivariable analysis found that AEF was the best predictor of high-grade tumors (p = 0.006). With AEF higher in high-grade BCa, AEF results were as follows: area under the curve (AUC), 0.924 (95% confidence interval, 0.861–0.988); sensitivity, 95.5%; specificity, 81.0%; and accuracy, 85.9%. The cutoff valve of AEF for predicting high-grade BCa was 67.7%.

Conclusion
Using DLCT AEF could help distinguish high-grade from low-grade BCa.

Critical relevance statement
This research demonstrates that the arterial enhancement fraction (AEF), a parameter derived from dual-layer spectral detector CT (DLCT), effectively distinguishes between high- and low-grade bladder cancer, thereby aiding in the selection of appropriate clinical treatment strategies.

Key Points

This study investigated the value of dual-layer spectral detector CT in the assessment of bladder cancer (BCa) histological grade.

The spectral parameter arterial enhancement fraction could help determine BCa grade.

Our results can help clinicians formulate initial treatment strategies and improve prognostications.

Graphical Abstract
Introducing an index on prediction of post-revascularization cerebral infarction using preoperative CT perfusion parameters in moyamoya disease
Authors: Hao Xiaojun , Zhang Chao , Yang Chen , Zhao Xintong , Zhou Yunfeng , Wang Juan 
Published: January 2025

Abstract
Objective
To determine the value of preoperative CT perfusion (CTP) parameters for prediction of post-revascularization cerebral infarction (post-CI) in adults with moyamoya disease (MMD).

Methods
This retrospective study included 92 adults with MMD who underwent surgical revascularization. Preoperative quantitative CTP parameters, including cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT), time to drain (TTD), and transit time to maximum of the residue function (Tmax), along with clinical data, were compared between the groups with and without post-CI. Predictors of post-CI were identified and assessed using multivariable logistic regression and receiver-operating characteristic curve analyses.

Results
Post-CI occurred in 11 patients (12.0%). In univariate analysis, preoperative mean values for CBF, MTT, TTD, Tmax, initial presentation, infarction within the 2 months before surgery, surgical side, and modified Rankin Scale score on admission were associated with post-CI (all p < 0.05). Multivariable logistic regression revealed that the preoperative mean Tmax (OR 2.342, 95% CI: 1.267–4.330, p = 0.007) and infarction within the 2 months before surgery (OR 14.345, 95% CI: 2.108–97.638, p = 0.006) were independent predictors of post-CI. The preoperative mean Tmax produced the largest area under the curve (0.955, 95% CI: 0.914–0.997) with a cutoff of 3.590 s (sensitivity, 100%; specificity, 87.7%).

Conclusions
Adults with MMD are at risk of post-CI when the preoperative mean Tmax is > 3.590 s. Cerebral infarction during the 2 months before revascularization is also a risk factor for post-CI.

Critical relevance statement
Post-CI is a serious complication for adults with MMD following surgical revascularization. The risk of post-CI can be predicted using preoperative CTP parameters, which will assist neurosurgeons with surgical decisions and implementing individualized prophylactic strategies.

Key Points

Predicting the risk of post-CI in MMD patients is beneficial to their prognosis.

The preoperative mean Tmax was an excellent perfusion parameter for predicting post-CI.

Preoperative CTP evaluation can help clinicians make cautious surgical decisions.

Graphical Abstract
Using low-discrepancy points for data compression in machine learning: an experimental comparison
Authors: Göttlich S. , Heieck J. , Neuenkirch A. 
Published: January 2025

AbstractLow-discrepancy points (also called Quasi-Monte Carlo points) are deterministically and cleverly chosen point sets in the unit cube, which provide an approximation of the uniform distribution. We explore two methods based on such low-discrepancy points to reduce large data sets in order to train neural networks. The first one is the method of Dick and Feischl (J Complex 67:101587, 2021), which relies on digital nets and an averaging procedure. Motivated by our experimental findings, we construct a second method, which again uses digital nets, but Voronoi clustering instead of averaging. Both methods are compared to the supercompress approach of (Stat Anal Data Min ASA Data Sci J 14:217–229, 2021), which is a variant of the K-means clustering algorithm. The comparison is done in terms of the compression error for different objective functions and the accuracy of the training of a neural network.
Prominent efficacy and good safety of sequential CD19 and CD22 CAR-T therapy in relapsed/refractory adult B-cell acute lymphoblastic leukemia
Authors: Yang Tingting , Dong Yetian , Zhang Mingming , Feng Jingjing , Fu Shan , Xiao Pingnan , Hong Ruimin , Xu Huijun , Cui Jiazhen , Huang Simao , Wei Guoqing , Kong Delin , Geng Jia , Chang Alex H. , Huang He , Hu Yongxian 
Published: January 2025

Abstract
Background
Sequential CD19 and CD22 chimeric antigen receptor (CAR)-T cell therapy offers a promising approach to antigen-loss relapse in relapsed/refractory (R/R) B-cell acute lymphoblastic leukemia (B-ALL); however, research in adults remains limited.

Methods
This study aimed to evaluate the efficacy and safety of sequential CD19 and CD22 CAR-T cell therapy in adult patients with R/R B-ALL between November 2020 and November 2023 (ChiCTR2100053871). Key endpoints included the adverse event incidence, overall survival (OS), and leukemia-free survival (LFS).

Results
Twenty-three patients with a median age of 58.1 years (range 25.9–75.0) were enrolled. High-risk cytogenetic and genomic aberrations were identified in 43.5% of patients, and five patients had baseline extramedullary disease (EMD). The median interval between the two infusions was 3.8 months. Grade ≥ 3 hematological adverse events occurred at comparable rates after both infusions. Cytokine release syndrome was observed in 78.3% and 39.1% of patients after CD19 and CD22 CAR-T therapy, respectively. Two patients experienced grade 2 immune effector cell-associated neurotoxicity syndrome (ICANS) during CD19 CAR-T, and no ICANS was reported during CD22 CAR-T. The median OS was not reached with a median follow-up of 19.4 months (range 8.7–45.6), while the median LFS was 20.8 months. OS and LFS rates were 91.3% and 67.1% at 1 year and 58.6% and 47.0% at 2 years, respectively. Eight patients experienced relapse, with the cumulative incidence of relapse being 28.6% at 1 year and 42.5% at 2 years. Higher baseline leukemia burden (≥ 64% bone marrow blasts) and the presence of EMD were significant risk factors for inferior OS and LFS, respectively.

Conclusions
Sequential CAR-T cell therapy demonstrated durable efficacy and a manageable safety profile in R/R B-ALL, providing a viable option to address antigen-loss relapse and improve long-term outcomes in high-risk adult patients.
A machine learning-based model to predict POD24 in follicular lymphoma: a study by the Chinese workshop on follicular lymphoma
Authors: Zha Jie , Chen Qinwei , Zhang Wei , Jing Hongmei , Ye Jingjing , Liu Huanhuan , Yu Haifeng , Yi Shuhua , Li Caixia , Zheng Zhong , Xu Wei , Li Zhifeng , Lin Zhijuan , Ping Lingyan , He Xiaohua , Zhang Liling , Xie Ying , Chen Feili , Sun Xiuhua , Su Liping , Zhang Huilai , Yang Haiyan , Zhao Weili , Qiu Lugui , Li Zhiming , Song Yuqin , Xu Bing 
Published: January 2025

Abstract
Background
Disease progression within 24 months (POD24) significantly impacts overall survival (OS) in patients with follicular lymphoma (FL). This study aimed to develop a robust predictive model, FLIPI-C, using a machine learning approach to identify FL patients at high risk of POD24.

Methods
A cohort of 1,938 FL patients (FL1-3a) from seventeen centers nationwide in China was randomly divided into training and internal validation sets (2:1 ratio). XGBoost was utilized to construct the POD24-predicting model, which was internally validated in the validation set and externally validated in the GALLIUM cohort. Key predictors of POD24 included lymphocyte-to-monocyte ratio (LMR), lactate dehydrogenase (LDH) > ULN, low hemoglobin (Hb), elevated beta-2 microglobulin (β2-MG), maximum standardized uptake value (SUVmax), and lymph node involvement. The FLIPI-C model assigned 2 points to LMR and 1 point to each of the other variables.

Results
The FLIPI-C model demonstrated superior accuracy (AUC) for predicting POD24 and 3-year overall survival (OS) in both the internal (AUC POD24: 0.764, OS: 0.700) and external validation cohorts (AUC POD24: 0.703, OS: 0.653), compared to existing models (FLIPI, FLIPI-2, PRIMA-PI, FLEX). Decision curve analysis confirmed the superior net benefits of FLIPI-C.

Conclusions
Developed using a machine learning approach, the FLIPI-C model offers superior predictive accuracy and utilizes simple, widely available markers. It holds promise for informing treatment decisions and prognostic assessments in clinical practice for FL patients at high risk of POD24.
The hidden rhythms of epilepsy: exploring biological clocks and epileptic seizure dynamics
Authors: Niu Ruili , Guo Xuan , Wang Jiaoyang , Yang Xiaofeng 
Published: January 2025

AbstractEpilepsy, characterized by recurrent seizures, is influenced by biological rhythms, such as circadian, seasonal, and menstrual cycles. These rhythms affect the frequency, severity, and timing of seizures, although the precise mechanisms underlying these associations remain unclear. This review examines the role of biological clocks, particularly the core circadian genes Bmal1, Clock, Per, and Cry, in regulating neuronal excitability and epilepsy susceptibility. We explore how the sleep-wake cycle, particularly non-rapid eye movement sleep, increases the risk of seizures, and discuss the circadian modulation of neurotransmitters like gamma-aminobutyric acid and glutamate. We explore clinical implications, including chronotherapy which refers to the practice of timing medical treatments to align with the body's natural biological rhythms, such as the circadian rhythm. Chronotherapy aligns anti-seizure medication administration with biological rhythms. We also discuss rhythm-based neuromodulation strategies, such as adaptive deep brain stimulation, which may dynamically change stimulation in response to predicted seizures in patients, provide additional therapeutic options. This review emphasizes the potential of integrating biological rhythm analysis into personalized epilepsy management, offering novel approaches to optimize treatment and improve patient outcomes. Future research should focus on understanding individual variability in seizure rhythms and harnessing technological innovations to enhance seizure prediction, precision treatment, and long-term management.
Millets in India: exploring historical significance, cultural heritage and ethnic foods
Authors: Ankita  , Seth Upasna 
Published: January 2025

AbstractThis review paper offers a comprehensive exploration of the historical significance of millets in India, their role in preserving cultural heritage and embodiment in a diverse array of ethnic foods. In-depth online literature searches were conducted to assess the data, and the information was retrieved from official government reports, journals and books. The study explores the archaeological evidence and historical records of millet cultivation in India, highlighting their importance in Vedic era, ancient civilizations and Mughal rule. Studies showed a diversity of cultures in India and the importance of millets in religious ceremonies, festivals, literature, and folklore, showcasing their deep-rooted presence in Indian traditions. Further, the inclusion of millets in various ethnic dishes of different states demonstrates the diverse culinary applications of millets in India. Recent processing technologies for millet need to be studied for producing various millet-based food products. Additionally, the paper briefly discusses the challenges of millet consumption and promotion in India along with its future prospects. The study suggests that promoting millets and reviving traditional millet-based ethnic food and cultural practices can help preserve India’s rich heritage.
Out of place: thoracic splenosis—a case report
Authors: Becherano Gaston , Podgaetz Eitan , Ahmed Shair , Ringenberg Sigrid , Schwartz Gary 
Published: January 2025

Abstract
Background
Thoracic splenosis is a rare condition where there is ectopic autotransplantation of splenic tissue, typically after traumatic injury of the spleen or splenectomy with or without history of diaphragmatic injury. This diagnosis is noteworthy, as fewer than 100 cases have been documented since its initial description in 1896. Additionally, the patient’s oncological background further complicated the diagnostic process.

Case presentation
We present a case of an asymptomatic 37-year-old male with a past medical history of a right testicular seminoma and a surgical history of splenectomy and left pneumothorax due to a motorcycle accident 18 years prior. During oncological surveillance, a computed tomography (CT) scan of the chest revealed a mediastinal mass, which raised concern for metastatic seminoma or a primary tumor. Surgical removal via a robotic-assisted thoracoscopic approach demonstrated thoracic splenosis.

Conclusions
Thoracic splenosis should be considered as a differential diagnosis of mediastinal masses in patients with history of traumatic spleen injury or splenectomy. Surgical resection may be necessary in oncological cases to rule out metastasis.
PSSR2: a user-friendly Python package for democratizing deep learning-based point-scanning super-resolution microscopy
Authors: Stites Hayden C. , Manor Uri 
Published: January 2025

Abstract
Background
To address the limitations of large-scale high quality microscopy image acquisition, PSSR (Point-Scanning Super-Resolution) was introduced to enhance easily acquired low quality microscopy data to a higher quality using deep learning-based methods. However, while PSSR was released as open-source, it was difficult for users to implement into their workflows due to an outdated codebase, limiting its usage by prospective users. Additionally, while the data enhancements provided by PSSR were significant, there was still potential for further improvement.

Methods
To overcome this, we introduce PSSR2, a redesigned implementation of PSSR workflows and methods built to put state-of-the-art technology into the hands of the general microscopy and biology research community. PSSR2 enables user-friendly implementation of super-resolution workflows for simultaneous super-resolution and denoising of undersampled microscopy data, especially through its integrated Command Line Interface and Napari plugin. PSSR2 improves and expands upon previously established PSSR algorithms, mainly through improvements in the semi-synthetic data generation (“crappification”) and training processes.

Results
In benchmarking PSSR2 on a test dataset of paired high and low resolution electron microscopy images, PSSR2 super-resolves high-resolution images from low-resolution images to a significantly higher accuracy than PSSR. The super-resolved images are also more visually representative of real-world high-resolution images.

Discussion
The improvements in PSSR2, in providing higher quality images, should improve the performance of downstream analyses. We note that for accurate super-resolution, PSSR2 models should only be applied to super-resolve data sufficiently similar to training data and should be validated against real-world ground truth data.
Explainable Machine Learning to Predict Treatment Response in Advanced Non–Small Cell Lung Cancer
Authors: Ahluwalia Vinayak S. , Parikh Ravi B. 
Published: January 2025

PURPOSE
Immune checkpoint inhibitors (ICIs) have demonstrated promise in the treatment of various cancers. Single-drug ICI therapy (immuno-oncology [IO] monotherapy) that targets PD-L1 is the standard of care in patients with advanced non–small cell lung cancer (NSCLC) with PD-L1 expression ≥50%. We sought to find out if a machine learning (ML) algorithm can perform better as a predictive biomarker than PD-L1 alone.

METHODS
Using a real-world, nationwide electronic health record–derived deidentified database of 38,048 patients with advanced NSCLC, we trained binary prediction algorithms to predict likelihood of 12-month progression-free survival (PFS; 12-month PFS) and 12-month overall survival (OS; 12-month OS) from initiation of first-line therapy. We evaluated the algorithms by calculating the AUC on the test set. We plotted Kaplan-Meier curves and fit Cox survival models comparing survival between patients who were classified as low-risk (LR) for 12-month disease progression or 12-month mortality versus those classified as high-risk.

RESULTS

The ML algorithms achieved an AUC of 0.701 (95% CI, 0.689 to 0.714) and 0.718 (95% CI, 0.707 to 0.730) for 12-month PFS and 12-month OS, respectively. Patients in the LR group had lower 12-month disease progression (hazard ratio [HR], 0.47 [95% CI, 0.45 to 0.50];
P
< .001) and 12-month all-cause mortality (HR, 0.31 [95% CI, 0.29 to 0.34];
P
< .0001) compared with the high-risk group. Patients deemed LR for disease progression and mortality on IO monotherapy were less likely to progress (HR, 0.53 [95% CI, 0.46 to 0.61];
P
< .0001) or die (HR, 0.30 [95% CI, 0.24 to 0.37];
P
< .001) compared with the high-risk group.

CONCLUSION
An ML algorithm can more accurately predict response to first-line therapy, including IO monotherapy, in patients with advanced NSCLC, compared with PD-L1 alone. ML may better aid clinical decision making in oncology than a single biomarker.
Role of Social Media for Medical Oncologists and Medical Oncology Fellows (SMARTY): An Italian Cross-Sectional Study
Authors: Battaiotto Elena , Valenza Carmine , Garutti Mattia , Molendini Luigi Orlando , Bellio Elena , Trapani Dario , Puglisi Fabio , Pravettoni Gabriella , Buccoliero Luca , Curigliano Giuseppe , Mazza Manuelita 
Published: January 2025

PURPOSE
The use of social media is transforming physician-patient communication, mainly in the field of medical oncology. The pattern of social media use by medical oncologists is poorly studied. Therefore, we developed a survey to understand the preferences, experiences, opinions, and expectations of Italian medical oncologists and oncology fellows regarding the use of social media in cancer medicine to identify the different profiles of social media users.

MATERIALS AND METHODS
This multicentric, cross-sectional, observational study included oncologists or oncology fellows from Italy, who were surveyed from July to December 2023 on their use of social media. Data were analyzed through K-means clustering, and the Hartigan-Wong algorithm was applied to identify different profiles of social media users among the participants.

RESULTS

Of the 245 participants who accepted the invitation, 116 completed the entire survey and were included in the cluster analysis. Three profiles of social media users were identified through clustering: the highly social, the social skeptic, and the moderately social, accounting for 31%, 31%, and 38% of the participants, respectively. In general, older age (
P
= .0001), being a specialized oncologist (
P
= .003), and a higher mean time spent on social media (
P
= .0001) were associated with a greater consideration of the professional use of social media.

CONCLUSION
The use of social media among medical oncologists and oncology fellows represents a spectrum ranging from the social skeptic user to the highly social. Age, professional status (specialist or fellow), and frequency on social media use were associated with different patterns, opinions, and behaviors related to social media use.
Comparative Analysis of Intracranial Response Assessment Criteria in Patients With Melanoma Brain Metastases Treated With Combination Nivolumab + Ipilimumab in CheckMate 204
Authors: Huang Raymond Y. , Youssef Gilbert , Nelson Thomas , Wen Patrick Y. , Forsyth Peter , Hodi F. Stephen , Margolin Kim , Algazi Alain P. , Hamid Omid , Lao Christopher D. , Ernstoff Marc S. , Moschos Stergios J. , Atkins Michael B. , Postow Michael A. , Reardon David A. , Grootendorst Diederik J. , Leung David , Askelson Margarita , Ritchings Corey , Tawbi Hussein A. 
Published: January 2025

PURPOSE
In CheckMate 204, nivolumab + ipilimumab showed high intracranial (IC) objective response rates (icORRs) in patients with melanoma brain metastases (MBMs). Using icORR as a surrogate for overall survival (OS) has prompted use of alternate response criteria. To set the stage for harmonized MBM trials, the aim of this exploratory analysis was to determine icORR using several response criteria and examine correlations of response with survival.

METHODS
Patients (N = 119) with ≥one unirradiated MBMs received nivolumab + ipilimumab every 3 weeks (four doses), followed by nivolumab every 2 weeks for ≤24 months. Blinded review icORR was assessed with modified RECIST (mRECIST), Response Assessment in Neuro-Oncology Brain Metastases (RANO-BM; 5 mm and 10 mm cutoffs), and volumetric criteria (5 mm and 10 mm). Using a 6-week response landmark, IC progression-free survival (icPFS) and OS were compared for responders versus nonresponders.

RESULTS
icORR was higher with mRECIST and volumetric criteria than with RANO-BM or RECIST. mRECIST and volumetric response also showed stronger correlations with icPFS and OS. mRECIST responders who were RANO-BM 5 mm nonresponders (n = 14) had similar OS to RANO-BM 5 mm responders (n = 41). Clinical deterioration affected RANO-BM icORR; however, when assessed only radiographically without deterioration, RANO-BM 5 mm performed similarly to mRECIST. Among 41 patients with target lesions all <10 mm, responder icPFS and OS were similar to those of responders in the total population, indicating that response could be accurately determined in these patients.

CONCLUSION
This analysis supports mRECIST or radiographic-only RANO-BM 5 mm as reliable assessment scales in MBM trials. Volumetric response correlated with survival, supporting its application in future trials. Response could be accurately determined in patients with MBMs all <10 mm, supporting the inclusion of patients with MBMs ≥5 mm in future trials.