bluuebunny/crossref_metadata_2025 huggingface dataset:
Figure S6 from Mapping of the T-cell Landscape of Biliary Tract Cancer Unravels Anatomic Subtype-Specific Heterogeneity
Authors: Nie Jianhua , Zhang Shuyuan , Guo Ying , Liu Caiqi , Shi Jiaqi , Wu Haotian , Na Ruisi , Liang Yingjian , Yu Shan , Quan Fei , Liu Kun , Li Mingwei , Zhou Meng , Zhao Ying , Li Xuehan , Luo Shengnan , Zhang Qian , Wang Guangyu , Zhang Yanqiao , Yao Yuanfei , Xiao Yun , Tai Sheng , Zheng Tongsen
Published: February 2025

The transcriptome differences of T cells from adjacent normal tissues among different anatomical subtypes of BTC.
Figure S5 from Mapping of the T-cell Landscape of Biliary Tract Cancer Unravels Anatomic Subtype-Specific Heterogeneity
Authors: Nie Jianhua , Zhang Shuyuan , Guo Ying , Liu Caiqi , Shi Jiaqi , Wu Haotian , Na Ruisi , Liang Yingjian , Yu Shan , Quan Fei , Liu Kun , Li Mingwei , Zhou Meng , Zhao Ying , Li Xuehan , Luo Shengnan , Zhang Qian , Wang Guangyu , Zhang Yanqiao , Yao Yuanfei , Xiao Yun , Tai Sheng , Zheng Tongsen
Published: February 2025

The transcriptome differences of TIL-T among different anatomical subtypes of BTC.
Figure S2 from Mapping of the T-cell Landscape of Biliary Tract Cancer Unravels Anatomic Subtype-Specific Heterogeneity
Authors: Nie Jianhua , Zhang Shuyuan , Guo Ying , Liu Caiqi , Shi Jiaqi , Wu Haotian , Na Ruisi , Liang Yingjian , Yu Shan , Quan Fei , Liu Kun , Li Mingwei , Zhou Meng , Zhao Ying , Li Xuehan , Luo Shengnan , Zhang Qian , Wang Guangyu , Zhang Yanqiao , Yao Yuanfei , Xiao Yun , Tai Sheng , Zheng Tongsen
Published: February 2025

The feature plots of representative genes in T cells in BTC.
Supplementary Figure S3 from Complete Remissions of HER2-Positive Trastuzumab-Resistant Xenografts Using a Potent [225Ac]Ac-Labeled Anti-HER2 Antibody–Drug Radioconjugate
Authors: Pougoue Ketchemen Jessica , Njotu Fabrice Ngoh , Babeker Hanan , Monzer Alissar , Nwangele Emmanuel , Tikum Anjong Florence , Henning Nikita , Hassani Nava , Frye Sarah , Perron Randy , Byrne Chris , Didychuk Candice , Qi Qi , Bannister Laura , Doroudi Alireza , Fonge Humphrey
Published: February 2025

Supplementary Fig. S3: Sodium dodecyl-sulfate polyacrylamide gel electrophoresis (SDS-PAGE) in non-reduced conditions for trastuzumab and its conjugates showing good chemical purity and structural integrity of the antibody.
Supplementary Table S5 from Genomic Profiling Reveals SMARCA4 Mutations Are Associated with Shorter Overall and Intracranial Progression-Free Survival in Patients with Melanoma Brain Metastases
Authors: Fischer Grant M. , Lamba Nayan , Vogelzang Jayne , Aizer Ayal , Ligon Keith L.
Published: February 2025

Supplementary Table S5. Extracranial metastasis samples from patients who never developed brain metastases and survival information from the time of first metastasis.
Supplemental Table 4 from Duvelisib with Docetaxel for Patients with Anti–PD-1 Refractory, Recurrent, or Metastatic Head and Neck Squamous Cell Carcinoma
Authors: Hanna Glenn J. , Oakley L. B. , Shi R. , ONeill A. , Shin K. Y. , Scarfo N. , Sehgal K. , Dennis M. J. , Quinn N. , Jo V. Y. , Wong K. , Shvyrkova A. , Kushnarev V. , Shanthappa B. U. , Tkachuk A. , Kryukov K. , Sarachakov A. , Svekolkin V. , Lennerz J. , Waters S. , Haddad R. I.
Published: February 2025

Supplemental Table 4. Univariate odds ratio and 95% CI for logistic regression models for predictors of response.
Supplemental Table 3 from Duvelisib with Docetaxel for Patients with Anti–PD-1 Refractory, Recurrent, or Metastatic Head and Neck Squamous Cell Carcinoma
Authors: Hanna Glenn J. , Oakley L. B. , Shi R. , ONeill A. , Shin K. Y. , Scarfo N. , Sehgal K. , Dennis M. J. , Quinn N. , Jo V. Y. , Wong K. , Shvyrkova A. , Kushnarev V. , Shanthappa B. U. , Tkachuk A. , Kryukov K. , Sarachakov A. , Svekolkin V. , Lennerz J. , Waters S. , Haddad R. I.
Published: February 2025

Supplemental Table 3. Reasons for Treatment Discontinuation.
Supplementary Table S2 from Smoking-Associated Carcinogen–Induced Inflammation Promotes Lung Carcinogenesis via IRAK4 Activation
Authors: Aggarwal Ritesh K. , Sidoli Simone , Wang Jingli , Sahu Srabani , Sanawar Rahul , Gupta Varun , Aluri Srinivas , Sukrithan Vineeth , Vegivinti Charan T.R. , Zavras Phaedon D. , Verma Divij , Gordon-Mitchell Shanisha , Agarwal Beamon , Verma Tanya , Starczynowski Daniel T. , Steidl Ulrich G. , Shastri Aditi , Halmos Balazs , LaFave Lindsay M. , Cheng Haiying , Verma Amit , Zou Yiyu
Published: February 2025

Supplementary Table S2. Manuscript Figure 2 data - The Excel file has data points for easy access to infer numerical values for data presented in the manuscript. Also see Data Availability Statement.
Supplementary Figure S2 from Next-Generation Sequencing–Based MSI Scoring Predicts Benefit in Mismatch Repair–Deficient Tumors Treated with Nivolumab: Follow-up on NCI-MATCH Arm Z1D
Authors: Schoenfeld Jonathan D. , Azad Nilofer S. , Gross Jacob , Chen Li , Overman Michael J. , Kao Katrina , Jackson Latifa , Brunnquell Donna , Bu Xiangning , Coppola Christina , Guan Ping , Lee Jennifer , Sims David , Fuchs Rebecca , Weirather Jason L. , Pfaff Kathleen L. , Gunasti Lauren , Ranasinghe Srin , Hamilton Stanley R. , Wang Victoria , O’Dwyer Peter J. , Wu Catherine J. , Rodig Scott J. , Patton David R. , Harris Lyndsay
Published: February 2025

Comparisons of clinical outcomes, MSIsensor-pro scores, and tumor mutational burden (TMB)
Supplementary Data 1,4 from Circulating Tumor DNA Dynamics and Clinical Outcome in Metastatic Colorectal Cancer Patients Undergoing Front-Line Chemotherapy
Authors: Ghidini Michele , Hahne Jens Claus , Senti Chiara , Heide Timon , Proszek Paula Z. , Shaikh Ridwan , Carter Paul , Hubank Mike , Trevisani Francesco , Garrone Ornella , Cappelletti Maria Rosa , Generali Daniele , Cattaneo Monica , Gnocchi Nicoletta , Donati Gianvito , Gobbi Angela , Grizzi Giulia , Lampis Andrea , Elghadi Raghad , Tanzi Giulia , Tomasello Gianluca , Ratti Margherita , Pinato David J. , Fassan Matteo , Vlachogiannis Georgios , Sottoriva Andrea , Cortellini Alessio , Passalacqua Rodolfo , Valeri Nicola
Published: February 2025

Supp Tables
Supplemental Figure 5 from Phase I/II Trial of Exportin 1 Inhibitor Selinexor plus Docetaxel in Previously Treated, Advanced KRAS-Mutant Non–Small Cell Lung Cancer
Authors: von Itzstein Mitchell S. , Burns Timothy F. , Dowell Jonathan E. , Horn Leora , Camidge D. Ross , York Sally J. , Eaton Keith D. , Kyle Kelly , Fattah Farjana , Liu Jialiang , Mu-Mosley Hong , Gupta Arjun , Nadeem Urooba , Gao Ang , Zhang Song , Gerber David E.
Published: February 2025

Supplemental Figure 5. Sample radiographic responses (n=3, all TP53 wild type).
Table S5 from Multiomics Reveals the Immunologic Features and the Immune Checkpoint Blockade Potential of Colorectal Medullary Carcinoma
Authors: Liu Chao , Zou Haoyi , Ruan Yuli , Fang Lin , Wang Bojun , Cui Luying , Wu Tong , Chen Zhuo , Dang Tianjiao , Lan Ya , Zhao Wenyuan , Zhang Chunhui , Meng Hongxue , Zhang Yanqiao
Published: February 2025

Supplementary Table 5. Group information for TCR-sequencing
Table S1 from Multiomics Reveals the Immunologic Features and the Immune Checkpoint Blockade Potential of Colorectal Medullary Carcinoma
Authors: Liu Chao , Zou Haoyi , Ruan Yuli , Fang Lin , Wang Bojun , Cui Luying , Wu Tong , Chen Zhuo , Dang Tianjiao , Lan Ya , Zhao Wenyuan , Zhang Chunhui , Meng Hongxue , Zhang Yanqiao
Published: February 2025

Supplementary Table 1. Total of 1257 samples for IHC and clinical information.
Figure S7 from Multiomics Reveals the Immunologic Features and the Immune Checkpoint Blockade Potential of Colorectal Medullary Carcinoma
Authors: Liu Chao , Zou Haoyi , Ruan Yuli , Fang Lin , Wang Bojun , Cui Luying , Wu Tong , Chen Zhuo , Dang Tianjiao , Lan Ya , Zhao Wenyuan , Zhang Chunhui , Meng Hongxue , Zhang Yanqiao
Published: February 2025

sFigure7. Distribution of POLE Functional Mutations and the influence of MSI status on survival.
Figure S4 from Multiomics Reveals the Immunologic Features and the Immune Checkpoint Blockade Potential of Colorectal Medullary Carcinoma
Authors: Liu Chao , Zou Haoyi , Ruan Yuli , Fang Lin , Wang Bojun , Cui Luying , Wu Tong , Chen Zhuo , Dang Tianjiao , Lan Ya , Zhao Wenyuan , Zhang Chunhui , Meng Hongxue , Zhang Yanqiao
Published: February 2025

sFigure4. Tumor-infiltrating lymphocytes analysis results.
Supplementary Figure S6 from Complete Remissions of HER2-Positive Trastuzumab-Resistant Xenografts Using a Potent [225Ac]Ac-Labeled Anti-HER2 Antibody–Drug Radioconjugate
Authors: Pougoue Ketchemen Jessica , Njotu Fabrice Ngoh , Babeker Hanan , Monzer Alissar , Nwangele Emmanuel , Tikum Anjong Florence , Henning Nikita , Hassani Nava , Frye Sarah , Perron Randy , Byrne Chris , Didychuk Candice , Qi Qi , Bannister Laura , Doroudi Alireza , Fonge Humphrey
Published: February 2025

Supplementary Fig. S6: Instant thin layer chromatography (left) and radio-SEC-HPLC (right) of [225Ac]Ac-Macropa-T-PEG6-DM1. The radio-SEC-HPLC was obtained by collecting fractions of the ADR at different time points and running them using the gamma counter. The results were plotted on excel.
Supplemental Figure 3 from Dynamic Tracking of Tumor Microenvironment Modulation Using Kaede Photoconvertible Transgenic Mice Unveils New Biological Properties of Viral Immunotherapy
Authors: Diers Anne R. , Guo Qiuchen , Li Zhi , Richardson Erin , Idris Suaad , Willis Claire , Tak Paul P. , Withers David R. , Barone Francesca
Published: February 2025

Supplemental Figure 3 shows the combined effect of CAN-2409 and anti-CTLA-4 Ab treatment on individual tumor growth curves and immune cell quantification in tumor tissue
Supplemental Figure 2 from Dynamic Tracking of Tumor Microenvironment Modulation Using Kaede Photoconvertible Transgenic Mice Unveils New Biological Properties of Viral Immunotherapy
Authors: Diers Anne R. , Guo Qiuchen , Li Zhi , Richardson Erin , Idris Suaad , Willis Claire , Tak Paul P. , Withers David R. , Barone Francesca
Published: February 2025

Supplemental Figure 2 shows the tumor growth inhibition rate 4 days after treatment and immune cell quantification in tumor tissue in Kaede mice
Data from Dynamic Tracking of Tumor Microenvironment Modulation Using Kaede Photoconvertible Transgenic Mice Unveils New Biological Properties of Viral Immunotherapy
Authors: Diers Anne R. , Guo Qiuchen , Li Zhi , Richardson Erin , Idris Suaad , Willis Claire , Tak Paul P. , Withers David R. , Barone Francesca
Published: February 2025

Abstract
CAN-2409 is a replication-defective adenovirus that delivers the herpes simplex virus–thymidine kinase gene to infected cells. Intratumoral administration of CAN-2409, followed by prodrug, results in the formation of a toxic metabolite able to induce immunogenic cell death, exposure of tumor-associated antigens, and activation of local and systemic immune responses. We used a dynamic labeling model with MC38 tumor cells implanted in photoconvertible Kaede mice. Violet light was used to label the tumor microenvironment (TME), distinguishing retained versus newly entering cells and allowing real-time monitoring of immune compartment changes within tumors. Administration of CAN-2409 + prodrug led to control of tumor growth and significantly increased effector CD8+ T-cell responses. Photolabeling of the TME revealed that rather than enhancing recruitment of T cells to the tumor, CAN-2409 altered the TME whereby newly entering and retained CD8+ T cells were significantly more proliferative. CAN-2409 supported reinvigoration of tumor-associated antigen–specific CD8+ T cells and expansion of regulatory T cells of an altered phenotype. Moreover, the combination of CAN-2409 + prodrug and anti–CTLA-4 antibody treatment further improved control of tumor growth, in part by the enhanced CD8+ T-cell–mediated effector function and diminished regulatory T cell–mediated immunosuppression. Collectively, these data defined at least two temporally distinct pathways underpinning the mechanism of action of CAN-2409 that overcome cell exhaustion and decrease immune suppression. The results also support the rationale for future clinical trials of CAN-2409 treatment combined with anti–CTLA-4 antibody therapy.
Significance:
This study utilized a novel photoconvertible mouse tumor model to track immune cell trafficking upon treatment with an investigational viral immunotherapy (CAN-2409), revealing enhanced T-cell responses after viral immunotherapy associated with local proliferation of T cells within tumors that could further enhance antitumor efficacy in combination with immune checkpoint inhibitors. These findings define temporally and spatially distinct interactions of immune cells that could be harnessed by novel therapeutics.
Impact of the COVID-19 pandemic on psychosocial distress in adolescents with obesity compared to those with type1 diabetes: Results from the KICK-COVID Study in Germany
Authors: Weihrauch-Blüher Susann , Wiegand Susanna , Tittel Sascha , Greber-Platzer Susanne , Lanzinger Stefanie , Kamrath Clemens , Minden Kirsten , Sengler Claudia , Linke Sabine , Büssenschütt Antje , Reschke Felix , Göldel Julia , Warschburger Petra , Holl Reinhard W.
Published: February 2025

Introduction: The aim of this study is to investigate the impact of the COVID-19 pandemic on psychosocial well-being in adolescents with obesity compared to those with type 1 diabetes.
Methods: As part of the German KICK-COVID Study, adolescents aged 12-21 with overweight or obesity from the German/Austrian Adiposity-Follow-up-Registry (APV) completed well-being, anxiety, and depression questionnaires (WHO-5; GAD-7; PHQ-9) during routine visits amidst the COVID-19 pandemic. By multivariable linear regression models, adjusted for age, gender, and immigration background, the association between psychosocial distress, anthropometrics, and cardiometabolic risk factors was analysed. Data were compared to those of youth with type 1 diabetes from the German/Austrian Diabetes-Follow-up-Registry (DPV) and normative values from the general population. Additionally, a mediation analysis examined the impact of loneliness on mental health through media consumption.
Results: From June 2021 to September 2023, 235 adolescents from 6 German and 1 Austrian paediatric obesity centres were enrolled. Results were compared to 235 age- and gender-matched participants from the DPV registry (54.04% males; mean age 15.21±1.66 years) and normative values. Youth with type-1 diabetes were more anxious about their health risk, but distress factors were more pronounced in the APV group (p<0.001). Girls from the APV group showed higher mental distress than boys across all applied questionnaires, but not for age, BMI-SDS, and migration background as predictors. Perception of loneliness correlated with poorer mental health outcomes, but it was not associated to media consumption. Comparisons with normative values revealed significantly higher depression and anxiety scores (p<0.001) and lower well-being scores in the APV group (p<0.01).
Conclusions: Youth with obesity and diabetes experienced significant psychosocial distress during the COVID-19 pandemic. Disease-specific differences were observed on the level of single items: Adolescents with type 1 diabetes expressed heightened concern about their health risks, while those with obesity reported lower self-esteem, increased suicidal thoughts, and fluctuating appetite. Female gender appeared to pose an additional risk factor. Media consumption was notably higher in the APV cohort. Healthcare providers should be vigilant regarding psychological comorbidities in youth with chronic conditions, particularly during periods of heightened stress.
Biomarkers for Predicting of Sepsis-Induced Cardiorenal Syndrome in Emergency Settings
Authors: Pei Yuanyuan , Guo Liping , Zhou Guangping , Cao Lingjie , Huang Wenfeng , Yang Fengtao , Li Dilu , Chi Cheng , Zhu Jihong
Published: February 2025

Introduction: Cardiorenal syndrome (CRS) is a common and critical complication of sepsis, with high morbidity and mortality rates. Studies on biomarkers for the early prediction of septic CRS are sporadic. Classic and novel potential biomarkers were identified to explore their diagnostic performance of in patients with septic CRS.
Methods: A total of 138 patients with sepsis from Peking University People’s Hospital were enrolled in this prospective observational study, which was conducted between May 2019 and June 2022. The patients were divided into non-CRS(n=106) and CRS (n=32) groups. Serum levels of cystatin C, KIM-1, NGAL and α-Klotho were detected at admission using enzyme-linked immunosorbent assay (ELISA). The relationship between the biomarker levels and risk factors of CRS were analyzed, as well as discrimination accuracy comparisons were performed.
Results: The incidence of CRS in patients with sepsis was 23.2% (32/138) during hospitalization, with an obvious mortality. Compared with the non-CRS group, serum cystatin C, BNP, TNI, KIM-1, and NGAL levels were both significantly elevated at admission in patients with sepsis complicated with CRS. Logistic regression analysis revealed that BNP, TNI cystatin C albumin, Lac, D-dimer were risk factors for CRS in sepsis patients. Compared with other biomarkers, serum cystatin C had moderate discriminative power for predicting septic CRS (AUROC 0.746, sensitivity 0.719, specificity 0.783). BNP combined with cystatin C and D-dimer demonstrated an excellent discrimination performance, for its AUROC was up to 0.878 (sensitivity, 0.844; specificity, 0.759).
Conclusion: Serum cystatin C, BNP, TNI, KIM-1, and NGAL levels are elevated in patients with septic CRS. Our study provides reliable evidence that cystatin C in combination with BNP and D-dimer might better predict septic CRS upon admission. Further research on sensitive biomarkers is needed.
Anti-Vascular Endothelial Growth Factor Treatment Outcomes in Macular Telangiectasia: A Systematic Review
Authors: Sriranganathan Aswen , Grad Justin , Mihalache Andrew , Popovic Marko M. , Kertes Peter J. , Kohly Radha , Muni Rajeev H.
Published: February 2025

Purpose: To assess clinical outcomes of anti-vascular endothelial growth factor agents (anti-VEGF) for patients with Macular Telangiectasia (MacTel).
Methods: A systematic literature search on Ovid MEDLINE, Embase, and Cochrane Library was performed from inception to June 2024 for comparative studies on anti-VEGF agents in MacTel. The primary outcomes were the change in best-corrected visual acuity (BCVA). Secondary outcomes were central macular thickness (CMT), central choroidal thickness (CCT), and fluorescein angiography (FA) leakage.
Results: Ten studies on 377 eyes of 239 patients followed up over 23.4 ± 8.3 months were included. Mean (SD) BCVA changed from 0.42 (0.39) to 0.35 (0.18) over 23.4 (8.3) months of follow up in non-proliferative MacTel. Mean BCVA changed from 0.66 (0.43) to 0.52 (0.34) at final follow-up in eyes with choroidal neovascularization (CNV). Five studies reported improved visual acuity, one showed improved FA leakage without visual acuity benefit, and four found no functional benefit. In non-proliferative MacTel, four studies showed no functional improvement, two reported significant functional and morphological improvements, and one suggested potential benefits in improving BCVA. In proliferative MacTel, two studies demonstrated improvement in both anatomical and functional outcomes, while one indicated that anti-VEGF treatment might produce improved results. In non-proliferative MacTel, mean CMT changed from 201 (32) µm to 199 (29) µm. CMT in patients with CNV decreased from an initial of 328.23 (161.16) µm to 267.44 (118.56) µm at final follow up. CCT in proliferative MacTel eyes decreased from 272.37 (52.65) µm and 247.40 (48.80) µm on anti-VEGF therapy. Overall, FA leakage outcomes were improved on ranibizumab therapy. No study documented any significant adverse effects with treatment.
Conclusion: Anti-VEGF agents may be associated with favourable anatomical and functional outcomes, particularly in proliferative MacTel, however, future large-scale clinical trials are warranted.
Surveillance of pathogens causing viral diarrhoea in Yichang in 2022 and 2023
Authors: Su Zhengyuan , Li Jing , Moming Abulimiti , Qian Jin , Zhang Danna , Zhou Kangping , Liu Yingle , Liu Bailin , Cai Kun , Yang Juan
Published: February 2025

Introduction: This study compared the pattern of viral diarrhoea in Yichang City, China in 2022 and 2023, before and after the lifting of the COVID-19 restrictions. Methods: Stool samples were collected from outpatients and inpatients with diarrhoea at three hospitals in Yichang from January to October 2022 and January to June 2023, before and after the lifting of COVID-19 restrictions, respectively. Samples were simultaneously tested for 13 types of enteric virus using a rapid multiplex assay that could simultaneously detect 13 types of five enteric viruses, including rotavirus (groups A, B, C and H), norovirus (I, II, IV, VII, VIII, and IX), adenovirus, sapovirus, and astrovirus. Results: Testing of 458 samples showed variations in pathogen distribution by age group. Specifically, there was an increase in the number of viral infections among adults, a decrease among children, an increase in co-infection rates, and variability in virus positivity in 2023 compared to 2022. Conclusions: The multiplex assay method improved diagnostic efficiency and provided epidemiological insights. This study highlights the impact of public health transitions on viral diarrhoea epidemiology, underscoring the need for ongoing surveillance and adaptable strategies in the post-COVID-19 pandemic era.
Acute Myeloid Leukemia with Core Binding Factor Rearrangements: A 10-Year Cancer Center Experience
Authors: Fareed Shehab , Soliman Dina Sameh , Al-Mashdali Abdulrahman F. , Gameil Amna , Mulikandathil Yahya , Alshurafa Awni , Abdalla ElMustafa , Fadul Abdallah , Aldali Sarah , Mudawi Deena , Singh Kaplana , Ibrahim Feryal , Hamad Anas , Cherif Honar , Yassin Mohamed
Published: February 2025

Background: Core-binding factor acute myeloid leukemia (CBF-AML) is characterized by t(8;21) or inv(16)/t(16;16) chromosomal rearrangements. Methods: In this retrospective study of 71 CBF-AML cases from Qatar's National Center for Cancer Care & Research (2013-2022), we analyzed clinicopathological characteristics and survival outcomes. Results: The cohort was predominantly male (76%) with a median age of 40 years, and 96% were de novo AML. The t(8;21) translocation was more frequent (69%) than inv(16)/t(16;16). Patients under 40 years showed higher white blood cell counts and blast percentages. Standard "3+7" induction chemotherapy (used in 69% of cases) achieved remission in 67% of patients. Median overall survival was 81% at 37 months, with median progression-free survival of 34 months. No significant survival differences were observed based on FLT3-ITD status, translocation type, complex karyotype, KIT mutation status, or allogeneic stem cell transplantation (performed in 19% of patients), though patients under 60 years demonstrated better survival outcomes. Conclusions: This study highlights CBF-AML heterogeneity and challenges established prognostic markers, suggesting a need for risk stratification reassessment, treatment strategy optimization, ELN guidelines implementation, and continuous molecular monitoring.
Subclinical inflammation and renal allograft dysfunction: myth or reality?
Authors: Couceiro Carlos , Visent Maria , Cruzado Josep M.
Published: February 2025

Since the implementation of the Banff classification, the diagnosis and treatment of transplant rejection has been standardized. However, the rigid categorization of transplant pathology has limited our perspective on allograft inflammation, particularly disregarding those inflammatory infiltrates who do not reach the category of rejection. The term subclinical inflammation was introduced to designate the inflammation found in protocol biopsies, without significant renal function deterioration. Following the introduction of modern immunosuppression with tacrolimus and mycophenolate, subclinical rejection rate decreased, and less attention was paid to this entity. However, in the last decades several studies have evaluated the impact of lower levels of inflammation and demonstrated its negative consequences on long-term outcomes. Although, in some patients this subclinical inflammation is not permanent and can spontaneously disappear. The uncomplete definition of subclinical inflammation, which only considered renal function stability, and the evaluation of the biopsy as a definitive diagnosis, and not as a picture of an evolving process are the main reasons why managing this inflammation represents a challenge, especially when there is no pathogenic mechanism identified. In this review, we revise the “natural” history of inflammation in the kidney allograft and its possible origins based on cellular composition and transcriptomic expression changes in kidney biopsies. In addition, we propose an updated definition and an approach to manage it.
Adaptive silicon synapse and CMOS neuron for neuromorphic VLSI computing
Authors: El-Khatib Ziad , Moussa Sherif , Kamalov Firuz , Yagoub Mustapha C. E.
Published: February 2025

The design of a fully integrated adaptive modified complementary metal-oxide-semiconductor (CMOS) synapse circuit is presented. By using multiple-gated transistor configuration in the modified CMOS synapse an additional branch provide control where the synaptic output current time-constant is tuned. The effect of changing the multiple-gated transistor bias voltage from 0.25 to 0.45 V tunes the spiking output current exponential time-constant range by 200 ms as shown in simulation results. Moreover, a fully-integrated adaptive quadratic integrate-and-fire (QIF) CMOS neuron circuit is presented as well. A differential pair with variable capacitor integrator and a tunable schmitt trigger threshold detector circuit are integrated in the CMOS neuron that can be tuned varying its spiking frequency. The proposed adaptive quadratic integrate-and-fire (AQIF) neuron has the ability to adjust the spiking frequency without changing the input current. The simulation results show the proposed CMOS neuron circuit spiking frequency can be tuned from 58.4 to 312.5 Hz and its spiking period from 17.1 to 3.2 ms with tuning the bias voltage of variable capacitor integrator. Having a peak voltage Vpeak=0.95 V, a reset voltage Vreset=-0.75 V and a voltage threshold of 0.35 V with a membrane potential range of 1.5 V. The proposed CMOS neuron circuit is designed in 130 nm process with a supply voltage of 1.8 V and a total power dissipation of 1.8 mW.
Schedule-free optimization of the transformers-based time series forecasting model
Authors: Yemets Kyrylo , Greguš Michal
Published: February 2025

The task of time series forecasting is important for many scientific, technical, and applied fields, such as finance, economics, meteorology, medicine, transportation, and telecommunications. Existing methods, such as autoregressive models and moving average models, have their limitations, especially when working with non-stationary and seasonal data. In this work, the basic architecture of transformers was modified to solve time series forecasting problems. Additionally, state-of-the-art optimizers were investigated and experimentally compared, including AdamW, stochastic gradient descent (SGD), and new methods such as schedule-free SGD and schedule-free AdamW, to improve forecasting accuracy and the efficiency of the training procedure for the transformer architecture. Modeling was conducted on meteorological data that included seasonal time series. The accuracy evaluation of the optimization methods studied in this work was performed using a range of different performance indicators. The results showed that the new optimization methods significantly improve forecasting accuracy compared to the use of traditional optimizers.
Unveiling precision: Eye cancer detection redefined with particle swarm optimization and genetic algorithms
Authors: Narwadkar Sanved , Mehta Pradnya Samit , Patil Rutuja Rajendra , Kadam Kalyani , Bidve Vijaykumar
Published: February 2025

Eye cancer detection is rare. The study introduces a holistic swarm intelligence method for the timely identification and categorization of three significant eye disorders: glaucoma, diabetic retinopathy, and cataract. Glaucoma is distinguished by elevated pressure within the eye and harm to the optic nerve, potentially leading to permanent loss of vision. Diabetic patients experience diabetic retinopathy primarily due to the presence of high blood sugar levels. The early detection and classification of cataracts can be achieved by combining swarm intelligence algorithms such as particle swarm optimization (PSO) and genetic algorithms (GA). In the case of diabetic retinopathy diagnosis, swarm intelligence is employed to optimize the parameters of deep learning models, thereby enhancing the accuracy of lesion segmentation and classification. Cataract detection used to improve the evaluation of lens opacity and cloudiness, providing a robust diagnostic mechanism. The accuracy obtained with a PSO is 85.79%, F1 score 83.45%, and recall 82.43%. The accuracy obtained with a GA is 82.10%, F1 score 81.16%, and recall 81.51%. The comparison of GA, convolution neural network, and PSO algorithms proves that the accuracy to detect the eye cancer is achieved with PSO and GA algorithm.
Spectral efficient network and resource selection model in 5G networks
Authors: B. G. Padmageetha , Kumar Naik Pramod , Patil Mallanagouda
Published: February 2025

This work addresses the challenges in modern communication networks, emphasizing the need for improved efficiency, higher data transfer rates, and reduced delays. In 5G networks, advanced resource optimization, network selection, and relaying techniques are crucial for expanding multi-cellular coverage and enhancing network performance. However, implementing these techniques in mobile environments with high interference levels increases computational demands for radio resource management (RRM). Machine learning (ML) and deep learning (DL) are proposed as solutions to enhance consumer applications, reduce communication overhead, and improve RRM. Current ML/DL methods, however, struggle with identifying key features for network selection and balancing system throughput with spectral efficiency. This paper introduces the spectral efficient network and resource selection (SENRS) model for 5G multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) networks. Tested using the Stanford University Interim (SUI) channel fading model in a highway scenario, the SENRS model demonstrates superior performance compared to existing network and resource selection systems.
Deep learning-based classifier for geometric dimensioning and tolerancing symbols
Authors: Bewoor Laxmi , Bewoor Anand , P. Hujare Pravin , Rathod Praveen , Yetekar Vedant , Dollin Shrish
Published: February 2025

This research investigates the recognition of geometric dimensioning and tolerancing (GD&T) symbols using a deep learning model for object detection. GD&T, playing a pivotal role in engineering and manufacturing, provides essential specifications for product design and production. Manual processes for GD&T are often time-consuming and error prone. The study demonstrates outstanding accuracy in automating GD&T symbol recognition in engineering applications using YOLOv8. A carefully curated dataset, encompassing a wide range of GD&T symbols, was employed for training and evaluating the model. The YOLOv8 architecture, renowned for its robust performance, was meticulously fine-tuned to cater to the specific requirements of GD&T symbol detection. This research not only addresses the challenges in manual GD&T processes but also showcases practical implications for improved quality control and streamlined engineering workflows. By automating GD&T symbol recognition, this study contributes to the efficiency and precision crucial in the engineering and manufacturing domains.
Detecting road damage utilizing retinanet and mobilenet models on edge devices
Authors: Mahmudah Haniah , Aisjah Aulia Siti , Arifin Syamsul , Prastyanto Catur Arif
Published: February 2025

A particular form of road digitalization produces a system that detects road damage automatically and in real time, employing the device to detect road damage as an edge device. The application of RetinaNet152 and MobileNetV2 models for road damage detection on edge devices necessitates a trade-off between high system performance and efficiency. Currently, edge devices have limited storage. In this paper, we explore how tuning hyperparameters with batch size and several optimizers improves system performance on RetinaNet152 and MobileNet models, as well as how they are implemented on edge devices. After tuning hyperparameters in the batch size of the optimizer, the Adam optimizer displayed enhanced performance with mean average precision (mAP), average recall (AR), and F1-score. This implies a positive impact on overall model performance. The MobileNetV2 model's hyperparameter tuning technique significantly improves performance, resulting in faster inference times and overall system performance. This demonstrates that the MobileNetV2 model could be used directly on edge devices to identify road damage. However, the RetinaNet152 model has a lower inference time, which cannot be deployed directly to edge devices. The RetinaNet152 model can be deployed on edge devices; however, a technique for speeding up inference time is essential.
Enhancing fall detection and classification using Jarratt‐butterfly optimization algorithm with deep learning
Authors: Durga Bhavani Kakirala , Ukrit Melkias Ferni
Published: February 2025

Falls pose significant risk to the health and safety of individuals, specifically for vulnerable populations as the elderly and those with specific medical conditions. The repercussions of falls can be severe, leading to injuries, loss of independence, and increased healthcare costs. Consequently, the development of effective fall detection systems is crucial for providing timely assistance and enhancing the overall well-being of affected individuals. Recent advancements in deep learning (DL) have opened new avenues for automating fall detection through the analysis of sensor data and video footage. DL algorithms are especially well-suited for this task because they can automatically learn complex features and patterns from raw data, eliminating the need for extensive manual feature engineering. This article introduces a novel approach to fall detection and classification, termed the fall detection and classification using Jarratt‐butterfly optimization algorithm with deep learning (FDC-JBOADL) algorithm. The FDC-JBOADL technique employs a median filtering (MF) method to mitigate noise and utilizes the EfficientNet model for robust feature extraction, capturing both motion patterns and appearance characteristics of individuals. Furthermore, the classification of fall events is achieved through a long short-term memory (LSTM) classifier, with hyperparameter optimization facilitated by Jarratt‐butterfly optimization algorithm (JBOA). Through a comprehensive series of experiments, the efficacy of FDC-JBOADL technique is validated, demonstrating superior performance compared to existing methodologies in the domain of fall detection.
Multi-task deep learning for Vietnamese capitalization and punctuation recognition
Authors: Nguyen Phuong-Nhung , Thu-Hien Nguyen , Nguyen Truong-Thang , Thu-Nga Nguyen Thi , Anh-Phuong Nguyen Thi , Nguyen Tuan-Linh
Published: February 2025

Speech recognition is the process of converting the speech signal of a particular language into a sequence of corresponding content words in text format. The output text of automatic speech recognition (ASR) systems often lacks struc- ture, such as punctuation, capitalization of the first letter of a sentence, proper nouns, and names of locations. This absence of structure complicates compre- hension and restricts the utility of ASR-generated text in various applications, such as creating movie subtitles, generating transcripts for online meetings, and extracting customer information. Therefore, developing standardization solu- tions for the output text from ASR is necessary to improve the overall quality of ASR systems. In this article, we use the idea of multitask deep learning for the task of capitalization and punctuation recognition (CPR) for the output text of Vietnamese ASR, with the aim of the named entity recognition (NER) task as a supplement to help the CPR model perform better, and proposed to use text-to- speech (TTS) to create a dataset for CPR-NER multitask model training. The experiment results show that the multi-task deep learning model has improved CPR results by 6.2% of F1 score with ASR output and 7.1% on raw text.
A proposed approach for plagiarism detection in Myanmar Unicode text
Authors: Thurain Moe Sun , Mar Soe Khin , Than Nwe Than
Published: February 2025

Around the world, with technology that improves over time, almost everyone can access the internet easily and quickly. With the increase in the use of the internet, the plagiarism of information that is easily available on the internet has also increased. Such plagiarism seriously undermines originality and ethical principles. In order to prevent these incidents, there is plagiarism detection software for many countries and languages, but there is no plagiarism detection software for the Myanmar language yet. In an attempt to fill that gap, this study proposed a deep learning model with Rabin-Karp hash code and Word2vec model and built a plagiarism detection system. Our deep learning model was trained by randomly obtaining information from Myanmar Wikipedia. According to the experiments, our proposed model can effectively detect plagiarism of educational content and information from Myanmar Wikipedia. Moreover, it is possible to distinguish plagiarized texts by rearranging words or substituting words with some synonyms. This study contributes to a broader understanding of the complexities of plagiarism in the Myanmar academic area and highlights the importance of measures to effectively prevent plagiarism. It maintains the credibility of education and promotes a culture that values originality and intellectual integrity.
A contest of sentiment analysis: k-nearest neighbor versus neural network
Authors: Kurniawan Fachrul , Supriyatno Triyo
Published: February 2025

Discourse about public matters often encompasses sentences that address topics emerging within societal contexts, including issues related to Islamophobia. Debates surrounding this subject frequently evoke support and opposition within digital platforms and interpersonal interactions. Categorizing such dialogic expressions within online media facilitates an evaluation of their negative and positive implications. This study employs two distinct methodologies, specifically deep learning and machine learning techniques, to visualize the findings by implementing dual algorithms. According to the comparative analysis, deep learning achieves a higher accuracy rate of 78%, whereas machine learning achieves a rate of 71%. Thus, deep learning is a better method for textual data classification.
A novel approach to enhancing software quality assurance through early detection and prevention of software faults
Authors: Rai Deepti , Prashant Jyothi Arcot
Published: February 2025

The current manuscript presents a predictive mechanism towards analyzing software defects by developing a line-level fault prediction technique. Current methodologies rely on customized attributes and overlook the sophisticated structural and semantic characteristics inherent in programming languages. This oversight often led to suboptimal defect identification, as code defects are intricately scrambled with their contextual environment. Moreover, conventional software defect prediction (SDP) strategies, typically focusing on larger code units such as modules or classes, impede precise error localization. To address these challenges, this study proposes an automated scheme utilizing a recurrent neural network (RNN) with an attention layer to analyze line-level quantifiers within the code, such as the number of pairwise operations and single operand operators. The efficacy of this learning-driven scheme is validated through comprehensive experiments conducted on several C++ programs. The experimental results demonstrate a 95.8% recall, 83.12% precision, and 90.35% accuracy in identifying fault-prone lines within a testing dataset. These outcomes confirm the effectiveness of proposed SDP scheme in accurately identifying the defects and highlighting its inter-project capabilities, exhibiting the model's adaptability across different software projects.
Interactive communication human-robot interface for reduced mobility people assistance
Authors: Jiménez Moreno Robinson , Espitia Cubillos Anny Astrid , Rodríguez Carmona Esperanza
Published: February 2025

Communication between a robot and its user is essential for the execution of tasks, even more so in a scenario where the robot is designed to assist people with reduced mobility. This document presents the evaluation of a conversation script between a human user and a robot for assistance using pre-recorded responses, for this a methodology with three phases was proposed and applied: establishment of the training scheme of a convolutional network that allows recognize user's words for execution of tasks by the robot, generation of dialogue between the user and possible interactions with the assistive robot and finally, the measurement of perception of interface users. Results show a high level of accuracy with words selected to command the robot, using a convolutional neural network, with an audio input discriminated in its components mel frequency cepstral coefficients (MFCCs) and command sets of male and female voices. It was possible to establish a dialogue model with three scenes to recognize the residential environment, rename spaces and execute action commands to move elements. It is concluded the designed instrument is reliable and the perception of proposed interactive communication interface is good in terms of usability (effectiveness, efficiency, and user satisfaction).
An enhanced cascade ensemble method for big data analysis
Authors: Izonin Ivan , Muzyka Roman , Tkachenko Roman , Gregus Michal , Korzh Roman , Yemets Kyrylo
Published: February 2025

In the digital age, the proliferation of data presents both challenges and opportunities, particularly in the realm of big data, which is characterized by its volume, velocity, and variety. Machine learning is a crucial technology for extracting insights from these vast datasets. Among machine learning methods, ensemble methods, and especially cascading ensembles, are highly effective for big data analysis. While it is true that the training procedures for cascade ensembles can be time-consuming and may have limitations in terms of accuracy, this paper proposes a solution to enhance their performance. Our method involves using stochastic gradient descent (SGD) classifiers, an improved training data separation algorithm, and integrating principal component analysis (PCA) at each ensemble level. We are confident that these enhancements lead to improved results and accuracy. The proposed approach is designed to enhance both the generalization properties and accuracy of the ensemble (3%), while also reducing its training time. Results from modelling on a real-world biomedical dataset demonstrate significant reductions in training duration, improvements in generalization properties, and enhanced accuracy when compared to other possible implementations of the ensemble.
Optimization model for endurance performance of electric rotorcraft transport drones and its application prospects
Authors: Zihan Huang , Bo Long , Jiyu Li
Published: February 2025

The operational parameter configuration and performance optimization of electric rotorcraft transport unmanned aerial vehicles (UAVs) currently lack comprehensive guiding theory, impacting UAV endurance and efficiency, thereby limiting industry growth. This paper analyzes factors affecting UAV endurance and establishes a hover endurance model for electric rotorcraft transport UAVs through theoretical derivation and testing. Based on this model, we introduce the concepts of thrust redundancy coefficient and load cut-off line, proposing an optimal endurance configuration theory. This theory categorizes the parameter configuration range into light load, ideal configuration, load cut-off, and endurance saturation zones. Using current operational parameters, we evaluate and optimize UAV performance. Verification results demonstrate high model accuracy, with error rates ranging from 1.89% to 5.69%. After optimization, the payload capacities of two transport UAVs increased by 6.25%, and their endurance improved by 6.97% and 9.5%, respectively, enhancing overall efficiency. This model provides a solid framework for assessing endurance capabilities and offers targeted optimization suggestions, making it crucial for improving UAV performance.
Design of a prototype firefighting robot based on an Arduino microcontroller using machine learning technique
Authors: Chenchireddy Kalagotla , Dora Radhika , Jagan Vadthya , Mulla Gouse Basha , Jegathesan Varghese , Sydu Shabbier Ahmed
Published: February 2025

The design and implementation of this paper are mainly based on control of the autonomous firefighting robot. In recent years, robotics has turned out to be an ingredient in which many people have shown their interest. Robotics has gained popularity due to the advancement of many technologies of computing and nanotechnologies. The output of the fire sensor is connected to the Arduino controller that controls the movement of the vehicle and the operation of spraying water. An infrared sensing circuit is designed with the infrared sensors placed in front of the vehicle to avoid collision with the obstacles. A total of two inbuilt reduction geared direct current motors are used in the paper for the robot movement in all the directions forward, backward, right, and left directions. For more practicality, a small water tank with a pumping motor is also arranged over the chassis and the water sprinkler pipe that is firmly fixed over the plate where the sensor is arranged can deliver water with some force. When the sensor detects the fire, the sprinkler is positioned toward fire flames; the pumping motor will be energized automatically to extinguish the fire. The main advantage of the proposed system automatically controls the fire by using advanced control techniques.
Description of the final instar larva of Aethriamanta brevipennis (Rambur, 1842) [Odonata: Anisoptera: Libellulidae] from Kerala
Authors: CHANDRAN AYIKKARA VIVEK , JOSE SUBIN KANIYAMATTATHIL , DAWN PROSENJIT
Published: February 2025

The final instar larva and exuvia of Aethriamanta brevipennis (Rambur, 1842) is described from Thumboor Wetlands, Kerala. This Asian species bears dorsal spines on S5–8, short lateral spines on S8–9; labium has 12–12 premental setae and 7–7 palpal setae. Numerous long, hair-like setae are present all over the body, distal abdominal segments being most setose; setae also present on tibiae of all legs, wing-pads and posterior margin of head.
Two new Clearwing moths from coastal Kenya (Lepidoptera: Sesiidae: Melittiini, Osminiini)
Authors: BARTSCH DANIEL , SÁFIÁN SZABOLCS
Published: February 2025

In this study, two new species of African Sesiidae are described from coastal Kenya. Melittia mida sp. nov. (Melittiini) from a series of eight males from Mida village at the edge of the Arabuko-Sokoke Forest, and Homogyna fenestra sp. nov. (Osminiini) from ten males collected across the Kenyan coast between the Dakatcha Woodland in the north and Diani beach in the south. Adults and male genitalia of both species are illustrated. Melittia mida sp. nov. is well characterized by the predominantly olive-brown body colour and the large transparent areas of the forewings. Homogyna fenestra sp. nov. differs from similar congeners by a transparent area distal to the forewing discal spot and by the completely transparent hindwings.