Rytis Maskeliūnas¹, Robertas Damaševičius^1,*, Audrius Kulikajevas¹, Kipras Pribuišis², Nora Ulozaitė-Stanienė², Virgilijus Uloza²

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4203-4223, 2025, DOI:10.32604/cmes.2025.072790 - 23 December 2025

Abstract This study introduces a novel voice cloning framework driven by Mordukhovich Subdifferential Optimization (MSO) to address the complex multi-objective challenges of pathological speech synthesis in under-resourced Lithuanian language with unique phonemes not present in most pre-trained models. Unlike existing voice synthesis models that often optimize for a single objective or are restricted to major languages, our approach explicitly balances four competing criteria: speech naturalness, speaker similarity, computational efficiency, and adaptability to pathological voice patterns. We evaluate four model configurations combining Lithuanian and English encoders, synthesizers, and vocoders. The hybrid model (English encoder, Lithuanian synthesizer, English More >

Si Qin, Shu Tang, Lijiao Xie, Jianbo Zhu, Jianguo Sun^*

Oncology Research, Vol.33, No.12, pp. 4145-4154, 2025, DOI:10.32604/or.2025.068300 - 27 November 2025

Abstract Background: Primary pulmonary lymphoepithelioma-like carcinoma (PPLELC) is a rare subtype of primary non-small cell lung cancer (NSCLC), with no established treatment guidelines. We present a case of a young female with PPLELC who achieved a pathological complete response (pCR) in both primary and metastatic lesions after receiving combined immunotherapy and radiotherapy. Case description: We present a 33-year-old female patient with stage IVa (cT2bN0M1b) PPLELC. As a first-line treatment, the patient received seven cycles of nab-paclitaxel combined with toripalimab (a PD-1 inhibitor) and achieved stable disease. This was followed by toripalimab maintenance therapy for nearly 30 months.… More >

Lihao Zhang^1,2, Lin Cao^1,2, Lige Huang^1,2, Jie Wang^1,2, Jiabing Li^2,3,*

Canadian Journal of Urology, Vol.32, No.5, pp. 457-468, 2025, DOI:10.32604/cju.2025.067364 - 30 October 2025

Abstract Background: Studies have indicated an association between inflammatory factors (IFs) in the blood and the development of bladder cancer (BC). This study aimed to explore the correlation and clinical significance of IFs with the pathological grading of BC. Methods: A retrospective analysis was conducted on the preoperative blood routine results, postoperative pathological findings, and baseline information of 163 patients. Patients were divided into high-grade and low-grade groups based on pathological grading. Group comparisons and logistic regression analyses were performed using R software version 4.1.3 to explore the relationships between IFs and BC pathological grading. Results: The… More >

Roseline Oluwaseun Ogundokun^*, Pius Adewale Owolawi, Chunling Tu

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4869-4885, 2025, DOI:10.32604/cmc.2025.064944 - 30 July 2025

Abstract Breast cancer is among the leading causes of cancer mortality globally, and its diagnosis through histopathological image analysis is often prone to inter-observer variability and misclassification. Existing machine learning (ML) methods struggle with intra-class heterogeneity and inter-class similarity, necessitating more robust classification models. This study presents an ML classifier ensemble hybrid model for deep feature extraction with deep learning (DL) and Bat Swarm Optimization (BSO) hyperparameter optimization to improve breast cancer histopathology (BCH) image classification. A dataset of 804 Hematoxylin and Eosin (H&E) stained images classified as Benign, in situ, Invasive, and Normal categories (ICIAR2018_BACH_Challenge) has… More >

Amjad Rehman¹, Muhammad Mujahid¹, Robertas Damasevicius^2,*, Faten S Alamri³, Tanzila Saba¹

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2375-2397, 2024, DOI:10.32604/cmes.2024.056937 - 31 October 2024

Abstract This study aims to develop a computational pathology approach that can properly detect and distinguish histology nuclei. This is crucial for histopathological image analysis, as it involves segmenting cell nuclei. However, challenges exist, such as determining the boundary region of normal and deformed nuclei and identifying small, irregular nuclei structures. Deep learning approaches are currently dominant in digital pathology for nucleus recognition and classification, but their complex features limit their practical use in clinical settings. The existing studies have limited accuracy, significant processing costs, and a lack of resilience and generalizability across diverse datasets. We… More >

Rabia Javed¹, Tanzila Saba², Tahani Jaser Alahmadi^3,*, Sarah Al-Otaibi⁴, Bayan AlGhofaily², Amjad Rehman²

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 809-825, 2024, DOI:10.32604/cmc.2024.052755 - 15 October 2024

Abstract Cancer poses a significant threat due to its aggressive nature, potential for widespread metastasis, and inherent heterogeneity, which often leads to resistance to chemotherapy. Lung cancer ranks among the most prevalent forms of cancer worldwide, affecting individuals of all genders. Timely and accurate lung cancer detection is critical for improving cancer patients’ treatment outcomes and survival rates. Screening examinations for lung cancer detection, however, frequently fall short of detecting small polyps and cancers. To address these limitations, computer-aided techniques for lung cancer detection prove to be invaluable resources for both healthcare practitioners and patients alike.… More >

D. Banumathy^1,*, Osamah Ibrahim Khalaf², Carlos Andrés Tavera Romero³, P. Vishnu Raja⁴, Dilip Kumar Sharma⁵

Computer Systems Science and Engineering, Vol.48, No.3, pp. 863-866, 2024, DOI:10.32604/csse.2024.053657 - 20 May 2024

Abstract This article has no abstract. More >

VIGNESH BALAJI E¹, DIVYA RAMESH², MANISHA CHUNGAN SHAJU³, AKSHARA KUMAR⁴, SAMYAK PANDEY¹, RAKSHA NAYAK¹, V. ALKA⁵, SRISHTI MUNJAL⁶, AMIR SALIMI⁷, K. SREEDHARA RANGANATH PAI^1,*, SHANKAR M. BAKKANNAVAR²

Oncology Research, Vol.32, No.1, pp. 73-94, 2024, DOI:10.32604/or.2023.030401 - 15 November 2023

Abstract Exosomes, small tiny vesicle contains a large number of intracellular particles that employ to cause various diseases and prevent several pathological events as well in the human body. It is considered a “double-edged sword”, and depending on its biological source, the action of exosomes varies under physiological conditions. Also, the isolation and characterization of the exosomes should be performed accurately and the methodology also will vary depending on the exosome source. Moreover, the uptake of exosomes from the recipients’ cells is a vital and initial step for all the physiological actions. There are different mechanisms More > Graphic Abstract

JIAWEI FU^1,2,3,#, CHUNSHUAI WU^1,2,3,#, GUANHUA XU^1,2,3, JINLONG ZHANG¹, YIQIU LI¹, CHUNYAN JI^1,2,3, ZHIMING CUI^1,2,3,*

BIOCELL, Vol.47, No.4, pp. 739-749, 2023, DOI:10.32604/biocell.2023.026881 - 08 March 2023

Abstract Spinal cord injury (SCI), a complex neurological disorder, triggers a series of devastating neuropathological events such as ischemia, oxidative stress, inflammatory events, neuronal apoptosis, and motor dysfunction. However, the classical necrosome, which consists of receptor-interacting protein (RIP)1, RIP3, and mixed-lineage kinase domain-like protein, is believed to control a novel type of programmed cell death called necroptosis, through tumour necrosis factor-alpha/tumour necrosis factor receptor-1 signalling or other stimuli. Several studies reported that necroptosis plays an important role in neural cell damage, release of intracellular pro-inflammatory factors, lysosomal dysfunction and endoplasmic reticulum stress. Recent research indicates that More >

M. Muthalakshmi^1,*, T. Merlin Inbamalar², C. Chandravathi³, K. Saravanan⁴

Computer Systems Science and Engineering, Vol.46, No.1, pp. 735-747, 2023, DOI:10.32604/csse.2023.032449 - 20 January 2023

Abstract This research work develops new and better prognostic markers for predicting Childhood MedulloBlastoma (CMB) using a well-defined deep learning architecture. A deep learning architecture could be designed using ideas from image processing and neural networks to predict CMB using histopathological images. First, a convolution process transforms the histopathological image into deep features that uniquely describe it using different two-dimensional filters of various sizes. A 10-layer deep learning architecture is designed to extract deep features. The introduction of pooling layers in the architecture reduces the feature dimension. The extracted and dimension-reduced deep features from the arrangement More >