bluuebunny/crossref_metadata_2025 huggingface dataset:
Metabolomic Profiling and Machine Learning Models for Tumor Classification in Patients with Recurrent IDH-Wild-Type Glioblastoma: A Prospective Study
Authors: Hodeify Rawad , Yu Nina , Balasubramaniam Meenakshisundaram , Godinez Felipe , Liu Yin , Aboud Orwa
Published: November 2024

Background/Objectives: The recurrence of glioblastoma is an inevitable event in this disease’s course. In this study, we sought to identify the metabolomic signature in patients with recurrent glioblastomas undergoing surgery and radiation therapy. Methods: Blood samples collected prospectively from six patients with recurrent IDH-wildtype glioblastoma who underwent one surgery at diagnosis and a second surgery at relapse were analyzed using untargeted gas chromatography–time-of-flight mass spectrometry to measure metabolite abundance. The data analysis techniques included univariate analysis, correlation analysis, and a sample t-test. For predictive modeling, machine learning (ML) algorithms such as multinomial logistic regression, gradient boosting, and random forest were applied to predict the classification of samples in the correct treatment phase. Results: Comparing samples after the first surgery and after the relapse surgeries to the pre-operative samples showed a significant decrease in sorbitol and mannitol; there was a significant increase in urea, oxoproline, glucose, and alanine. After chemoradiation, two metabolites, erythritol and 6-deoxyglucitol, showed a decrease, with a cut-off of three and a significant reduction for 6-deoxyglucitol, while 2,4-difluorotoluene and 9-myristoleate showed an increase post radiation, with a fold-change cut-off of three. The gradient-boosting ML model achieved a high performance for the prediction of tumor conditions in patients with glioblastoma who had undergone relapse surgery. Conclusions: We developed an ML predictor for tumor phase based on the plasma metabolomic profile. Our study suggests the potential of combining metabolomics with ML as a new tool to stratify the risk of tumor progression in patients with glioblastoma.
First-Line Combination of R-CHOP with the PDE4 Inhibitor Roflumilast for High-Risk DLBCL
Authors: Duque Adolfo E. Diaz , Ferrari Pedro S. S. M. , Ethiraj Purushoth , Jaafar Carine , Qiu Zhijun , Holder Kenneth , Butler Mathew J. , Huelgas-Morales Gabriela , Karnad Anand , Dahia Patricia L. M. , Aguiar Ricardo C. T.
Published: November 2024

Background: Diffuse large B-cell lymphoma (DLBCL) is a common and often fatal malignancy. The standard-of-care immunochemotherapy, R-CHOP, cures only about 60% of DLBCL patients. Improving this cure rate will likely require the effective translation of basic biology knowledge into clinical activities. We previously identified the cyclic-AMP/phosphodiesterase 4 (PDE4) axis as an important modulator of lymphomagenic processes. We also showed that the FDA-approved PDE4 inhibitor roflumilast can suppress B-cell receptor (BCR) signals, phosphoinositide 3-kinase (PI3K) activity and angiogenesis. These data suggested that combining roflumilast with R-CHOP may be beneficial in DLBCL. Methods: We conducted a single-center, single-arm, open-label, phase 1 study of roflumilast in combination with the standard of care, R-CHOP (Ro+R-CHOP), in pathologically proven, treatment-naïve, high-risk DLBCL patients. Results: Ro+R-CHOP was safe, and at a median follow-up time of 44 months, 70% of patients were alive and disease free (median OS not reached, PFS 44% (95% CI, 21–92). In this pilot series, we found that the addition of roflumilast suppressed PI3K activity in peripheral blood mononuclear cells, and VEGF-A secretion in the urine. We also encountered preliminary evidence to suggest that the Ro+R-CHOP combination may be particularly beneficial to patients diagnosed with high-risk genetic subtypes of DLBCL, namely MCD and A53. Conclusions: These initial findings suggest that roflumilast may be an alternative agent able to inhibit BCR/PI3K activity and angiogenesis in DLBCL, and that the testing of Ro+R-CHOP in a larger series of genetically characterized tumors is warranted. This study was registered at ClinicalTrials.gov, number NCT03458546.
Liposarcoma: A Journey into a Rare Tumor’s Epidemiology, Diagnosis, Pathophysiology, and Limitations of Current Therapies
Authors: Jonczak Emily , Grossman Julie , Alessandrino Francesco , Seldon Taswell Crystal , Velez-Torres Jaylou M. , Trent Jonathan
Published: November 2024

Sarcomas are a heterogeneous group of neoplasms that develop from bone and soft tissue. Approximately 80% of sarcomas affect soft tissue, with liposarcoma being one of the most common types, accounting for approximately 13–20% of all soft-tissue sarcomas. Per the World Health Organization, liposarcoma can be broadly classified into four different subtypes based on histologic examination: well-differentiated liposarcoma (WDLS)/atypical lipomatous tumors (ALT), dedifferentiated liposarcoma (DDLS), myxoid liposarcoma (MLS), and pleomorphic liposarcoma (PLS). WDLS/ALT is the most common liposarcoma subtype, accounting for approximately 31–33% of liposarcomas; DDLS accounts for 20%; MLS accounts for 19%; and PLS, the least common subtype, represents 7–8% of liposarcomas. Sarcoma diagnosis is challenging because of its rarity, intrinsic complexity, and diagnostic technological complexity. Sarcomas are misdiagnosed in approximately 30% of cases, leading to delays in diagnosis and access to appropriate therapy and clinical trials. Furthermore, treatment options are limited for those diagnosed with liposarcoma. This review discusses the epidemiology, pathology, and treatment options currently available for liposarcoma.
Brief Magnetic Field Exposure Stimulates Doxorubicin Uptake into Breast Cancer Cells in Association with TRPC1 Expression: A Precision Oncology Methodology to Enhance Chemotherapeutic Outcome
Authors: Sukumar Viresh Krishnan , Tai Yee Kit , Chan Ching Wan , Iversen Jan Nikolas , Wu Kwan Yu , Fong Charlene Hui Hua , Lim Joline Si Jing , Franco-Obregón Alfredo
Published: November 2024

Background/Objectives: Doxorubicin (DOX) is commonly used as a chemotherapeutic agent for the treatment of breast cancer. Nonetheless, its systemic delivery via intravenous injection and toxicity towards healthy tissues commonly result in a broad range of detrimental side effects. Breast cancer severity was previously shown to be correlated with TRPC1 channel expression that conferred upon it enhanced vulnerability to pulsed electromagnetic field (PEMF) therapy. PEMF therapy was also previously shown to enhance breast cancer cell vulnerability to DOX in vitro and in vivo that correlated with TRPC1 expression and mitochondrial respiratory rates. Methods: DOX uptake was assessed by measuring its innate autofluorescence within murine 4T1 or human MCF7 breast cancer cells following magnetic exposure. Cellular vulnerability to doxorubicin uptake was assessed by monitoring mitochondrial activity and cellular DNA content. Results: Here, we demonstrate that 10 min of PEMF exposure could augment DOX uptake into 4T1 and MCF7 breast cancer cells. DOX uptake could be increased by TRPC1 overexpression, whereas inhibiting the activity of TRPC1 channels with SKF-96356 or genetic knockdown, precluded DOX uptake. PEMF exposure enhances DOX-mediated killing of breast cancer cells, reducing the IC50 value of DOX by half, whereas muscle cells, representative of collateral tissues, were less sensitive to PEMF-enhanced DOX-mediated cytotoxicity. Vesicular loading of DOX correlated with TRPC1 expression. Conclusions: This study presents a novel TRPC1-mediated mechanism through which PEMF therapy may enhance DOX cytotoxicity in breast cancer cells, paving the way for the development of localized non-invasive PEMF platforms to improve cancer outcomes with lower systemic levels of DOX.
Altered Mechanobiology of PDAC Cells with Acquired Chemoresistance to Gemcitabine and Paclitaxel
Authors: Gregori Alessandro , Bergonzini Cecilia , Capula Mjriam , de Mercado Rick Rodrigues , Danen Erik H. J. , Giovannetti Elisa , Schmidt Thomas
Published: November 2024

Background: Pancreatic ductal adenocarcinoma acquired resistance to chemotherapy poses a major limitation to patient survival. Despite understanding some biological mechanisms of chemoresistance, much about those mechanisms remains to be uncovered. Mechanobiology, which studies the physical properties of cells, holds promise as a potential target for addressing the challenges of chemoresistance in PDAC. Therefore, we, here in an initial step, assessed the altered mechanobiology of PDAC cells with acquired chemoresistance to gemcitabine and paclitaxel. Methods: Five PDAC cell lines and six stably resistant subclones were assessed for force generation on elastic micropillar arrays. Those measurements of mechanical phenotype were complemented by single-cell motility and invasion in 3D collagen-based matrix assays. Further, the nuclear translocation of Yes-associated protein (YAP), as a measure of active mechanical status, was compared, and biomarkers of the epithelial-to-mesenchymal transition (EMT) were evaluated using RT-qPCR. Results: The PDAC cells with acquired chemoresistance exert higher traction forces than their parental/wild-type (WT) cells. In 2D, single-cell motility was altered for all the chemoresistant cells, with a cell-type specific pattern. In 3D, the spheroids of the chemoresistant PDAC cells were able to invade the matrix and remodel collagen more than their WT clones. However, YAP nuclear translocation and EMT were not significantly altered in relation to changes in other physical parameters. Conclusions: This is the first study to investigate and report on the altered mechanobiological features of PDAC cells that have acquired chemoresistance. A better understanding of mechanical features could help in identifying future targets to overcome chemoresistance in PDAC.
Prostate-Specific Membrane Antigen (PSMA) PET/CT in the Detection and Diagnosis of Hepatocellular Carcinoma (HCC): A Systematic Review and Meta-Analysis
Authors: Hannah Nicholas , Yu Catherine , Nedumannil Leya , Haridy James , Kong Grace , Boussioutas Alex , Sood Siddharth
Published: November 2024

Prostate-specific membrane antigen (PSMA) positron emission tomography (PET) is widely used in prostate cancer. Recent studies indicate hepatocellular carcinoma (HCC) demonstrates PSMA PET uptake. The diagnostic accuracy of PSMA PET for HCC is not known. We conducted a systematic review and meta-analysis of studies assessing 68Ga-PSMA-11 in HCC. Nine studies were included, with 196 patients and a total of 491 HCC lesions. Per-patient analysis yielded a pooled sensitivity of 89.8% (95% CI 78.5–95.5). Specificity was poorly reported, with insufficient data. When per-lesion level analysis was performed on seven studies, the pooled sensitivity was 94.5% (95% CI 82.9–98.4), and specificity was again poorly reported with insufficient data. Among the three studies with adequate data for full per-lesion meta-analysis, 115 lesions in 41 patients demonstrated sensitivity of 97.1% (95% CI 87.8–99.4), while specificity was 42.2% (95% CI 0.3–99.4). Two studies provided sufficient data for meta-analysis on a per-patient level (n = 50 patients), demonstrating a sensitivity of 92.5% (95% CI 64.0–98.9) and specificity of 72.4% (95% CI 1.3–99.8). PSMA PET demonstrates a high sensitivity for HCC and shows promise as an imaging modality for diagnosis and staging of HCC. However, the existing literature does not provide enough data to confidently evaluate its specificity and, therefore, accuracy. Further prospective studies are necessary, with a focus on the accurate reporting of benign lesions and inclusion of patients with an intermediate probability of HCC.
Cellular Therapies for Multiple Myeloma: Engineering Hope
Authors: Vera-Cruz Sarah , Jornet Culubret Maria , Konetzki Verena , Alb Miriam , Friedel Sabrina R. , Hudecek Michael , Einsele Hermann , Danhof Sophia , Scheller Lukas
Published: November 2024

Multiple myeloma (MM) treatment remains challenging due to its relapsed/refractory disease course as well as intra- and inter-patient heterogeneity. Cellular immunotherapies, especially chimeric antigen receptor (CAR)-T cells targeting B cell maturation antigen (BCMA), mark a major breakthrough, achieving long-lasting remissions and instilling hope for a potential cure. While ongoing clinical trials are increasingly driving approved cellular products towards earlier lines of therapy, novel targets as well as advanced approaches employing natural killer (NK) cells or dendritic cell (DC) vaccines are currently under investigation. Treatment resistance, driven by tumor-intrinsic factors such as antigen escape and the intricate dynamics of the tumor microenvironment (TME), along with emerging side effects such as movement and neurocognitive treatment-emergent adverse events (MNTs), are the major limitations of approved cellular therapies. To improve efficacy and overcome resistance, cutting-edge research is exploring strategies to target the microenvironment as well as synergistic combinatorial approaches. Recent advances in CAR-T cell production involve shortened manufacturing protocols and “off-the-shelf” CAR-T cells, aiming at decreasing socioeconomic barriers and thereby increasing patient access to this potential lifesaving therapy. In this review, we provide an extensive overview of the evolving field of cellular therapies for MM, underlining the potential to achieve long-lasting responses.
A Pathological Assessment of the Microvasculature of Biliary Tract Neoplasms Referring to Pre-Existing Blood Vessels and Vessel Co-Option
Authors: Nakanuma Yasuni , Li Zihan , Sato Yasunori , Sasaki Motoko , Harada Kenichi , Kakuda Yuko , Sugino Takashi
Published: November 2024

There are several types of microvasculature supplying neoplasms: “newly formed blood vessels” (neoangiogenesis), which are a component of the tumor microenvironment (TME) of invasive carcinoma with wound healing-like reaction; and “pre-existing blood vessels”, which are used as tumor-supplying vessels by neoplasms (co-option vessels) and are likely to develop in hypervascularized organs. We herein review the microvasculature of neoplasms of biliary tract with reference to pre-existing vessels and vessel co-options. In the hepatobiliary system, intrahepatic large and extrahepatic bile ducts (large bile ducts) and the gallbladder as well as hepatic lobules are highly vascularized regions. In large bile ducts, the biliary lining epithelia and underlining capillaries (peribiliary capillary plexus [PCP]) form the biliary epithelia–PCP alignment, whereas the hepatocyte–sinusoid alignment composes hepatic lobules. Cholangiocarcinoma (CCA) and gallbladder carcinoma (GBC) are the main biliary tract carcinomas. CCA is subdivided into distal (d/CCA), perihilar (pCCA), and intrahepatic (iCCA), and iCCA is subdivided into small duct type (SD-iCCA) and large duct type (LD-iCCA). High-grade biliary intraepithelial neoplasm (BilIN), intraductal papillary neoplasm of the bile duct (IPNB), pyloric gland adenoma (PGA), and intracholecystic papillary neoplasm (ICPN) have recently been proposed as the precursors of LD-iCCA, p/dCCA, and GBC. In the large bile ducts and gallbladder, all cases of high-grade BilIN and PGA, about half of IPNB, and one-third of ICPN with less-complicated structure were found to have hijacked the PCP as their supporting vessels (vessel co-option), while p/dCCA, LD-iCCA, and GBC were supplied by neo-angiogenetic vessels associated with fibrous stroma. The intraluminal components of the remaining cases of ICPN and IPNB with complicated structure presented sparse capillaries without fibrous stroma, a unique microvasculature different from that of co-option or neoangiogenesis. Regarding iCCA showing invasion into the hepatic lobules, some SD-iCCAs replaced hepatocytic cords and used pre-existing sinusoids as co-opted vessels. Visualization of pre-existing vessels could be a new pathological tool for the evaluation of malignant progression and of vascular supply in CCAs and its precursors.
Prognostic Role of Inflammatory and Nutritional Biomarkers in Non-Small-Cell Lung Cancer Patients Treated with Immune Checkpoint Inhibitors Alone or in Combination with Chemotherapy as First-Line
Authors: Veccia Antonello , Dipasquale Mariachiara , Kinspergher Stefania , Caffo Orazio
Published: November 2024

Introduction: In recent years, several inflammation-related factors and nutritional parameters have been evaluated to develop prognostic scores as potential biomarkers in non-small-cell lung cancer (NSCLC) patients receiving immune checkpoint inhibitors (ICIs). The aim of this study was to retrospectively investigate the prognostic role of the advanced lung cancer inflammation (ALI) index, lung immune prognostic index (LIPI), prognostic nutritional index (PNI) and systemic inflammation score (SIS) in metastatic NSCLC patients receiving ICI alone or in combination with chemotherapy. Methods and patients: We retrospectively included 191 patients with advanced NSCLC who received first-line ICI with or without chemotherapy from 2017 to 2024. The association between pretreatment ALI, LIPI, PNI, and SIS and overall survival (OS) was evaluated using the Kaplan–Meier method and Cox regression models. Results: After a median follow-up of 27.7 months, significantly longer OS was associated with an ALI score > 18 vs. ≤18 (18.0 vs. 7.3 months; p = 0.00111), LIPI score 0 vs. 1 and 2 [18.9 vs. 8.2 and 4.2 months; (p = 0.001)], PNI ≥ 45 vs. <45 (22.7 vs. 9.6 months; p = 0.002), and SIS score 0 vs. 1 and 2 (27.4 vs. 7.1 and 8.6 months, respectively; p < 0.001). The OS benefit was independent of treatment (ICI vs. ICI + chemotherapy). At multivariate analysis, pretreatment albumin was positively associated with OS, while ECOG PS 1 and liver metastases were negatively associated with OS. Conclusions: Inflammatory and nutritional biomarkers such as the ALI, LIPI, PNI, and SIS represent useful tools to prognosticate survival in metastatic lung cancer patients treated with ICI alone or in combination with chemotherapy as first-line.
The Role of eHsp90 in Extracellular Matrix Remodeling, Tumor Invasiveness, and Metastasis
Authors: Singh Pragya , Jay Daniel G.
Published: November 2024

Identifying proteins that act in tumor invasiveness and metastasis remains a critical unmet need in our search for effective cancer therapy. Hsp90, an abundant intracellular chaperone protein, plays a key role in maintaining cell homeostasis, and its elevated activity is pivotal in cancer progression. Due to the reliance of cancer cells on Hsp90’s chaperone function to sustain tumor growth and spread, Hsp90 inhibitors have been the subject of numerous clinical trials over the past two decades. However, these efforts have largely been unsuccessful, primarily due to the cellular toxicity caused by pan-Hsp90 inhibitors at doses required for anticancer efficacy. Therefore, novel approaches to target Hsp90 are necessary. An identified subpopulation of Hsp90 located outside cells (eHsp90) may offer a promising alternative as a therapeutic target against cancer. Studies including our own have shown that eHsp90 is released specifically by cancer cells, and eHsp90 has unique interactors and functions extracellularly to promote tumor invasiveness, the initial step in metastasis. Inhibition of eHsp90 has been shown to suppress metastasis in animal models, indicating its therapeutic potential, although the underlying mechanisms remain incompletely understood. Cancer cells modulate the tumor microenvironment (TME) during the invasion, especially the ECM proteins and the state of the ECM is a strong predictor of invasive and metastatic cancer. Given that most of the known eHsp90 clients are ECM proteins or are proteins involved in ECM modulation, ECM remodelling could be the key mechanism through which eHsp90 enhances invasiveness. This review will focus on ECM modulation by eHsp90 as a driver of cancer invasion and metastasis. We will also discuss the potency of inhibiting eHsp90 in inhibiting invasion and metastatic spread in preclinical models and the using circulating Hsp90 patient samples as a biomarker of cancer invasion and metastasis.
Adjuvant Radiation Therapy in Desmoplastic Melanoma: A Scoping Review
Authors: Sharafi Christina Setareh , Guadagnolo B. Ashleigh , Nelson Kelly C. , Mitra Devarati
Published: November 2024

Desmoplastic melanoma (DM) is an uncommon subtype of cutaneous melanoma that presents distinct diagnostic and treatment challenges. This review aims to explore the role of adjuvant radiation therapy (RT) in managing DM. To evaluate this question, we reviewed relevant published reports on DM and its treatment and synthesized these findings. It was found that the clinical behavior of DM varies significantly based on its classification as either “pure” DM (pDM, ≥90% desmoplastic features) or mixed DM (mDM, ≤90% desmoplastic features). Patients with pDM have a uniquely high risk of local recurrence but a relatively lower likelihood of nodal disease. Recent studies question the necessity of sentinel lymph node biopsy for pDM patients while illustrating impressive response rates to immune checkpoint inhibition. Most data supporting adjuvant RT predate these changes in surgical management and systemic therapy, yet consistently demonstrate that adjuvant RT reduces the absolute risk of local recurrence by >50%, without significant long-term toxicity. Thus, the existing literature continues to support the conclusion that adjuvant RT effectively reduces the likelihood of local recurrence in pDM patients. Although evolving surgical and systemic therapies are reshaping treatment approaches, adjuvant RT should remain a standard of care.
Eco-Friendly Synthesis of ZnO Nanoparticles for Quinoline Dye Photodegradation and Antibacterial Applications Using Advanced Machine Learning Models
Authors: Chelghoum Hayet , Nasrallah Noureddine , Tahraoui Hichem , Seleiman Mahmoud F. , Bouhenna Mustapha Mounir , Belmeskine Hayet , Zamouche Meriem , Djema Souhila , Zhang Jie , Mendil Amina , Dergal Fayçal , Kebir Mohammed , Amrane Abdeltif
Published: November 2024

Community drinking water sources are increasingly contaminated by various point and non-point sources, with emerging organic contaminants and microbial strains posing health risks and disrupting ecosystems. This study explores the use of zinc oxide nanoparticles (ZnO-NPs) as a non-specific agent to address groundwater contamination and combat microbial resistance effectively. The ZnO-NPs were synthesized via a green chemistry approach, employing a sol-gel method with lemon peel aqueous extract. The catalyst was characterized using techniques including XRD, ATR-FTIR, SEM-EDAX, UV-DRS, BET, and Raman spectroscopy. ZnO-NPs were then tested for photodegradation of quinoline yellow dye (QY) under sunlight irradiation, as well as for their antibacterial and antioxidant properties. The ZnO-NP photocatalyst showed significant photoactivity, attributed to effective separation of photogenerated charge carriers. The efficiency of sunlight dye photodegradation was influenced by catalyst dosage (0.1–0.6 mg L−1), pH (3–11), and initial QY concentration (10–50 mg L−1). The study developed a first-order kinetic model for ZnO-NPs using the Langmuir–Hinshelwood equation, yielding kinetic constants of equilibrium adsorption and photodegradation of Kc = 6.632 × 10−2 L mg−1 and kH = 7.104 × 10−2 mg L−1 min−1, respectively. The results showed that ZnO-NPs were effective against Gram-positive bacterial strains and showed moderate antioxidant activity, suggesting their potential in wastewater disinfection to achieve sustainable development goals. A potential antibacterial mechanism of ZnO-NPs involving interactions with microbial cells is proposed. Additionally, Gaussian Process Regression (GPR) combined with an improved Lévy flight distribution (FDB-LFD) algorithm was used to model QY photodegradation by ZnO-NPs. The ARD-Exponential kernel function provided high accuracy, validated through residue analysis. Finally, an innovative MATLAB-based application was developed to integrate the GPR_FDB-LFD model and FDB-LFD algorithm, streamlining optimization for precise photodegradation rate predictions. The results obtained in this study show that the GPR and FDB-LFD approaches offer efficient and cost-effective methods for predicting dye photodegradation, saving both time and resources.
Understanding the Negative Apparent Activation Energy for Cu2O and CoO Oxidation Kinetics at High Temperature near Equilibrium
Authors: Wang Yang , Liu Haiyang , Duan Qiwei , Li Zhenshan
Published: November 2024

The pairs of Cu2O/CuO and CoO/Co3O4 as the carriers of transferring oxygen and storing heat are essential for the recently emerged high-temperature thermochemical energy storage (TCES) system. Reported research results of Cu2O and CoO oxidation kinetics show that the reaction rate near equilibrium decreases with the temperature, which leads to the negative activation energy obtained using the Arrhenius equation and apparent kinetics models. This study develops a first-principle-based theoretical model to analyze the Cu2O and CoO oxidation kinetics. In this model, the density functional theory (DFT) is adopted to determine the reaction pathways and to obtain the energy barriers of elementary reactions; then, the DFT results are introduced into the transition state theory (TST) to calculate the reaction rate constants; finally, a rate equation is developed to describe both the surface elemental reactions and the lattice oxygen concentration in a grain. The reaction mechanism obtained from DFT and kinetic rate constants obtained from TST are directly implemented into the rate equation to predict the oxidation kinetics of Cu2O without fitting experimental data. The accuracy of the developed theory is validated by experimental data obtained from the thermogravimetric analyzer (TGA). Comparing the developed theory with the traditional apparent models, the reasons why the latter cannot appropriately predict the true oxidation characteristics are explained. The reaction rate is jointly controlled by thermodynamics (reaction driving force) and kinetics (reaction rate constant). Without considering the effect of the reaction driving force, the negative apparent activation energy of Cu2O oxidation is obtained. However, for CoO oxidation, the negative apparent activation energy is still obtained although the effect of the reaction driving force is considered. According to the DFT results, the activation energy of the overall CoO oxidation reaction is negative, but the energy barriers of the elementary reactions are positive. Moreover, according to the first-principle-based rate equation theory, the pre-exponential factor in the kinetic model is dependent on the partition function ratio and decreases with the temperature for the Cu2O and CoO oxidation near equilibrium, which results in the apparent activation energy being slightly lower than the actual value.
The Clinical Significance of Cancer-Associated Fibroblasts Classification in Non-Small Cell Lung Cancer
Authors: Papavassiliou Kostas A. , Sofianidi Amalia A. , Gogou Vassiliki A. , Papavassiliou Athanasios G.
Published: November 2024

Malignant cells flourish within a specialized environment known as the tumor microenvironment (TME) [...]
Impact of Intrauterine Insults on Fetal and Postnatal Cerebellar Development in Humans and Rodents
Authors: Westerhuis Judith A. W. , Dudink Jeroen , Wijnands Bente E. C. A. , De Zeeuw Chris I. , Canto Cathrin B.
Published: November 2024

Many children suffer from neurodevelopmental aberrations that have long-term effects. To understand the consequences of pathological processes during particular periods in neurodevelopment, one has to understand the differences in the developmental timelines of brain regions. The cerebellum is one of the first brain structures to differentiate during development but one of the last to achieve maturity. This relatively long period of development underscores its vulnerability to detrimental environmental exposures throughout gestation. Moreover, as postnatal functionality of the cerebellum is multifaceted, enveloping sensorimotor, cognitive, and emotional domains, prenatal disruptions in cerebellar development can result in a large variety of neurological and mental health disorders. Here, we review major intrauterine insults that affect cerebellar development in both humans and rodents, ranging from abuse of toxic chemical agents, such as alcohol, nicotine, cannabis, and opioids, to stress, malnutrition, and infections. Understanding these pathological mechanisms in the context of the different stages of cerebellar development in humans and rodents can help us to identify critical and vulnerable periods and thereby prevent the risk of associated prenatal and early postnatal damage that can lead to lifelong neurological and cognitive disabilities. The aim of the review is to raise awareness and to provide information for obstetricians and other healthcare professionals to eventually design strategies for preventing or rescuing related neurodevelopmental disorders.
A Transcriptomics-Based Machine Learning Model Discriminating Mild Cognitive Impairment and the Prediction of Conversion to Alzheimer’s Disease
Authors: Park Min-Koo , Ahn Jinhyun , Lim Jin-Muk , Han Minsoo , Lee Ji-Won , Lee Jeong-Chan , Hwang Sung-Joo , Kim Keun-Cheol
Published: November 2024

The clinical spectrum of Alzheimer’s disease (AD) ranges dynamically from asymptomatic and mild cognitive impairment (MCI) to mild, moderate, or severe AD. Although a few disease-modifying treatments, such as lecanemab and donanemab, have been developed, current therapies can only delay disease progression rather than halt it entirely. Therefore, the early detection of MCI and the identification of MCI patients at high risk of progression to AD remain urgent unmet needs in the super-aged era. This study utilized transcriptomics data from cognitively unimpaired (CU) individuals, MCI, and AD patients in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort and leveraged machine learning models to identify biomarkers that differentiate MCI from CU and also distinguish AD from MCI individuals. Furthermore, Cox proportional hazards analysis was conducted to identify biomarkers predictive of the progression from MCI to AD. Our machine learning models identified a unique set of gene expression profiles capable of achieving an area under the curve (AUC) of 0.98 in distinguishing those with MCI from CU individuals. A subset of these biomarkers was also found to be significantly associated with the risk of progression from MCI to AD. A linear mixed model demonstrated that plasma tau phosphorylated at threonine 181 (pTau181) and neurofilament light chain (NFL) exhibit the prognostic value in predicting cognitive decline longitudinally. These findings underscore the potential of integrating machine learning (ML) with transcriptomic profiling in the early detection and prognostication of AD. This integrated approach could facilitate the development of novel diagnostic tools and therapeutic strategies aimed at delaying or preventing the onset of AD in at-risk individuals. Future studies should focus on validating these biomarkers in larger, independent cohorts and further investigating their roles in AD pathogenesis.
DiscHAR: A Discrete Approach to Enhance Human Activity Recognition in Cyber Physical Systems: Smart Homes
Authors: Fatima Ishrat , Farhan Asma Ahmad , Tamoor Maria , ur Rehman Shafiq , Alhulayyil Hisham Abdulrahman , Tariq Fawaz
Published: November 2024

The main challenges in smart home systems and cyber-physical systems come from not having enough data and unclear interpretation; thus, there is still a lot to be done in this field. In this work, we propose a practical approach called Discrete Human Activity Recognition (DiscHAR) based on prior research to enhance Human Activity Recognition (HAR). Our goal is to generate diverse data to build better models for activity classification. To tackle overfitting, which often occurs with small datasets, we generate data and convert them into discrete forms, improving classification accuracy. Our methodology includes advanced techniques like the R-Frame method for sampling and the Mixed-up approach for data generation. We apply K-means vector quantization to categorize the data, and through the elbow method, we determine the optimal number of clusters. The discrete sequences are converted into one-hot encoded vectors and fed into a CNN model to ensure precise recognition of human activities. Evaluations on the OPP79, PAMAP2, and WISDM datasets show that our approach outperforms existing models, achieving 89% accuracy for OPP79, 93.24% for PAMAP2, and 100% for WISDM. These results demonstrate the model’s effectiveness in identifying complex activities captured by wearable devices. Our work combines theory and practice to address ongoing challenges in this field, aiming to improve the reliability and performance of activity recognition systems in dynamic environments.
A Fuzzy-Immune-Regulated Single-Neuron Proportional–Integral–Derivative Control System for Robust Trajectory Tracking in a Lawn-Mowing Robot
Authors: Saleem Omer , Hamza Ahmad , Iqbal Jamshed
Published: November 2024

This paper presents the constitution of a computationally intelligent self-adaptive steering controller for a lawn-mowing robot to yield robust trajectory tracking and disturbance rejection behavior. The conventional fixed-gain proportional–integral–derivative (PID) control procedure lacks the flexibility to deal with the environmental indeterminacies, coupling issues, and intrinsic nonlinear dynamics associated with the aforementioned nonholonomic system. Hence, this article contributes to formulating a self-adaptive single-neuron PID control system that is driven by an extended Kalman filter (EKF) to ensure efficient learning and faster convergence speeds. The neural adaptive PID control formulation improves the controller’s design flexibility, which allows it to effectively attenuate the tracking errors and improve the system’s trajectory tracking accuracy. To supplement the controller’s robustness to exogenous disturbances, the adaptive PID control signal is modulated with an auxiliary fuzzy-immune system. The fuzzy-immune system imitates the automatic self-learning and self-tuning characteristics of the biological immune system to suppress bounded disturbances and parametric variations. The propositions above are verified by performing the tailored hardware in the loop experiments on a differentially driven lawn-mowing robot. The results of these experiments confirm the enhanced trajectory tracking precision and disturbance compensation ability of the prescribed control method.
Fish Diversity in a Little-Known Border River Between China, North Korea, and Russia, According to Traditional and eDNA Surveys
Authors: Tian Chen , Sun Zhixian , Fang Yutian , Sheng Dong , Li Bo , Liu Cunqi , Zhao Yahui
Published: November 2024

The Tumen River, which delineates the border between China, North Korea, and Russia, is a vital river in Northeast Asia. Understanding its current fish diversity holds significant scientific value for aquatic ecological protection. Therefore, traditional and eDNA sampling were conducted from 2022 to 2023. Integrating the historical literature with our field collection results reveals that the Tumen River has a total of 64 native fish species, of which 51 species have been surveyed in the wild. The fish composition is mainly dominated by cold-water species, with a high proportion of these being rare, endangered, and nationally protected. eDNA surveys are crucial complements in boundary rivers where traditional methods are not feasible. The results indicated that α diversity values were highest in the Hunchunhe River, attributed to the favorable natural geographical conditions and effective conservation efforts. Recently, the fish diversity in the Tumen River has been disrupted, primarily reflected in the absence of migratory species such as Oncorhynchus gorbuscha and Pseudaspius hakonensis. The study suggests that establishing nature reserves, preventing and controlling alien species, and strengthening international cooperation are key to protect fish diversity.
Candidemia in an Orthopedic Patient Detected Coincidentally by Peripheral Blood Smear
Authors: Spatha Eirini , Gheorghe Loredana-Mariana , Chaliori Ioulia , Tsagarakis Nikolaos J. , Patsiogiannis Nikolaos , Chaniotaki Sofia K.
Published: November 2024

An elderly male, with a recent COVID-19 infection and cardiovascular comorbidities, experienced a prolonged hospitalization due to a periprosthetic joint infection (PJI) and bacteremia, post hip hemiarthroplasty. Despite the initial clinical improvement while on targeted antimicrobial therapy, the patient later developed a low-grade fever and signs of myelosuppression. In the May–Grünwald–Giemsa stain of peripheral blood smear (PBS), pseudohyphae among red blood cells (RBCs) and phagocytosed blastospores in neutrophils and monocytes were detected, indicating candidemia rather than contamination of the stain. Echinocandin treatment was immediately initiated, and Candida albicans was identified from the blood culture, using multiplex polymerase chain reaction (PCR). Despite the early initiation of antifungal therapy and the removal of the central venous line (CVL), the patient passed away within 24 h. Candidemia is a leading cause of nosocomial bloodstream infections with high morbidity and mortality and is associated with multiple risk factors including surgery, CVLs, prolonged hospitalization, concomitant bacterial infection, broad-spectrum antibiotics, and immunosuppression. Isolation from blood cultures remains the gold standard for diagnosing candidemia. Detection of candidemia by PBS is extremely rare, requires an experienced microscopist, and is considered to be an emergency. Clinical suspicion, early laboratory identification, and immediate clinician notification are crucial for prompt antifungal treatment.
Predictive Value of Clinical and Dual-Energy Computed Tomography Parameters for Hemorrhagic Transformation and Long-Term Outcomes Following Endovascular Thrombectomy
Authors: Huang Shiu-Yuan , Liao Nien-Chen , Huang Jin-An , Chen Wen-Hsien , Chen Hung-Chieh
Published: November 2024

Objective: This study’s objective was to explore whether certain parameters measurable by dual-energy computed tomography (DECT) performed 24 h after endovascular thrombectomy (EVT) can predict subsequent hemorrhagic transformation. Material and Methods: We retrospectively reviewed patients with acute ischemic stroke (AIS) managed with EVT who had follow-up DECT within 24 h post-EVT between January 2019 and December 2023. Clinical and image parameters were recorded for predictive factor analysis. The primary outcome was hemorrhagic transformation, which was determined by using follow-up computed tomography (CT) or magnetic resonance imaging (MRI). The secondary outcomes were in-hospital mortality and 3-month post-EVT favorable functional outcome, as defined by a modified ranking scale (mRS) score of ≤2. Results: A total of 152 patients were included in this study. Multivariable analysis showed that the VNC-ASPECT score (p = 0.002) and superior sagittal sinus density (p = 0.01) were significantly associated with hemorrhagic transformation. For in-hospital survival rate analysis, post-EVT NIHSS measured 24 h post-EVT was an effective predictor, with a cutoff value of 23 (≤23: 88% vs. >23: 52.1%; p < 0.001). For functional outcome analysis, age (p < 0.001), tPA prior to EVT (p = 0.017), NIHSS 24 h post-EVT (p = 0.001), and VNC-ASPECT score (p < 0.003) were associated with a favorable functional outcome 3 months after EVT. Conclusions: The VNC-ASPECT score was associated with both hemorrhagic transformation and a 3-month post-EVT favorable functional outcome, and could therefore be an useful predictor for the development of hemorrhagic transformation.
A Systematic Review and Meta-Analysis of Molecular Characteristics on Colistin Resistance of Acinetobacter baumannii
Authors: Ciftci Ihsan Hakki , Kahraman Kilbas Elmas Pinar , Kilbas Imdat
Published: November 2024

Background: This study aimed to determine the molecular epidemiology of colistin-resistant A. baumannii in the last ten years and the frequency of gene regions related to pathogenesis, to compare the methods used to detect genes, and to confirm colistin resistance. Methods: This meta-analysis study was conducted under Preferred Reporting Items for Systematic Reviews and Meta-Analysis Guidelines. In the meta-analysis, research articles published in English and Turkish in electronic databases between January 2012 and November 2023 were examined. International Business Machines (IBM) Statistical Package for the Social Sciences (SPSS) Statistics for Macbook (Version 25.0. Armonk, NY, USA) was used for statistical analysis. The Comprehensive Meta-Analysis (CMA) (Version 3.0. Biostat, NJ, USA) program was used for heterogeneity assessment in the articles included in the meta-analysis. Results: After evaluating the studies according to the elimination criteria, 18 original articles were included. Among colistin-resistant strains, blaOXA-51 positivity was 243 (19.61%), blaOXA-23 was 113 (9.12%), blaOXA-58 was 7 (0.56%), blaOXA-143 was 15 (1.21%), and blaOXA-72 was seen in two (0.16%) strains. The positivity rates of pmrA, pmrB, and pmrC were found to be 22 (1.77%), 26 (2.09%), and 6 (0.48%). The mcr-1 rate was found to be 91 (7.34%), the mcr-2 rate was 78 (6.29%), and the mcr-3 rate was 82 (6.61%). Conclusions: The colistin resistance rate in our study was found to be high. However, only some research articles report and/or investigate more than one resistance gene together. Additionally, it may be challenging to explain colistin resistance solely by expressing resistance genes without discussing accompanying components such as efflux pumps, virulence factors, etc.
An Investigation of High School Preservice Teachers’ Self-Efficacy in Teaching Mathematics
Authors: Hendricks Winston , Olawale Babawande Emmanuel , Saddiq Khalid
Published: November 2024

The influential theory proposed by Bandura suggests that teachers with high self-efficacy—those who strongly believe in their abilities—are more likely to effectively enhance learners’ performance. Numerous studies have consistently provided substantial evidence supporting this claim. Therefore, it is essential to investigate preservice teachers’ self-efficacy in teaching mathematics and their expectations for outcomes. This study aims to ascertain the self-efficacy beliefs of preservice high school teachers regarding mathematics instruction. The Mathematics Teaching Efficacy Beliefs Instrument (MTEBI) survey was completed by 63 participants from a rural institution. This survey was administered after the completion of a Mathematics Methods course but before the onset of the school experience exercise (practicum). The MTEBI consists of 21 items and uses a five-point, forced-choice Likert-like scale to assess participants’ perceptions of their effectiveness in teaching mathematics. The survey evaluates two subscales: Personal Knowledge of Mathematics Teaching Efficacy (PMTE) and Expectancy of Mathematics Teaching Outcomes (MTOE). The results on the PMTE subscale showed a mean score of 43.1 and a standard deviation of 6.1, indicating that respondents possess good personal mathematics teaching efficacy. On the MTOE subscale, the participants had a mean score of 31.5 and a standard deviation of 3.72, reflecting a positive anticipation of learners’ mathematics learning outcomes. The research also found no statistically significant differences in self-efficacy in teaching mathematics or expectations between the two genders. However, female preservice mathematics teachers reported a notably higher average in teaching self-efficacy and teaching outcome expectancy compared to their male colleagues. Consequently, this study concludes that although female preservice teachers may be more effective than their male counterparts, there is a general need to enhance self-efficacy among all preservice teachers by providing various opportunities, such as increased coursework, field experiences, and peer mentoring.
A Novel Multi-Scale Feature Enhancement U-Shaped Network for Pixel-Level Road Crack Segmentation
Authors: Wang Jing , Shen Benlan , Li Guodong , Gao Jiao , Chen Chao
Published: November 2024

Timely and accurate detection of pavement cracks, the most common type of road damage, is essential for ensuring road safety. Automatic image segmentation of cracks can accurately locate their pixel positions. This paper proposes a Multi-Scale Feature Enhanced U-shaped Network (MFE-UNet) for pavement crack detection. This network model uses a Residual Detail-Enhanced Block (RDEB) instead of a conventional convolution in the encoder–decoder process. The block combines Efficient Multi-Scale Attention to enhance its feature extraction performance. The Multi-Scale Gating Feature Fusion (MGFF) is incorporated into the skip connections, enhancing the fusion of multi-scale features to capture finer crack details while maintaining rich semantic information. Furthermore, we created a pavement crack image dataset named China_MCrack, consisting of 1500 images collected from road surfaces using smartphone-mounted motorbikes. The proposed network was trained and tested on the China_MCrack, DeepCrack, and Crack-Forest datasets, with additional generalization experiments on the BochumCrackDataset. The results were compared with those of the U-Net model, ResUNet, and Attention U-Net. The experimental results show that the proposed MFE-UNet model achieves accuracies of 82.95%, 91.71%, and 69.02% on three datasets, namely, China_MCrack, DeepCrack, and Crack-Forest datasets, respectively, and the F1_score is improved by 1–4% compared with other networks. Experimental results demonstrate that the proposed method is effective in detecting cracks at the pixel level.
LEVIOSA: Natural Language-Based Uncrewed Aerial Vehicle Trajectory Generation
Authors: Aikins Godwyll , Dao Mawaba Pascal , Moukpe Koboyo Josias , Eskridge Thomas C. , Nguyen Kim-Doang
Published: November 2024

This paper presents LEVIOSA, a novel framework for text- and speech-based uncrewed aerial vehicle (UAV) trajectory generation. By leveraging multimodal large language models (LLMs) to interpret natural language commands, the system converts text and audio inputs into executable flight paths for UAV swarms. The approach aims to simplify the complex task of multi-UAV trajectory generation, which has significant applications in fields such as search and rescue, agriculture, infrastructure inspection, and entertainment. The framework involves two key innovations: a multi-critic consensus mechanism to evaluate trajectory quality and a hierarchical prompt structuring for improved task execution. The innovations ensure fidelity to user goals. The framework integrates several multimodal LLMs for high-level planning, converting natural language inputs into 3D waypoints that guide UAV movements and per-UAV low-level controllers to control each UAV in executing its assigned 3D waypoint path based on the high-level plan. The methodology was tested on various trajectory types with promising accuracy, synchronization, and collision avoidance results. The findings pave the way for more intuitive human–robot interactions and advanced multi-UAV coordination.