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Recent Articles  

15 pages, 1351 KiB  
Article
A Machine Learning-Based Detection for Parameter Tampering Vulnerabilities in Web Applications Using BERT Embeddings
by Sun Young Yun and Nam-Wook Cho
Symmetry 2025, 17(7), 985; https://doi.org/10.3390/sym17070985 (registering DOI) - 22 Jun 2025
Abstract
The widespread adoption of web applications has led to a significant increase in the number of automated cyberattacks. Parameter tampering attacks pose a substantial security threat, enabling privilege escalation and unauthorized data exfiltration. Traditional pattern-based detection tools exhibit limited efficacy against such threats, [...] Read more.
The widespread adoption of web applications has led to a significant increase in the number of automated cyberattacks. Parameter tampering attacks pose a substantial security threat, enabling privilege escalation and unauthorized data exfiltration. Traditional pattern-based detection tools exhibit limited efficacy against such threats, as identical parameters may produce varying response patterns contingent on their processing context, including security filtering mechanisms. This study proposes a machine learning-based detection model to address these limitations by identifying parameter tampering vulnerabilities through a contextual analysis. The training dataset aggregates real-world vulnerability cases collected from web crawls, public vulnerability databases, and penetration testing reports. The Synthetic Minority Over-sampling Technique (SMOTE) was employed to address the data imbalance during training. Recall was adopted as the primary evaluation metric to prioritize the detection of true vulnerabilities. Comparative analysis showed that the XGBoost model demonstrated superior performance and was selected as the detection model. Validation was performed using web URLs with known parameter tampering vulnerabilities, achieving a detection rate of 73.3%, outperforming existing open-source automated tools. The proposed model enhances vulnerability detection by incorporating semantic representations of parameters and their values using BERT embeddings, enabling the system to learn contextual characteristics beyond the capabilities of pattern-based methods. These findings suggest the potential of the proposed method for scalable, efficient, and automated security diagnostics in large-scale web environments. Full article
(This article belongs to the Section Computer)
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28 pages, 6169 KiB  
Article
FairChain: A Trusted and Transparent Blockchain-Based Ecosystem for Drug Development for Nagoya Protocol Implementation
by Shada AlSalamah, Shaima A. Alnehmi, Anfal A. Abanumai, Asmaa H. Alnashri, Sara S. Alduhim, Norah A. Alnamlah, Khulood AlGhamdi, Haytham A. Sheerah, Sara A. Alsalamah and Hessah A. Alsalamah
Electronics 2025, 14(13), 2527; https://doi.org/10.3390/electronics14132527 (registering DOI) - 22 Jun 2025
Abstract
The coronavirus pandemic has spread globally, affecting over 700 million people and resulting in over 7 million deaths. In response, global pharmaceutical companies and disease control centers have urgently sought effective treatments and vaccines. However, the rise of counterfeit drugs has become a [...] Read more.
The coronavirus pandemic has spread globally, affecting over 700 million people and resulting in over 7 million deaths. In response, global pharmaceutical companies and disease control centers have urgently sought effective treatments and vaccines. However, the rise of counterfeit drugs has become a significant concern amid this urgency. To standardize the legal provision and usage of genetic resources, the United Nations Development Program (UNDP) introduced the Nagoya Protocol. Despite advancements in drug research, the production process remains tedious, complex and vulnerable to fraud. FairChain addresses this pressing challenge by creating a transparent ecosystem that builds trust among all stakeholders throughout the Drug Development Life Cycle (DDLC) by using decentralized, immutable, and transparent blockchain technology. This makes FairChain the first digital health tool to implement the principles of the UNDP’s Nagoya Protocol among all stakeholders throughout all DDLC stages, starting with sample collection, to discovery and development, to preclinical research, to clinical development, to regulator review, and ending with post-market monitoring. Therefore, FairChain allows pharmaceutical companies to document the entire drug production process, landowners to monitor bio-samples from their land, doctors to share clinical research, and regulatory agencies such as the Food and Drug Authority to oversee samples and authorize production. FairChain should enhance transparency, foster trust and efficiency, and ensure a fair and traceable DDLC. To date, no blockchain-based framework has addressed the integration of traceability, auditability, and Nagoya Protocol compliance within a unified system architecture. This paper introduces FairChain, a system that formalizes these requirements in a modular, policy-aligned, and verifiable digital trust infrastructure. Full article
(This article belongs to the Section Computer Science & Engineering)
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18 pages, 3704 KiB  
Article
BTEX-K Ameliorates Rheumatoid Arthritis Through Regulating the NF-κB and PPAR-γ Signaling Pathways in Incomplete Freund’s Adjuvant-Induced Arthritis Mice
by Joonpyo Hong, Jin-Ho Lee, Ga Young Lee, Jin-Hwan Oh, Hana Lee, Han Sung Kim and Tack-Joong Kim
Biomedicines 2025, 13(7), 1524; https://doi.org/10.3390/biomedicines13071524 (registering DOI) - 22 Jun 2025
Abstract
Background/Objectives: Degenerative arthritis is a chronic inflammatory disease marked by tissue degradation and vascular fibrosis. Macrophages play a central role in the inflammatory response by releasing mediators such as nitric oxide (NO), interleukin (IL)-6, tumor necrosis factor alpha (TNF-α), and prostaglandin E2 [...] Read more.
Background/Objectives: Degenerative arthritis is a chronic inflammatory disease marked by tissue degradation and vascular fibrosis. Macrophages play a central role in the inflammatory response by releasing mediators such as nitric oxide (NO), interleukin (IL)-6, tumor necrosis factor alpha (TNF-α), and prostaglandin E2 (PGE2). This study aimed to investigate the anti-inflammatory potential of BTEX-K, a formulation of dried red ginseng combined with alpha-galactosidase, in lipopolysaccharide (LPS)-stimulated cells. Methods: LPS-treated immune cells were used to assess the anti-inflammatory effects of BTEX-K. The levels of NO, IL-6, TNF-α, and PGE2 were measured following BTEX-K treatment. The protein expression of inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2) was evaluated. Cytotoxicity assays were conducted to determine whether the observed effects were due to cell viability loss. The involvement of MAPK signaling and NF-κB pathway modulation was examined by analyzing JNK phosphorylation, IκB degradation, and PPAR-γ expression. Results: BTEX-K significantly reduced the production of NO, IL-6, TNF-α, and PGE2 in LPS-treated cells without inducing cytotoxicity. The protein expression levels of iNOS and COX-2 were also suppressed. Furthermore, BTEX-K inhibited the LPS-induced phosphorylation of JNK in the MAPK pathway. It restored IκB levels and suppressed NF-κB activation by preventing the downregulation of PPAR-γ. Conclusions: BTEX-K demonstrates notable anti-inflammatory effects by inhibiting key inflammatory mediators and signaling pathways in immune cells. These findings support its therapeutic potential in mitigating inflammation-related symptoms, including pain, swelling, and redness, commonly seen in degenerative arthritis. Full article
(This article belongs to the Section Cell Biology and Pathology)
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19 pages, 4731 KiB  
Article
The Evaluation of Potential Anticancer Activity of Meloxicam—In Vitro Study on Amelanotic and Melanotic Melanoma
by Marta Karkoszka-Stanowska, Zuzanna Rzepka and Dorota Wrześniok
Int. J. Mol. Sci. 2025, 26(13), 5985; https://doi.org/10.3390/ijms26135985 (registering DOI) - 22 Jun 2025
Abstract
Meloxicam (MLX), a member of the non-steroidal anti-inflammatory drugs (NSAIDs), is a preferential inhibitor of cyclooxygenase-2 (COX-2) responsible for the synthesis of pro-inflammatory prostaglandins. MLX, due to its inhibition of the COX-2 enzyme, which is overexpressed in many cancers, including melanoma, leading to [...] Read more.
Meloxicam (MLX), a member of the non-steroidal anti-inflammatory drugs (NSAIDs), is a preferential inhibitor of cyclooxygenase-2 (COX-2) responsible for the synthesis of pro-inflammatory prostaglandins. MLX, due to its inhibition of the COX-2 enzyme, which is overexpressed in many cancers, including melanoma, leading to rapid growth, angiogenesis, and metastasis, represents a potentially important compound with anticancer activity. This study aimed to investigate the potential anticancer activity of meloxicam against amelanotic C32 and melanotic COLO 829 melanoma cell lines. The objective was achieved by assessing cell metabolic activity using the WST-1 assay and analyzing mitochondrial potential, levels of reduced thiols, annexin, and caspases 3/7, 8, and 9 by imaging cytometry, as well as assessing reactive oxygen species (ROS) levels using the H2DCFDA probe. The amelanotic melanoma C32 was more sensitive to MLX exposure, thus exhibiting antiproliferative effects, a disruption of redox homeostasis, a reduction in mitochondrial potential, and an induction of apoptosis. The results provide robust molecular evidence supporting the pharmacological effects of MLX, highlighting its potential as a valuable agent for in vivo melanoma treatment. Full article
(This article belongs to the Special Issue Molecular Biology of Cancer—Implications for Diagnosis and Treatment: 3rd Edition)
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29 pages, 2086 KiB  
Review
Impact of Temperature Stresses on Wheat Quality: A Focus on Starch and Protein Composition
by Pei Han, Yaping Wang and Hui Sun
Foods 2025, 14(13), 2178; https://doi.org/10.3390/foods14132178 (registering DOI) - 22 Jun 2025
Abstract
With climate change, maintaining wheat quality has become essential for the functional properties, end-use, commodity value, and nutritional benefits of wheat flour. Temperature indirectly influences wheat quality by modulating grain size, starch and protein content, and the balance between these components. This review [...] Read more.
With climate change, maintaining wheat quality has become essential for the functional properties, end-use, commodity value, and nutritional benefits of wheat flour. Temperature indirectly influences wheat quality by modulating grain size, starch and protein content, and the balance between these components. This review systematically analyzes temperature-mediated alterations in wheat grain quality, with particular emphasis on the two core components: starch and protein. Specifically, daytime warming generally increases protein content while reducing starch accumulation; however, temperatures exceeding 30 °C diminish key protein quality parameters (UPP%, Glu/Gli ratio, HMW-GS/LMW-GS ratio). Nighttime warming enhances protein quality but compromises starch content and yield potential. Conversely, under low-temperature conditions, starch content declines, whereas protein content is primarily influenced by genotypes and treated temperatures. Furthermore, the underlying mechanisms driving temperature-induced changes in wheat quality traits are discussed. However, the mechanisms of temperature effects have not been fully elucidated, and the results often vary between regions or over years. Thus, identifying conserved high/low-temperature resistance genes, QTLs, epialleles, and epiQTL, as well as developing corresponding molecular markers and epi-markers, is an urgent priority. Meanwhile, genome-editing tools such as CRISPR/Cas could serve as a powerful approach for creating new wheat germplasm with durable high/low-temperature resistance. Full article
(This article belongs to the Special Issue Advancing Research on Quality and Nutrition in Cereals and Cereal-Based Foods)
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27 pages, 4210 KiB  
Article
Efficient Fault Diagnosis of Elevator Cabin Door Drives Using Machine Learning with Data Reduction for Reliable Transmission
by Jakub Gęca, Dariusz Czerwiński, Bartosz Drzymała and Krzysztof Kolano
Appl. Sci. 2025, 15(13), 7017; https://doi.org/10.3390/app15137017 (registering DOI) - 22 Jun 2025
Abstract
This article addresses the issue of the elevator cabin door drive system failure diagnosis. The analyzed component is one of the most critical and the most vulnerable part of the entire elevator. Existing solutions in the literature include methods such as spectral analysis [...] Read more.
This article addresses the issue of the elevator cabin door drive system failure diagnosis. The analyzed component is one of the most critical and the most vulnerable part of the entire elevator. Existing solutions in the literature include methods such as spectral analysis of system vibrations, motor current signature analysis, fishbone diagrams, fault trees, multi-agent systems, image recognition, and machine learning techniques. However, there is a noticeable gap in comprehensive studies that specifically address classification of the multiple types of system components failures, class imbalance in the dataset, and the need to reduce data transmitted over the elevator’s internal bus. The developed diagnostic system measures the drive system’s parameters, processes them to reduce data, and classifies 11 device failures. This was achieved by constructing a test bench with a prototype cabin door drive system, identifying the most frequent system faults, developing a data preprocessing method that aggregates every driving cycle to one sample, reducing the transmitted data by 300 times, and using machine learning for modeling. A comparative analysis of the fault detection performance of seven different machine learning algorithms was conducted. An optimal cross-validation method and hyperparameter optimization techniques were employed to fine-tune each model, achieving a recall of over 97% and an F1 score approximately 97%. Finally, the developed data preparation method was implemented in the cabin door drive controller. Full article
(This article belongs to the Special Issue Industrial AI: Applications in Fault Detection, Diagnosis, and Prognosis—2nd Edition)
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13 pages, 593 KiB  
Article
A Secondary Analysis of Caloric Restriction and Exercise Effects on Cognitive Function in Functionally Limited Postmenopausal Women with Overweight or Obesity
by Christian W. McLaren, Rebecca L. Pearl, Glenn E. Smith and Stephen D. Anton
Nutrients 2025, 17(13), 2075; https://doi.org/10.3390/nu17132075 (registering DOI) - 22 Jun 2025
Abstract
Background: Postmenopausal women face a higher risk of obesity and related chronic diseases. While lifestyle interventions improve cardiometabolic health and physical function, their effects on cognitive function remain understudied, especially in diverse populations. This study examined the impact of a lifestyle intervention combining [...] Read more.
Background: Postmenopausal women face a higher risk of obesity and related chronic diseases. While lifestyle interventions improve cardiometabolic health and physical function, their effects on cognitive function remain understudied, especially in diverse populations. This study examined the impact of a lifestyle intervention combining caloric restriction and exercise on cognitive function in a diverse sample of postmenopausal women with overweight or obesity and functional limitations. Methods: This study represents a secondary analysis of a previously conducted pilot trial, in which 34 participants were randomly assigned to a 24-week intervention: (i) caloric restriction plus exercise (CR + E; n = 17) or (ii) educational control (EC; n = 17). In the CR + E group, participants engaged in group-based weight management focused on caloric restriction and three weekly exercise sessions, including walking and lower-body resistance training. The EC group attended monthly health education lectures. Changes in cognitive scores were assessed using the Digit Symbol Substitution Test (DSST) and the Controlled Oral Word Association (COWA) test. Additionally, we explored the correlation between changes in cognitive scores and physical function in the CR + E group. Results: In the CR + E group, DSST scores significantly improved compared to the EC group (p < 0.05). There were no significant changes in COWA scores for either group compared to their baseline value or between groups. Furthermore, changes in DSST or COWA were not significantly correlated with changes in walking speed or physical function. Conclusions: The preliminary results of this study suggest that CR + E may improve complex attention in functionally limited postmenopausal women with overweight or obesity but does not appear to significantly affect verbal fluency. Full article
(This article belongs to the Special Issue Healthy Aging Through Nutrition and Exercise)
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11 pages, 651 KiB  
Article
Prognostic Significance of Plasma Short-Chain Fatty Acid Levels in Assessing Mortality Risk in Patients with Chronic Heart Failure and Sarcopenia
by Anna V. Sokolova, Dmitrii O. Dragunov, Anastasiya V. Klimova, Yaroslav V. Golubev, Tatiana A. Shmigol, Vadim V. Negrebetsky and Gregory P. Arutyunov
Int. J. Mol. Sci. 2025, 26(13), 5984; https://doi.org/10.3390/ijms26135984 (registering DOI) - 22 Jun 2025
Abstract
Short-chain fatty acids (SCFAs) are microbial metabolites involved in immune regulation, energy metabolism, and intestinal barrier integrity. Among them, the role of hexanoic acid (C6), predominantly derived from dietary sources, remains poorly understood in chronic heart failure (CHF) and sarcopenia. A total of [...] Read more.
Short-chain fatty acids (SCFAs) are microbial metabolites involved in immune regulation, energy metabolism, and intestinal barrier integrity. Among them, the role of hexanoic acid (C6), predominantly derived from dietary sources, remains poorly understood in chronic heart failure (CHF) and sarcopenia. A total of 636 patients with confirmed CHF were screened between 2019 and 2021. Sarcopenia was diagnosed in 114 patients, with 74 meeting the inclusion criteria for analysis. Plasma levels of SCFAs—including butanoic, propanoic, isobutyric, 2- and 3-methylbutanoic, hexanoic, pentanoic, and 4-methylpentanoic acids—were measured using HPLC-MS/MS. Muscle strength, mass, and physical performance were assessed using handgrip dynamometry, bioelectrical impedance analysis, and SPPB, respectively. All patients showed elevated SCFA levels compared to reference values. Butanoic acid levels exceeded reference values by 32.8-fold, propanoic acid by 10.9-fold, and hexanoic acid by 1.09-fold. Patients with plasma hexanoic acid levels above the 50th percentile had a seven-fold increased mortality risk (OR = 7.10; 95% CI: 1.74–28.9; p < 0.01). Kaplan–Meier analysis confirmed significantly lower survival in this group (p = 0.00051). The mean left ventricular ejection fraction was 41.2 ± 7.5%, and the mean SPPB score was 6.1 ± 1.8, indicating impaired physical performance. Elevated plasma hexanoic acid is associated with poor prognosis in CHF patients with sarcopenia. These findings suggest that C6 may serve as a potential prognostic biomarker and therapeutic target in this population. Full article
(This article belongs to the Special Issue Musculoskeletal Disease: From Molecular Basis to Therapy)
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25 pages, 9677 KiB  
Article
YOLO-SEA: An Enhanced Detection Framework for Multi-Scale Maritime Targets in Complex Sea States and Adverse Weather
by Hongmei Deng, Shuaiqun Wang, Xinyao Wang, Wen Zheng and Yanli Xu
Entropy 2025, 27(7), 667; https://doi.org/10.3390/e27070667 (registering DOI) - 22 Jun 2025
Abstract
Maritime object detection is essential for resource monitoring, maritime defense, and public safety, yet detecting diverse targets beyond ships remains challenging. This paper presents YOLO-SEA, an efficient detection framework based on the enhanced YOLOv8 architecture. The model incorporates the SESA (SimAM-Enhanced SENetV2 Attention) [...] Read more.
Maritime object detection is essential for resource monitoring, maritime defense, and public safety, yet detecting diverse targets beyond ships remains challenging. This paper presents YOLO-SEA, an efficient detection framework based on the enhanced YOLOv8 architecture. The model incorporates the SESA (SimAM-Enhanced SENetV2 Attention) module, which integrates the channel-adaptive weight adjustment of SENetV2 with the parameter-free spatial-channel modeling of SimAM to enhance feature representation. An improved BiFPN (Bidirectional Feature Pyramid Network) structure enhances multi-scale fusion, particularly for small object detection. In the post-processing stage, Soft-NMS (Soft Non-Maximum Suppression) replaces traditional NMS to reduce false suppression in dense scenes. YOLO-SEA detects eight maritime object types. Experiments show it achieves a 5.8% improvement in mAP@0.5 and 7.2% improvement in mAP@0.5:0.95 over the baseline, demonstrating enhanced accuracy and robustness in complex marine environments. Full article
(This article belongs to the Special Issue Space-Air-Ground-Sea Integrated Communication Networks)
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9 pages, 200 KiB  
Article
Use of Cangrelor in Patients Undergoing Percutaneous Coronary Intervention: Insights and Outcomes from District General Hospital
by Ibrahim Antoun, Sotirios Dardas, Falik Sher, Mueed Akram, Navid Munir, Georgia R. Layton, Mustafa Zakkar, Kamal Chitkara, Riyaz Somani and Andre Ng
Hearts 2025, 6(3), 16; https://doi.org/10.3390/hearts6030016 (registering DOI) - 22 Jun 2025
Abstract
Background/Objectives: Cangrelor, an intravenous P2Y12 inhibitor, is increasingly used during percutaneous coronary intervention (PCI) for rapid and reversible platelet inhibition in patients unable to take oral antiplatelet agents, particularly in emergencies such as ST-elevation myocardial infarction (STEMI), cardiac arrest, or cardiogenic shock. [...] Read more.
Background/Objectives: Cangrelor, an intravenous P2Y12 inhibitor, is increasingly used during percutaneous coronary intervention (PCI) for rapid and reversible platelet inhibition in patients unable to take oral antiplatelet agents, particularly in emergencies such as ST-elevation myocardial infarction (STEMI), cardiac arrest, or cardiogenic shock. This single-centre study evaluates cangrelor and outcomes in a non-surgical centre. Methods: Between June 2017 and December 2021, all the patients for whom cangrelor was used at a district general hospital (DGH) in the UK were included in this study. Data collection included baseline characteristics, admission, procedural details, and patient outcomes. The primary outcome was a composite of all-cause mortality, bleeding, and cardiovascular events, including myocardial infarction, stent thrombosis, and stroke, within 48 h. Secondary outcomes included predictors of the composite outcome at 48 h. Results: During the study period, cangrelor was administered peri-procedurally to 93 patients. Males comprised 85% of the patients; the mean age was 65.5 ± 10.6 years. A total of 1 patient (1.1%) had a cardiovascular event within 48 h of cangrelor administration, whereas all-cause mortality occurred in 17 patients (18%) within 48 h. No major bleeding events were noted at 48 h following cangrelor administration. Regression analysis did not find predictors of composite outcomes at 48 h. Conclusions: Cangrelor offers a potential alternative to oral P2Y12 inhibitors in specific high-risk scenarios. Further research is needed to validate its role in broader populations. Full article
19 pages, 2831 KiB  
Article
High Spatial Resolution Soil Moisture Mapping over Agricultural Field Integrating SMAP, IMERG, and Sentinel-1 Data in Machine Learning Models
by Diego Tola, Lautaro Bustillos, Fanny Arragan, Rene Chipana, Renaud Hostache, Eléonore Resongles, Raúl Espinoza-Villar, Ramiro Pillco Zolá, Elvis Uscamayta, Mayra Perez-Flores and Frédéric Satgé
Remote Sens. 2025, 17(13), 2129; https://doi.org/10.3390/rs17132129 (registering DOI) - 21 Jun 2025
Abstract
Soil moisture content (SMC) is a critical parameter for agricultural productivity, particularly in semi-arid regions, where irrigation practices are extensively used to offset water deficits and ensure decent yields. Yet, the socio-economic and remote context of these regions prevents sufficiently dense SMC monitoring [...] Read more.
Soil moisture content (SMC) is a critical parameter for agricultural productivity, particularly in semi-arid regions, where irrigation practices are extensively used to offset water deficits and ensure decent yields. Yet, the socio-economic and remote context of these regions prevents sufficiently dense SMC monitoring in space and time to support farmers in their work to avoid unsustainable irrigation practices and preserve water resource availability. In this context, our study addresses the challenge of high spatial resolution (i.e., 20 m) SMC estimation by integrating remote sensing datasets in machine learning models. For this purpose, a dataset made of 166 soil samples’ SMC along with corresponding SMC, precipitation, and radar signal derived from Soil Moisture Active Passive (SMAP), Integrated Multi-satellitE Retrievals for GPM (IMERG), and Sentinel-1 (S1), respectively, was used to assess four machine learning models’ (Decision Tree—DT, Random Forest—RF, Gradient Boosting—GB, Extreme Gradient Boosting—XGB) reliability for SMC mapping. First, each model was trained/validated using only the coarse spatial resolution (i.e., 10 km) SMAP SMC and IMERG precipitation estimates as independent features, and, second, S1 information (i.e., 20 m) derived from single scenes and/or composite images was added as independent features to highlight the benefit of information (i.e., S1 information) for SMC mapping at high spatial resolution (i.e., 20 m). Results show that integrating S1 information from both single scenes and composite images to SMAP SMC and IMERG precipitation data significantly improves model reliability, as R2 increased by 12% to 16%, while RMSE decreased by 10% to 18%, depending on the considered model (i.e., RF, XGB, DT, GB). Overall, all models provided reliable SMC estimates at 20 m spatial resolution, with the GB model performing the best (R2 = 0.86, RMSE = 2.55%). Full article
(This article belongs to the Special Issue Remote Sensing for Soil Properties and Plant Ecosystems)
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16 pages, 1587 KiB  
Article
Role of Mediterranean Diet and Ultra-Processed Foods on Sperm Parameters: Data from a Cross-Sectional Study
by Gabriel Cosmin Petre, Francesco Francini-Pesenti, Luca De Toni, Andrea Di Nisio, Asia Mingardi, Ilaria Cosci, Nicola Passerin, Alberto Ferlin and Andrea Garolla
Nutrients 2025, 17(13), 2066; https://doi.org/10.3390/nu17132066 (registering DOI) - 21 Jun 2025
Abstract
Background/Objectives: Male infertility is multifactorial, involving genetic, environmental, lifestyle, and medical factors. Recent research has underscored the influence of lifestyle choices, such as dietary habits, smoking, alcohol abuse, and metabolic disturbances, on sperm quality. In this context, nutrition plays a pivotal role: adherence [...] Read more.
Background/Objectives: Male infertility is multifactorial, involving genetic, environmental, lifestyle, and medical factors. Recent research has underscored the influence of lifestyle choices, such as dietary habits, smoking, alcohol abuse, and metabolic disturbances, on sperm quality. In this context, nutrition plays a pivotal role: adherence to a healthy diet like the Mediterranean Diet (MD), which emphasizes seasonal, fresh, and whole foods, has been linked to improved sperm performance. Conversely, a high intake of ultra-processed foods (UPFs), characterized by additives, high levels of sugars, fats, and salt, and a nutrient-poor profile, may impair sperm quality. Methods: Based on data supporting the reproductive health benefits of the MD, this observational cross-sectional study aimed at evaluating the possible relationship between MD adherence, assessed using the 14-point a priori Mediterranean Diet Adherence Screener (MEDAS) and intake of ultra-processed foods (UPFs), based on the NOVA classification, and sperm quality in 358 individuals (mean age 34.6 ± 9.3 years) who spontaneously referred to our center of reproductive medicine. Semen analyses were performed according to the WHO 2021 criteria. Hormonal profiles (FSH, LH, testosterone, SHBG, bioavailable testosterone, and calculated free testosterone) were also determined. Results: MD adherence score was significantly and positively correlated with semen parameters, whilst negatively correlated with FSH and LH levels. In contrast, UPF intake was correlated with poor semen parameters, whilst no association was observed with hormonal levels. Multivariate analyses confirmed these associations and showed the independency from age and BMI. Notably, among men with FSH levels < 8 IU/mL, higher quartiles of UPF intake had lower markers of sperm quality, particularly for viability and typical morphology. Differently, high MD adherence scores were associated with high quality sperm parameters even when FSH levels were >8 IU/mL. Conclusions: This study provides evidence that the adherence to MD, and conversely reduced intake of ultra-processed foods, is associates with a better semen profile. These findings suggest the possible role of dietary interventions as a modifiable factor in the management of male infertility. Full article
(This article belongs to the Section Nutrition and Public Health)
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13 pages, 989 KiB  
Article
Multi-Algorithm Feature Extraction from Dual Sections for the Recognition of Three African Redwoods
by Jiawen Sun, Jiashun Niu, Liren Xu, Jianping Sun and Linhong Zhao
Forests 2025, 16(7), 1043; https://doi.org/10.3390/f16071043 (registering DOI) - 21 Jun 2025
Abstract
To address the persistent challenge of low recognition accuracy in precious wood species classification, this study proposes a novel methodology for identifying Pterocarpus santalinus, Pterocarpus tinctorius (PTD), and Pterocarpus tinctorius (Zambia). This approach synergistically integrates artificial neural networks (ANNs) with advanced image feature [...] Read more.
To address the persistent challenge of low recognition accuracy in precious wood species classification, this study proposes a novel methodology for identifying Pterocarpus santalinus, Pterocarpus tinctorius (PTD), and Pterocarpus tinctorius (Zambia). This approach synergistically integrates artificial neural networks (ANNs) with advanced image feature extraction techniques, specifically Fast Fourier Transform, Gabor Transform, Wavelet Transform, and Gray-Level Co-occurrence Matrix. Features were extracted from both transverse and longitudinal wood sections. Fifteen distinct ANN models were subsequently developed: hybrid-section models combined features from different sections using a single algorithm, while multi-algorithm models aggregated features from the same section across all four algorithms. The dual-section hybrid wavelet model (LC4) demonstrated superior performance, achieving a perfect 100% recognition accuracy. High accuracies were also observed in the four-parameter combination models for longitudinal (L5) and transverse (C5) sections, yielding 97.62% and 91.67%, respectively. Notably, 92.31% of the LC4 model’s test samples exhibited an absolute error of ≤1%, highlighting its high reliability and precision. These findings confirm the efficacy of integrating image processing with neural networks for fine-grained wood identification and underscore the exceptional discriminative power of wavelet-based features in cross-sectional data fusion. Full article
(This article belongs to the Section Wood Science and Forest Products)
18 pages, 4829 KiB  
Article
A Chinese Herbal Compound Fertilizer Improved the Soil Bacterial Community and Promoted the Quality of Chrysanthemum morifolium ‘Huangju’
by Hongliang Li, Hongyao Qu, Huaqiang Xuan, Bei Liu, Lixiang Zhu, Xianchao Shang, Yi Xie, Li Zhang, Long Yang, Ling Yuan, Sitakanta Pattanaik, Li Xiang and Xin Hou
Agronomy 2025, 15(7), 1512; https://doi.org/10.3390/agronomy15071512 (registering DOI) - 21 Jun 2025
Abstract
Abstract [...] Full article
(This article belongs to the Section Innovative Cropping Systems)
25 pages, 2021 KiB  
Article
Price Forecasting of Crude Oil Using Hybrid Machine Learning Models
by Jyoti Choudhary, Haresh Kumar Sharma, Pradeep Malik and Saibal Majumder
J. Risk Financial Manag. 2025, 18(7), 346; https://doi.org/10.3390/jrfm18070346 (registering DOI) - 21 Jun 2025
Abstract
Crude oil is a widely recognized, indispensable global and national economic resource. It is significantly susceptible to the boundless fluctuations attributed to various variables. Despite its capacity to sustain the global economic framework, the embedded uncertainties correlated with the crude oil markets present [...] Read more.
Crude oil is a widely recognized, indispensable global and national economic resource. It is significantly susceptible to the boundless fluctuations attributed to various variables. Despite its capacity to sustain the global economic framework, the embedded uncertainties correlated with the crude oil markets present formidable challenges that investors must diligently navigate. In this research, we propose a hybrid machine learning model based on random forest (RF), gated recurrent unit (GRU), conventional neural network (CNN), extreme gradient boosting (XGBoost), functional partial least squares (FPLS), and stacking. This hybrid model facilitates the decision-making process related to the import and export of crude oil in India. The precision and reliability of the different machine learning models utilized in this study were validated through rigorous evaluation using various error metrics, ensuring a thorough assessment of their forecasting capabilities. The conclusive results revealed that the proposed hybrid ensemble model consistently delivered effective and robust predictions compared to the individual models. Full article
(This article belongs to the Section Mathematics and Finance)
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26 pages, 9007 KiB  
Article
AI and Deep Learning for Image-Based Segmentation of Ancient Masonry: A Digital Methodology for Mensiochronology of Roman Brick
by Lorenzo Fornaciari
Heritage 2025, 8(7), 241; https://doi.org/10.3390/heritage8070241 (registering DOI) - 21 Jun 2025
Abstract
Abstract [...] Full article
(This article belongs to the Special Issue AI and the Future of Cultural Heritage)
16 pages, 978 KiB  
Article
Sex-Specific Associations of Childhood BMI Patterns with Cardiometabolic Risk: An 11-Year Korean Longitudinal Study
by Hyo-Jin Kim, Sarang Jeong, Joo Hyun Lim and Dankyu Yoon
Children 2025, 12(7), 821; https://doi.org/10.3390/children12070821 (registering DOI) - 21 Jun 2025
Abstract
Background/Objectives: Childhood overweight/obesity status is a critical risk factor for adverse cardiometabolic outcomes. We aimed to evaluate the sex-specific associations between a maintained childhood overweight status and late-adolescent cardiometabolic risk factors using data from a Korean longitudinal study. Methods: We used data [...] Read more.
Background/Objectives: Childhood overweight/obesity status is a critical risk factor for adverse cardiometabolic outcomes. We aimed to evaluate the sex-specific associations between a maintained childhood overweight status and late-adolescent cardiometabolic risk factors using data from a Korean longitudinal study. Methods: We used data from the Korean Children-Adolescents Study, a prospective cohort of children enrolled at age 7 and followed annually from 2005 to 2020. Among participants who were followed at least once, a total of 899 children (438 boys, 461 girls) with consistent body mass index (BMI) status at ages 7–9 and 10–12 were included in the analysis. Participants were categorized into two groups on the basis of BMI: normal weight maintenance and overweight maintenance. Multivariable linear regression was used to examine the associations between BMI patterns and cardiometabolic risk factors, with adjustments for covariates. Results: Among the 899 children (mean age: 7.1 ± 0.4 years, 48.7% boys), 12.8% of boys and 5.9% of girls were classified into the overweight maintenance group. Boys in the overweight maintenance group had significantly greater BMIs, waist circumferences (WC), body fat percentages, trunk fat mass, and aspartate aminotransferase and alanine aminotransferase levels at ages 15 and 18. Girls in the same group had elevated BMI, WC, body fat percentage, trunk fat mass, and blood pressure and experienced earlier pubertal onset. Conclusions: Maintaining an overweight status during childhood is associated with adverse cardiometabolic profiles in adolescence, with sex-specific differences. These findings highlight the importance of early, sex-specific interventions to prevent long-term health risks associated with childhood obesity. Full article
(This article belongs to the Section Pediatric Endocrinology & Diabetes)
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14 pages, 642 KiB  
Article
Point-of-Care Ultrasound Within One Hour Associated with ED Flow and Resource Use in Non-Traumatic Abdominal Pain: A Retrospective Observational Study
by Sheng-Yao Hung, Fen-Wei Huang, Wan-Ching Lien, Te-Fa Chiu, Tse-Chyuan Wong, Wei-Jun Lin and Shih-Hao Wu
Diagnostics 2025, 15(13), 1580; https://doi.org/10.3390/diagnostics15131580 (registering DOI) - 21 Jun 2025
Abstract
Background: Although the value of point-of-care ultrasound (PoCUS) is well-established for specific diseases and in the hands of trained users, its broader impact on overall ED efficiency is not yet fully known. This study aims to evaluate the association of early PoCUS, [...] Read more.
Background: Although the value of point-of-care ultrasound (PoCUS) is well-established for specific diseases and in the hands of trained users, its broader impact on overall ED efficiency is not yet fully known. This study aims to evaluate the association of early PoCUS, performed within 1 h of presentation, with ED patient flow, healthcare resource utilization, and quality of care in adults with non-traumatic abdominal pain. Method: This retrospective cohort study included 44,863 adult patients (≥18 years) presenting with non-traumatic abdominal pain from January 2021 to December 2023. Patients were grouped into PoCUS and no-PoCUS categories, with a subgroup analysis for those receiving PoCUS within 1 h, to evaluate ED LOS, and costs for different ED dispositions. Outcomes measured included hospital LOS, costs, mortality, and ICU admission. Results: The mean age of the subjects was 44.4 ± 17.9 years, and 61.2% were female. PoCUS was performed in 39.7% of cases, with 69.6% of these conducted within one hour. Additionally, 30.5% underwent CT. The PoCUS group had a significantly shorter ED LOS compared to the no-PoCUS group among patients admitted to general wards (p < 0.001), but not in outpatient dispositions (p = 0.282) or ICU admissions (p = 0.081). Subgroup analysis of patients receiving PoCUS within 1 h showed a significantly shorter LOS for both outpatient dispositions (p < 0.001) and general ward admissions (p < 0.001), with no effect on ICU admissions (p = 0.869). The presence or absence of CT did not alter these findings. Multivariable analysis indicated that patients who received PoCUS within one hour alone at index visit and admitted after an unscheduled return visit had lower initial ED costs (−9436.1 TWD, p < 0.001) and shorter ED LOS (−11.59 min, p < 0.001) than patients admitted directly at the index visit, with no significant increase in total resource utilization or adverse outcomes after return visits. Conclusions: PoCUS, especially when performed within one hour, was associated with reduced ED LOS and healthcare resource utilization for both outpatient dispositions and inpatient admissions without compromising patient safety or quality of care. Full article
(This article belongs to the Special Issue The Utility of Ultrasound in Emergency Medicine)
25 pages, 16836 KiB  
Article
Indoor Dynamic Environment Mapping Based on Semantic Fusion and Hierarchical Filtering
by Yiming Li, Luying Na, Xianpu Liang and Qi An
ISPRS Int. J. Geo-Inf. 2025, 14(7), 236; https://doi.org/10.3390/ijgi14070236 (registering DOI) - 21 Jun 2025
Abstract
To address the challenges of dynamic object interference and redundant information representation in map construction for indoor dynamic environments, this paper proposes an indoor dynamic environment mapping method based on semantic fusion and hierarchical filtering. First, prior dynamic object masks are obtained using [...] Read more.
To address the challenges of dynamic object interference and redundant information representation in map construction for indoor dynamic environments, this paper proposes an indoor dynamic environment mapping method based on semantic fusion and hierarchical filtering. First, prior dynamic object masks are obtained using the YOLOv8 model, and geometric constraints between prior static objects and dynamic regions are introduced to identify non-prior dynamic objects, thereby eliminating all dynamic features (both prior and non-prior). Second, an initial semantic point cloud map is constructed by integrating prior static features from a semantic segmentation network with pose estimates from an RGB-D camera. Dynamic noise is then removed using statistical outlier removal (SOR) filtering, while voxel filtering optimizes point cloud density, generating a compact yet texture-rich semantic dense point cloud map with minimal dynamic artifacts. Subsequently, a multi-resolution semantic octree map is built using a recursive spatial partitioning algorithm. Finally, point cloud poses are corrected via Transform Frame (TF) transformation, and a 2D traversability grid map is generated using passthrough filtering and grid projection. Experimental results demonstrate that the proposed method constructs multi-level semantic maps with rich information, clear structure, and high reliability in indoor dynamic scenarios. Additionally, the map file size is compressed by 50–80%, significantly enhancing the reliability of mobile robot navigation and the efficiency of path planning. Full article
(This article belongs to the Special Issue Indoor Mobile Mapping and Location-Based Knowledge Services)
26 pages, 34279 KiB  
Article
Complementary Local–Global Optimization for Few-Shot Object Detection in Remote Sensing
by Yutong Zhang, Xin Lyu, Xin Li, Siqi Zhou, Yiwei Fang, Chenlong Ding, Shengkai Gao and Jiale Chen
Remote Sens. 2025, 17(13), 2136; https://doi.org/10.3390/rs17132136 (registering DOI) - 21 Jun 2025
Abstract
Few-shot object detection (FSOD) in remote sensing remains challenging due to the scarcity of annotated samples and the complex background environments in aerial images. Existing methods often struggle to capture fine-grained local features or suffer from bias during global adaptation to novel categories, [...] Read more.
Few-shot object detection (FSOD) in remote sensing remains challenging due to the scarcity of annotated samples and the complex background environments in aerial images. Existing methods often struggle to capture fine-grained local features or suffer from bias during global adaptation to novel categories, leading to misclassification as background. To address these issues, we propose a framework that simultaneously enhances local feature learning and global feature adaptation. Specifically, we design an Extensible Local Feature Aggregator Module (ELFAM) that reconstructs object structures via multi-scale recursive attention aggregation. We further introduce a Self-Guided Novel Adaptation (SGNA) module that employs a teacher-student collaborative strategy to generate high-quality pseudo-labels, thereby refining the semantic feature distribution of novel categories. In addition, a Teacher-Guided Dual-Branch Head (TG-DH) is developed to supervise both classification and regression using pseudo-labels generated by the teacher model to further stabilize and enhance the semantic features of novel classes. Extensive experiments on DlOR and iSAlD datasets demonstrate that our method achieves superior performance compared to existing state-of-the-art FSOD approaches and simultaneously validate the effectiveness of all proposed components. Full article
(This article belongs to the Special Issue Efficient Object Detection Based on Remote Sensing Images)
28 pages, 7335 KiB  
Article
Research on Reservoir Identification of Gas Hydrates with Well Logging Data Based on Machine Learning in Marine Areas: A Case Study from IODP Expedition 311
by Xudong Hu, Wangfeng Leng, Kun Xiao, Guo Song, Yiming Wei and Changchun Zou
J. Mar. Sci. Eng. 2025, 13(7), 1208; https://doi.org/10.3390/jmse13071208 (registering DOI) - 21 Jun 2025
Abstract
Natural gas hydrates, with their efficient and clean energy characteristics, are deemed a significant pillar within the future energy sector, and their resource quantification and development have a profound impact on the transformation of global energy structure. However, how to accurately identify gas [...] Read more.
Natural gas hydrates, with their efficient and clean energy characteristics, are deemed a significant pillar within the future energy sector, and their resource quantification and development have a profound impact on the transformation of global energy structure. However, how to accurately identify gas hydrate reservoirs (GHRs) is currently a hot research topic. This study explores the logging identification method of marine GHRs based on machine learning (ML) according to the logging data of the International Ocean Drilling Program (IODP) Expedition 311. This article selects six ML methods, including Gaussian process classification (GPC), support vector machine (SVM), multilayer perceptron (MLP), random forest (RF), extreme gradient boosting (XGBoost), and logistic regression (LR). The internal relationship between logging data and hydrate reservoir is analyzed through six ML algorithms. The results show that the constructed ML model performs well in gas hydrate reservoir identification. Among them, RF has the highest accuracy, precision, recall, and harmonic mean of precision and recall (F1 score), all of which are above 0.90. With an area under curve (AUC) of nearly 1 for RF, it is confirmed that ML technology is effective in this area. Research has shown that ML provides an alternative method for quickly and efficiently identifying GHRs based on well logging data and also offers a scientific foundation and technical backup for the future prospecting and mining of natural gas hydrates. Full article
(This article belongs to the Special Issue Advances in Marine Gas Hydrate Exploration and Discovery—2nd Edition)
24 pages, 1625 KiB  
Article
Circular Economy Strategy Selection Through a Digital Twin Approach
by Marta Rinaldi, Mario Caterino, Marcello Fera, Raffaele Abbate, Umberto Daniele and Roberto Macchiaroli
Appl. Sci. 2025, 15(13), 7016; https://doi.org/10.3390/app15137016 (registering DOI) - 21 Jun 2025
Abstract
This study investigated the impact of different reverse logistics strategies on the economic and environmental performance of a system within the rubber flooring sector. A simulation tool was developed to replicate the behavior of a real production system, focusing on the transition from [...] Read more.
This study investigated the impact of different reverse logistics strategies on the economic and environmental performance of a system within the rubber flooring sector. A simulation tool was developed to replicate the behavior of a real production system, focusing on the transition from linear to circular processes. By considering multiple factors influencing system performance, this research offers an overview of the sustainability of various RL strategies and provides realistic estimates for different scenarios. Three key factors were used to evaluate each strategy’s response: transportation distance, flooring thickness, and returned flooring quality. The findings suggest that an environmental advantage generally favors on-site inspections at the customer’s location to assess the returned product’s condition, regardless of distance. However, centralizing inspections at the manufacturer’s facility is more economically advantageous when distances are short, particularly when the company prioritizes recycling over other circular economy practices. Based on these results, practical implications and guidelines are proposed to help companies balance cost-effectiveness with sustainability, optimizing their operations within a circular economy framework. Full article
(This article belongs to the Special Issue Sustainability and Green Supply Chain Management in Industrial Fields)
15 pages, 664 KiB  
Article
A Bio-Inspired Optimization Approach for Low-Carbon Dispatch in EV-Integrated Virtual Power Plants
by Renfei Gao, Kunze Song, Bijiang Zhu and Hongbo Zou
Processes 2025, 13(7), 1969; https://doi.org/10.3390/pr13071969 (registering DOI) - 21 Jun 2025
Abstract
With the increasing penetration of renewable energy and the large-scale integration of electric vehicles (EVs), the economic optimization dispatch of EV-integrated virtual power plants (VPPs) faces multiple uncertainties and challenges. This paper first proposes an optimized dispatching model for EV clusters to form [...] Read more.
With the increasing penetration of renewable energy and the large-scale integration of electric vehicles (EVs), the economic optimization dispatch of EV-integrated virtual power plants (VPPs) faces multiple uncertainties and challenges. This paper first proposes an optimized dispatching model for EV clusters to form large-scale coordinated regulation capabilities. Subsequently, considering diversified resources such as energy storage systems and photovoltaic (PV) generation within VPPs, a low-carbon economic optimization dispatching model is established to minimize the total system operation costs and polluted gas emissions. To address the limitations of traditional algorithms in solving high-dimensional, nonlinear dispatching problems, this paper introduces a plant root-inspired growth optimization algorithm. By simulating the nutrient-adaptive uptake mechanism and branching expansion strategy of plant roots, the algorithm achieves a balance between global optimization and local fine-grained search. Compared with the genetic algorithm, particle swarm optimization algorithm and bat algorithm, simulation results demonstrate that the proposed method can effectively enhance the low-carbon operational economy of VPPs with high PV, ESS, and EV penetration. The research findings provide theoretical support and practical references for optimal dispatch of multi-stakeholder VPPs. Full article
(This article belongs to the Special Issue Advances in Smart Grids and Microgrids: Distributed Generation and Energy Storage Systems)
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15 pages, 242 KiB  
Article
Efficacy of Dual Hormonal Therapy with Fulvestrant and Aromatase Inhibitors as Neoadjuvant Endocrine Treatment for Locally Advanced Breast Cancer
by Ana Majić, Žarko Bajić, Marija Ban, Ivana Tica Sedlar, Dora Čerina Pavlinović, Branka Petrić Miše, Ante Strikić, Snježana Tomić and Eduard Vrdoljak
Cancers 2025, 17(13), 2083; https://doi.org/10.3390/cancers17132083 (registering DOI) - 21 Jun 2025
Abstract
Background: The role of neoadjuvant endocrine therapy (NET) in patients with luminal tumors is still not well defined in everyday clinical practice. To assess the efficacy of combination NET, we analyzed the outcomes of fulvestrant and aromatase inhibitors (AI) in combination in [...] Read more.
Background: The role of neoadjuvant endocrine therapy (NET) in patients with luminal tumors is still not well defined in everyday clinical practice. To assess the efficacy of combination NET, we analyzed the outcomes of fulvestrant and aromatase inhibitors (AI) in combination in a real-world population. Methods: This was a single-arm, retrospective longitudinal study of the total population of patients diagnosed with locoregionally advanced, clinical stage II-III, HR+ HER2-, luminal-type eBC, who were treated with the neoadjuvant combination of fulvestrant and AI between 2019 and 2024 at the Clinical University Hospital of Split, Croatia. Results: We enrolled 44 patients in the intention-to-treat (ITT) population, while 34 completed NET and surgery (per-protocol population; PPP). The median duration of NET was 11 months (interquartile range [IQR] of 9–16 months). The best radiological objective response rate (partial or complete response) was achieved by 30 (68.2%) in ITT, and 26 (76.5%) in PPP, defined by radiological examination, breast ultrasound, or MR. In the PPP, the minimal or moderate pathological response according to residual cancer burden (I or II) was observed in 29 (85.3%) patients. The median of absolute changes in Ki-67 was −5 (95% CI: −9 to 0), and the median of relative Ki67 changes was −40% (95% CI: −72% to 0%). Post-surgical Ki-67 was significantly predicted by initial Ki-67, positive lymph nodes, and time from diagnosis to the initiation of NET. Treatment was well tolerated, with no therapy discontinuation or dose reductions needed due to toxicity. The most commonly reported side effects included musculoskeletal pain (45.5%), asthenia (34.1%), and hot flashes (29.5%). Conclusions: Dual hormonal therapy with fulvestrant and AI is an active, easily given, non-toxic, promising neoadjuvant treatment in real-world patients with locally advanced luminal-type eBC who are not candidates for chemotherapy. Full article
(This article belongs to the Section Cancer Therapy)
10 pages, 1282 KiB  
Article
Long-Term Results of Single- and Multi-Incision Minimally Invasive Esophagectomy for Esophageal Cancer: Experience of 348 Cases
by Yung-Hsin Chen, Pei-Ming Huang, Ke-Cheng Chen and Jang-Ming Lee
Biomedicines 2025, 13(7), 1523; https://doi.org/10.3390/biomedicines13071523 (registering DOI) - 21 Jun 2025
Abstract
Importance: While minimally invasive esophagectomy is currently accepted as an effective treatment for patients with esophageal cancer, the long-term survival outcomes of single-incision minimally invasive esophagectomy in these patients are still unknown, particularly when compared to those of the more invasive multi-incision minimally [...] Read more.
Importance: While minimally invasive esophagectomy is currently accepted as an effective treatment for patients with esophageal cancer, the long-term survival outcomes of single-incision minimally invasive esophagectomy in these patients are still unknown, particularly when compared to those of the more invasive multi-incision minimally invasive esophagectomy. Objective: To determine the long-term oncological outcomes of single-incision minimally invasive esophagectomy in patients with esophageal cancer and to compare these outcomes with those of multi-incision minimally invasive esophagectomy. Design: This was a prospective, randomized, and propensity score-matched study wherein we analyzed patients who underwent treatment from February 2005 to May 2022. Setting: Our study was carried out by a single surgical team in a tertiary medical center. Participants: We analyzed 348 patients with esophageal cancer who underwent single-incision minimally invasive esophagectomy and 469 who underwent multi-incision minimally invasive esophagectomy. Main Outcomes and Measures: We aimed to determine the long-term survival outcomes of single-incision minimally invasive esophagectomy and compare these to those of multi-incision minimally invasive esophagectomy in our study population, and further conducted a propensity score-matching (n = 251 in each arm) study. Results: The disease progression-free (DFS) and overall survival (OS) rates of patients who underwent single-incision minimally invasive esophagectomy (SIMIE) was significantly better than that of those who underwent by multi-incision minimally invasive esophagectomy (MIMIE) (p = 0.024 for OS and p = 0.027 for PFS). This trend of difference was observed in the subsequent propensity-score matching analysis (p = 0.009 and 0.016 for OS and PFS, respectively). Conclusions and Relevance: The single-incision technique applied in minimally invasive esophagectomy to treat esophageal cancer is feasible without compromising the patient’s long-term oncological outcome, as opposed to that applied using multi-incision minimally invasive esophagectomy. Full article
(This article belongs to the Section Cancer Biology and Oncology)
34 pages, 3346 KiB  
Article
A Riemannian Dichotomizer Approach on Symmetric Positive Definite Manifolds for Offline, Writer-Independent Signature Verification
by Nikolaos Vasilakis, Christos Chorianopoulos and Elias N. Zois
Appl. Sci. 2025, 15(13), 7015; https://doi.org/10.3390/app15137015 (registering DOI) - 21 Jun 2025
Abstract
Automated handwritten signature verification continues to pose significant challenges. A common approach for developing writer-independent signature verifiers involves the use of a dichotomizer, a function that generates a dissimilarity vector with the differences between similar and dissimilar pairs of signature descriptors as components. [...] Read more.
Automated handwritten signature verification continues to pose significant challenges. A common approach for developing writer-independent signature verifiers involves the use of a dichotomizer, a function that generates a dissimilarity vector with the differences between similar and dissimilar pairs of signature descriptors as components. The Dichotomy Transform was applied within a Euclidean or vector space context, where vectored representations of handwritten signatures were embedded in and conformed to Euclidean geometry. Recent advances in computer vision indicate that image representations to the Riemannian Symmetric Positive Definite (SPD) manifolds outperform vector space representations. In offline signature verification, both writer-dependent and writer-independent systems have recently begun leveraging Riemannian frameworks in the space of SPD matrices, demonstrating notable success. This work introduces, for the first time in the signature verification literature, a Riemannian dichotomizer employing Riemannian dissimilarity vectors (RDVs). The proposed framework explores a number of local and global (or common pole) topologies, as well as simple serial and parallel fusion strategies for RDVs for constructing robust models. Experiments were conducted on five popular signature datasets of Western and Asian origin, using blind intra- and cross-lingual experimental protocols. The results indicate the discriminative capabilities of the proposed Riemannian dichotomizer framework, which can be compared to other state-of-the-art and computationally demanding architectures. Full article
(This article belongs to the Special Issue Applications of Image Processing and Pattern Recognition in Biometrics)
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