Search Results (823)

Cholangiocarcinoma (CCA) is highly prevalent in the Greater Mekong sub-region, especially northeastern Thailand, where infection with the liver fluke Opisthorchis viverrini is a major etiological factor. Limited therapeutic options and the absence of reliable early diagnosis tools impede effective disease control. Atractylodes lancea (Thunb.) DC.—long used in Thai and East Asian medicine, contains atractylodin (ATD), a potent bioactive compound with anticancer potential. Here, we developed ATD-loaded poly(lactic co-glycolic acid) nanoparticles (ATD PLGA NPs) and evaluated their antitumor efficacy against CCA. The formulated nanoparticles had a mean diameter of 229.8 nm, an encapsulation efficiency of 83%, and exhibited biphasic, sustained release, reaching a cumulative release of 92% within seven days. In vitro, ATD-PLGA NPs selectively reduced the viability of CL-6 and HuCCT-1 CCA cell lines, with selectivity indices (SI) of 3.53 and 2.61, respectively, outperforming free ATD and 5-fluorouracil (5-FU). They suppressed CL-6 cell migration and invasion by up to 90% within 12 h and induced apoptosis in 83% of cells through caspase-3/7 activation. Micronucleus assays showed lower mutagenic potential than the positive control. In vivo, ATD-PLGA NPs dose-dependently inhibited tumor growth and prolonged survival in CCA-xenografted nude mice; the high-dose regimen matched or exceeded the efficacy of 5-FU. Gene expression analysis revealed significant downregulation of pro-tumorigenic factors (VEGF, MMP-9, TGF-β, TNF-α, COX-2, PGE₂, and IL-6) and upregulation of the anti-inflammatory cytokine IL-10. Collectively, these results indicate that ATD-PLGA NPs are a promising nanotherapeutic platform for targeted CCA treatment, offering improved anticancer potency, selectivity, and safety compared to conventional therapies. Full article

Internet of Things (IoT)-based solar insecticidal lamps (SIL-IoTs) offer an eco-friendly alternative by merging solar energy harvesting with intelligent sensing, advancing sustainable smart agriculture. However, SIL-IoTs encounter practical challenges, e.g., hardware aging, electromagnetic interference, and abnormal data patterns. Therefore, developing an effective fault detection and diagnosis (FDD) system is essential. In this survey, we systematically identify and address the core challenges of implementing FDD of SIL-IoTs. Firstly, the fuzzy boundaries of sample features lead to complex feature interactions that increase the difficulty of accurate FDD. Secondly, the category imbalance in the fault samples limits the generalizability of the FDD models. Thirdly, models trained on single scenarios struggle to adapt to diverse and dynamic field conditions. To overcome these challenges, we propose a multi-level solution by discussing and merging existing FDD methods: (1) a data augmentation strategy can be adopted to improve model performance on small-sample datasets; (2) federated learning (FL) can be employed to enhance adaptability to heterogeneous environments, while transfer learning (TL) addresses data scarcity; and (3) deep learning techniques can be used to reduce dependence on labeled data; these methods provide a robust framework for intelligent and adaptive FDD of SIL-IoTs, supporting long-term reliability of IoT devices in smart agriculture. Full article

Introduction: Subcutaneous panniculitis-like T-cell lymphoma (SPTCL) is a very rare subtype of cutaneous T-cell lymphoma. It is characterized by the neoplastic infiltration of subcutaneous adipose tissue. Its clinical presentation, including subcutaneous nodules, fever, and systemic symptoms, often mimics inflammatory panniculitis, making diagnosis difficult. Case Presentation: This case report describes a 14-year-old female presenting with fever, limb pain, swelling, and subcutaneous nodules, who was ultimately diagnosed with SPTCL via punch biopsy and BIOMED-2 clonality assays, confirming positive T-cell receptor-γ chain gene rearrangement. Positron emission tomography–computed tomography revealed diffuse subcutaneous involvement across multiple body regions. Methylprednisolone and cyclosporine A treatment rapidly resolved her symptoms, with laboratory parameters, including ferritin and inflammatory markers, showing significant improvement. Next-generation sequencing identified a heterozygous C9 gene mutation (c.346C>T, p.Arg116Ter), adding a novel genetic dimension to the case. Following a tapered discontinuation of immunosuppressive therapy, the patient achieved sustained remission without relapse for over 1 year. Conclusions: We report a case of adolescent SPTCL treated with immunosuppressive therapy and suggest that immunosuppressive therapy should be considered before chemotherapy in pediatric patients with SPTCL but without HLH. Full article

This conceptual position is based on the diagnosis that artificial intelligence (AI) accentuates existing economic and geopolitical divides in communities in the Global South, which provide data without receiving rewards. Based on bioethical precedents of fair distribution of genetic resources, it is proposed to transfer the principle of benefit-sharing to the emerging algorithmic governance in the context of AI. From this discussion, the study reveals an algorithmic concentration in the Global North. This dynamic generates political, cultural, and labor asymmetries. Regarding the methodological design, the research was qualitative, with an interpretive paradigm and an inductive method, applying documentary review and content analysis techniques. In addition, two theoretical and two analytical categories were used. As a result, six emerging categories were identified that serve as pillars of the studied principle and are capable of reversing the gaps: equity, accessibility, transparency, sustainability, participation, and cooperation. At the end of the research, it was confirmed that AI, without a solid ethical framework, concentrates benefits in dominant economies. Therefore, if this trend does not change, the Global South will become dependent, and its data will lack equitable returns. Therefore, benefit-sharing is proposed as a normative basis for fair, transparent, and participatory international governance. Full article

Background/Objectives: Wilson’s disease (WD) is a rare autosomal recessive disorder of copper metabolism that results in hepatic, neurological, and psychiatric manifestations. Despite being described globally, data from the Middle East remains limited. This study presents the first comprehensive national cohort analysis of WD in Oman, examining clinical features, diagnostic challenges, treatment patterns, and long-term outcomes. Methods: A retrospective cohort study was conducted on 36 Omani patients diagnosed with WD between 2013 and 2020 at Sultan Qaboos University Hospital using AASLD diagnostic criteria. Clinical presentation, biochemical parameters, treatment regimens, and progression-free survival were analyzed. Results: The median age at diagnosis was 14.5 years, with a slight female predominance (55.6%). Clinical presentation varied: 25% had hepatic symptoms, 22.2% had mixed hepatic-neurological features, and 16.7% presented with neurological symptoms alone. Asymptomatic cases identified via family screening accounted for 33.3%. Diagnostic delays were most pronounced among patients presenting with neurological symptoms. A positive family history was reported in 88.9% of cases, suggesting strong familial clustering despite a low rate of consanguinity (5.6%). Regional distribution was concentrated in Ash Sharqiyah North and Muscat. Chelation therapy with trientine or penicillamine, often combined with zinc, was the mainstay of treatment. Treatment adherence was significantly associated with improved progression-free survival (p = 0.012). Conclusions: WD in Oman is marked by heterogeneous presentations, frequent diagnostic delays, and strong familial clustering. Early detection through cascade screening and sustained treatment adherence are critical for favorable outcomes. These findings support the need for national screening policies and structured long-term care models for WD in the region. Full article

Chronic nasopharyngeal and otic disorders in children represent a significant clinical challenge due to their multifactorial etiology, variable presentation, and frequent resistance to standard therapies. Although often approached from a symptomatic or anatomical perspective, these conditions are deeply rooted in histological and molecular alterations that sustain inflammation, impair mucosal function, and promote recurrence. This narrative review synthesizes the current knowledge on the normal histology of the nasopharynx, Eustachian tube, and middle ear, and explores key pathophysiological mechanisms, including epithelial remodeling, immune cell infiltration, cytokine imbalance, and tissue fibrosis. Special emphasis is placed on the role of immunohistochemistry in defining inflammatory phenotypes, barrier dysfunction, and remodeling pathways. The presence of biofilm, epithelial plasticity, and dysregulated cytokine signaling are also discussed as contributors to disease chronicity. These findings have direct implications for diagnosis, therapeutic stratification, and postoperative monitoring. By integrating histological, immunological, and molecular data, clinicians can better characterize disease subtypes, anticipate treatment outcomes, and move toward a more personalized and biologically informed model of pediatric ENT care. Full article

Agriculture is the backbone of Punjab’s economy, and with much of India’s population dependent on agriculture, the requirement for accurate and timely monitoring of land has become even more crucial. Blending remote sensing with state-of-the-art machine learning algorithms enables the detailed classification of agricultural lands through thematic mapping, which is critical for crop monitoring, land management, and sustainable development. Here, a Hyper-tuned Deep Neural Network (Hy-DNN) model was created and used for land use and land cover (LULC) classification into four classes: agricultural land, vegetation, water bodies, and built-up areas. The technique made use of multispectral data from Sentinel-2 and Landsat-8, processed on the Google Earth Engine (GEE) platform. To measure classification performance, Hy-DNN was contrasted with traditional classifiers—Convolutional Neural Network (CNN), Random Forest (RF), Classification and Regression Tree (CART), Minimum Distance Classifier (MDC), and Naive Bayes (NB)—using performance metrics including producer’s and consumer’s accuracy, Kappa coefficient, and overall accuracy. Hy-DNN performed the best, with overall accuracy being 97.60% using Sentinel-2 and 91.10% using Landsat-8, outperforming all base models. These results further highlight the superiority of the optimised Hy-DNN in agricultural land mapping and its potential use in crop health monitoring, disease diagnosis, and strategic agricultural planning. Full article

Background: Malnutrition and unintended weight loss are frequent in cancer patients and linked to poorer outcomes. However, data on long-term weight trajectories, particularly comparing different cancer entities, remain limited. Methods: In this retrospective, multicenter study, we analyzed 145 patients diagnosed with either head and neck cancer (HNC; n = 48) or high-grade B-cell lymphoma (HGBCL; n = 97). Body weight, C-reactive protein (CrP), albumin, and modified Glasgow Prognostic Score (mGPS) were assessed at diagnosis and at 3, 6, 9, and 12 months. Clinically relevant weight loss was defined as >5% from baseline. Survival analyses were performed for HGBCL patients. Results: Weight loss was common in both cohorts, affecting 32.2% at 3 months and persisting in 42.3% at 12 months. Nearly half of HNC patients had sustained >5% weight loss at one year, whereas HGBCL patients were more likely to regain weight, with significantly higher rates of weight gain at 6 and 12 months (p = 0.04 and p = 0.02). At baseline, HGBCL patients showed elevated CrP and lower albumin compared to HNC (both p < 0.001). Weight loss at 6 months was significantly associated with reduced overall survival in HGBCL (p < 0.01). Both Δweight at 6 months and mGPS emerged as useful prognostic indicators. Conclusions: This study reveals distinct patterns of weight change and systemic inflammation between HNC and HGBCL patients during the first year after diagnosis. These findings highlight the need for entity-specific nutritional monitoring and tailored supportive care strategies extending into survivorship. Prospective studies integrating body composition analyses are warranted to better guide long-term management. Full article

Lower respiratory tract infections remain a leading cause of morbidity and mortality among Intensive Care Unit patients, with severe cases often progressing to acute respiratory distress syndrome (ARDS). This life-threatening syndrome results from alveolar–capillary membrane injury, causing refractory hypoxemia and respiratory failure. Early detection and management are critical to treat the underlying cause, provide protective lung ventilation, and, eventually, improve patient outcomes. The 2012 Berlin definition standardized ARDS diagnosis but excluded patients on non-invasive ventilation (NIV) or high-flow nasal cannula (HFNC) modalities, which are increasingly used, especially after the COVID-19 pandemic. By excluding these patients, diagnostic delays can occur, risking the progression of lung injury despite ongoing support. Indeed, sustained, vigorous respiratory efforts under non-invasive modalities carry significant potential for patient self-inflicted lung injury (P-SILI), underscoring the need to broaden diagnostic criteria to encompass these increasingly common therapies. Recent proposals expand ARDS criteria to include NIV and HFNCs, lung ultrasound, and the SpO₂/FiO₂ ratio adaptations designed to improve diagnosis in resource-limited settings lacking arterial blood gases or advanced imaging. However, broader criteria risk overdiagnosis and create challenges in distinguishing ARDS from other causes of acute hypoxemic failure. Furthermore, inter-observer variability in imaging interpretation and inconsistencies in oxygenation assessment, particularly when relying on non-invasive measurements, may compromise diagnostic reliability. To overcome these limitations, a more nuanced diagnostic framework is needed—one that incorporates individualized therapeutic strategies, emphasizes lung-protective ventilation, and integrates advanced physiological or biomarker-based indicators like IL-6, IL-8, and IFN-γ, which are associated with worse outcomes. Such an approach has the potential to improve patient stratification, enable more targeted interventions, and ultimately support the design and conduct of more effective interventional studies. Full article

The reliability of unmanned aerial vehicle (UAV) energy storage battery systems is critical for ensuring their safe operation and efficient mission execution, and has the potential to significantly advance applications in logistics, monitoring, and emergency response. This paper reviews theoretical and technical advancements in UAV battery reliability, covering definitions and metrics, modeling approaches, state estimation, fault diagnosis, and battery management system (BMS) technologies. Based on international standards, reliability encompasses performance stability, environmental adaptability, and safety redundancy, encompassing metrics such as the capacity retention rate, mean time between failures (MTBF), and thermal runaway warning time. Modeling methods for reliability include mathematical, data-driven, and hybrid models, which are evaluated for accuracy and efficiency under dynamic conditions. State estimation focuses on five key battery parameters and compares neural network, regression, and optimization algorithms in complex flight scenarios. Fault diagnosis involves feature extraction, time-series modeling, and probabilistic inference, with multimodal fusion strategies being proposed for faults like overcharge and thermal runaway. BMS technologies include state monitoring, protection, and optimization, and balancing strategies and the potential of intelligent algorithms are being explored. Challenges in this field include non-unified standards, limited model generalization, and complexity in diagnosing concurrent faults. Future research should prioritize multi-physics-coupled modeling, AI-driven predictive techniques, and cybersecurity to enhance the reliability and intelligence of battery systems in order to support the sustainable development of unmanned systems. Full article

Background/Objectives: Endometriosis is a chronic, estrogen-driven gynecological disorder affecting approximately 10% of reproductive-aged women worldwide, with significant physical, psychosocial, and socioeconomic impacts. Recent research suggests a possible involvement of the gut microbiome in endometriosis disease mechanisms through immune manipulation, estrogen metabolism, and inflammatory networks. This narrative review aims to summarize current evidence on gut microbiota changes in endometriosis patients, explore the mechanisms by which gut dysbiosis contributes to disease progression, and examine epidemiological links between gastrointestinal health and endometriosis risk. Methods: A narrative review was conducted to synthesize available literature on the compositional changes in gut microbiota associated with endometriosis. The review also evaluated studies investigating potential mechanisms and epidemiological patterns connecting gut health with endometriosis development and severity. Results: Alterations in gut microbiota composition were observed in endometriosis patients, suggesting roles in immune dysregulation, estrogen metabolism, and inflammation. Potential gut-oriented interventions, including dietary changes, probiotics, and lifestyle modifications, emerged as promising management options. However, methodological variability and research gaps remain barriers to clinical translation. Conclusions: Integrating gut microbiome research into endometriosis management holds potential for improving early diagnosis, patient outcomes, and healthcare system sustainability. The study emphasizes the need for further research to address existing challenges and to develop public health strategies that incorporate microbiome-based interventions in population-level endometriosis care. Full article

Background: As COVID-19 transitions to an endemic phase, long COVID symptoms remain a significant public health issue affecting both physical and mental health. A notable proportion of college students report symptoms such as fatigue, cough, and brain fog persisting for weeks or months post-infection. Objectives: This study explored the prevalence and contributing factors of long COVID symptoms among both infected and uninfected students in medicine-related departments. Methods: A cross-sectional study was conducted using online self-reported questionnaires completed by 1523 undergraduate and graduate students in medicine-related departments at a medical university. Participants who had tested positive for COVID-19 within the past three months were excluded. The survey assessed long COVID symptoms, with comparisons conducted between infected and uninfected groups. Multivariate logistic regression identified risk factors associated with long COVID symptoms. Results: Of the 1118 participants, 47.5% of those with a prior COVID-19 diagnosis reported long COVID symptoms within the past month. Significant differences between the infected and uninfected groups were observed in physical, cognitive, and psychological health. Logistic regression identified that prior COVID-19 diagnosis had an association with the presence of long COVID symptoms (odds ratio = 1.48, p = 0.024) after adjusted model analysis. Meanwhile, higher anxiety levels (odds ratio = 1.09, p < 0.001) and a BMI ≥ 24 (odds ratio = 4.50, p < 0.01) were identified as significant risk factors for post-infection syndrome among previously infected students. Sex and exercise habits also influenced symptom prevalence. Conclusions: Since late 2023, with those experiencing cumulative infections surpassing half of Taiwan’s population, long COVID symptoms have persisted as a widespread concern affecting both physical and mental health, continuing into 2025. This study underscores critical risk factors and symptom patterns among students in medicine-related departments, reinforcing the urgency of sustained surveillance and targeted interventions to facilitate comprehensive recovery. Full article

Background and Clinical Significance: Serratus Anterior Muscle Pain Syndrome (SAMPS) is an underdiagnosed cause of anterior chest wall pain, often attributed to myofascial trigger points of the serratus anterior muscle (SAM) or dysfunction of the Long Thoracic Nerve (LTN), leading to significant disability and affecting ipsilateral upper limb movement and quality of life. Current diagnosis relies on exclusion and physical examination, with limited treatment options beyond conservative approaches. This case report presents a novel approach to chronic SAMPS, successfully diagnosed using Sonoguided Digital Palpation (SDP) and treated with ultrasound-guided hydrodissection of the LTN using 5% dextrose in water (D5W) without local anesthetic (LA), in a patient where conventional treatments had failed. Case Presentation: A 72-year-old male presented with a three-year history of persistent left chest pain radiating to the upper back, exacerbated by activity and mimicking cardiac pain. His medical history included two percutaneous coronary interventions. Physical examination revealed tenderness along the anterior axillary line and a positive hyperirritable spot at the mid axillary line at the 5th rib level. SDP was used to visualize the serratus anterior fascia (SAF) and LTN, and to reproduce the patient’s concordant pain by palpating the LTN. Ultrasound-guided hydrodissection of the LTN was then performed using 20–30cc of D5W without LA to separate the nerve from the surrounding tissues, employing a “fascial unzipping” technique. The patient reported immediate pain relief post-procedure, with the pain reducing from 9/10 to 1/10 on the Numeric Rating Scale (NRS), and sustained relief and functional improvement at the 12-month follow-up. Conclusions: Sonoguided Digital Palpation (SDP) of the LTN can serve as a valuable diagnostic adjunct for visualizing and diagnosing SAMPS. Ultrasound-guided hydrodissection of the LTN with D5W without LA may provide a promising and safe treatment option for patients with chronic SAMPS refractory to conservative management, resulting in rapid and sustained pain relief. Further research, including controlled trials, is warranted to evaluate the long-term efficacy and generalizability of these findings and to compare D5W to other injectates. Full article

Abdominal arteriovenous fistula [AVF] is a rare but serious complication of penetrating trauma, often associated with high morbidity and mortality. This report presents the case of a 24-year-old male who sustained multiple gunshot wounds, leading to the formation of an ilio-iliac AVF and a pseudoaneurysm. The patient arrived at the emergency department hemodynamically unstable, with bullet wounds to the forearm, thigh, and lumbosacral region. Initial non-arterial phase CT revealed a pseudoaneurysm anterior to the right external iliac vessels and a surrounding hematoma, raising suspicion for AVF. A second biphasic CTA confirmed an AVF connection between the right external iliac artery and external iliac vein, as well as the arterialization of the vein. Additionally, fat stranding and bowel wall thickening suggested potential hollow viscus injury. Due to the patient’s unstable condition and possible intra-abdominal injuries, an open laparotomy was performed. A stent was placed in the right external iliac artery, the vein was primarily repaired, and serosal injuries to the duodenum and cecum were surgically addressed. The patient recovered gradually, although a persistent serous discharge was noted and managed in follow-up. This case highlights the importance of considering AVF in penetrating abdominal trauma and the critical role of biphasic CTA in diagnosis and surgical planning. Full article

Congenital hypothyroidism (CH) is a leading preventable cause of neurocognitive impairment. Its incidence appears to be rising in several countries. We analysed 27 years of newborn-screening data (1997–2023) from the largest Chilean screening centre, covering 3,225,216 newborns (51.1% of national births), to characterise temporal trends and potential drivers of CH incidence. Annual CH incidence was modelled with Prais–Winsten regression to correct for first-order autocorrelation; additional models assessed trends in gestational age, sex, biochemical markers, and aetiological subtypes. We identified 1550 CH cases, giving a mean incidence of 4.9 per 10,000 live births and a significant yearly increase of 0.067 per 10,000 (95 % CI 0.037–0.098; p < 0.001). Mild cases (confirmation TSH < 20 mU/L) rose (+0.89 percentage points per year; p = 0.002). The program’s recall was low (0.05%). Over time, screening and diagnostic TSH values declined, total and free T4 concentrations rose, gestational age at diagnosis fell, and a shift from thyroid ectopy toward hypoplasia emerged; no regional differences were detected. The sustained increase in CH incidence, alongside falling TSH thresholds and growing detection of in situ glands, suggests enhanced recognition of milder disease. Ongoing surveillance should integrate environmental, iodine-nutrition, and genetic factors to clarify the causes of this trend. Full article