Search Results (6,891)

Accurate cotton boll identification and boll number estimation from UAV imagery are essential for large-scale yield prediction and precision management, yet severe leaf occlusion and complex canopy backgrounds often hinder robust performance. Here, UAV RGB images were acquired 3 days before defoliant application and at 3, 6, 9, 12, 15, and 18 days after defoliation. Cotton bolls were extracted using Mahalanobis distance, a support vector machine, and a neural network. Boll number was then estimated using an improved random forest model with multi-feature fusion. Across all defoliation stages, the NN produced the most accurate and stable boll extraction, achieving a maximum Kappa of 0.914, an overall accuracy of 95.77%, and an F1 score of 0.96. Extraction accuracy increased rapidly from 3 to 9 days after application and stabilized from 12 to 18 days. For boll number estimation, fusing the boll pixel ratio with color indices and texture features improved accuracy and consistency over time; the best performance was obtained at 18 days after application (R² = 0.7264; rRMSE = 4.9%). Overall, imagery acquired 15–18 days after defoliation provided the most reliable estimation window, supporting operational pre-harvest assessment and harvest-timing decisions. Full article

To establish a biorefinery within kraft-pulp mills, the extraction of fermentable sugars must be balanced with the preservation of fiber quality for papermaking. This study investigates this trade-off by applying partial enzymatic hydrolysis to unbleached high-kappa eucalyptus kraft pulp to co-produce bioethanol and packaging-grade materials. Although the mass-transfer limitations inherent to the high-consistency strategy (15% solids or 150 g L⁻¹) restrict extensive saccharification (keeping glucose conversion below 5% at 1.5 h), it naturally directs the process toward a low-severity regime essential for fiber conservation. Structural analysis (X-ray diffraction and microscopy) revealed that enzymes preferentially targeted amorphous regions, increasing crystallinity (from ≈74% to ≈82%) but reducing intrinsic fiber strength (tear) over time (dropping from ~5.6 to ~2.3 mN·m²·g⁻¹ within 30 min). However, a strategic window for valorization has been identified. Instead of direct papermaking, hydrolyzed residue is highly effective as a strength-enhancing additive. When blended (20% w w⁻¹) with commercial pulp, the modified fibers improved interfiber bonding, restored the tensile strength, and significantly increased the Burst Index (up to ~1.7 kPa·m²·g⁻¹). These results demonstrate a viable industrial approach using partial hydrolysis to recover hemicellulose-based sugars for biofuels, while transforming the solid fraction into a high-performance reinforcement agent for paper packaging. This approach effectively converts a potential trade-off into a synergistic dual-product stream. Full article

Inflammation and cellular senescence are fundamental contributors to aging and neurodegenerative disorders. Marine algae are increasingly acknowledged for their content of bioactive molecules capable of influencing inflammation and cellular aging. In this research, we examined the capacity of Sargassum siliquastrum aqueous extract (SSE) to counteract inflammatory responses in RAW 264.7 macrophages stimulated by lipopolysaccharide, as well as aging-related changes in a mouse model of D-galactose (D-gal)-induced aging. SSE treatment markedly lowered levels of pro-inflammatory cytokines, prostaglandin E₂, and nitric oxide. Furthermore, SSE attenuated the transcriptional activities of nuclear factor kappa-B (NF-κB) and activator protein 1, while modulating protein expression associated with NF-κB and mitogen-activated protein kinase (MAPK) signaling pathways in RAW 264.7 cells. In vivo, SSE reduced the phosphorylation levels of MAPKs in the hippocampus of D-gal-treated mice. Additionally, SSE modulated the expression of genes associated with cellular senescence and inflammation in the hippocampus and cerebral cortex. However, the apparent molecular effects were not accompanied by significant improvement in passive avoidance performance, which showed only a non-significant trend between the model control and SSE-administrated groups. Collectively, these findings suggest that SSE exerts anti-inflammatory effects in vitro and provide preliminary evidence of its potential to modulate D-gal-induced aging-related neuroinflammatory changes in mice. Full article

Background/Objectives: Functional foods are widely discussed in nutrition research, yet their consumption is rarely quantified using a standardized, food-based metric. We developed the Functional Food Consumption Index (FunFoCI) and conducted an initial evaluation of its performance in adults. Methods: In this cross-sectional study, 500 adults (≥18 years, 286 women, 214 men) were assessed using a 210-item quantitative food frequency questionnaire (FFQ) and a 3 day food record (FR). Candidate index foods were evaluated by five experts, using a 4-point Likert scale to establish content validity, and the finalized FunFoCI comprised 100 foods across nine groups: fruits; vegetables; whole grains; legumes; nuts and oilseeds; fermented foods and products; animal-based foods; functional oils; and spices, herbal teas, and functional beverages. FunFoCI scoring used a sample distribution-based percentile approach, including modifications for zero-inflated or sparsely consumed items, followed by group-level normalization (0–1), equal weighting across nine groups, and rescaling to 0–100. FR data were used to examine the between-method feasibility of the scoring approach. The convergent validity was assessed via correlation analyses, with the Diet Quality Index-International (DQI-I) and Healthy Eating Index-2015 (HEI-2015) derived from both FFQ and FR data, and additional correlation analyses and robustness checks were conducted to examine associations among key study variables. Known group patterns were examined across sociodemographic, lifestyle, and anthropometric characteristics. Results: Content evaluation supported index coverage (S-CVI/Ave = 0.912; S-CVI/UA = 0.877; mean modified kappa = 0.899). The mean FunFoCI total scores were 32.68 ± 11.92 (FFQ) and 13.29 ± 4.65 (FR). Participants were classified into low (32.8%, n = 164), moderate (33.0%, n = 165), and high (34.2%, n = 171) FunFoCI categories. FunFoCI correlated with FFQ-derived DQI-I and HEI-2015 (r = 0.367 and r = 0.368; both p < 0.001), and both indices increased across ascending FunFoCI total scores (p < 0.001). The FFQ-derived FunFoCI total score was correlated with the FR-derived FunFoCI score (r = 0.294; p < 0.001). FunFoCI scores showed differences across participant sociodemographic, lifestyle and anthropometric characteristics. Conclusions: FunFoCI is a newly developed, expert-reviewed, food-based index with transparent, sample distribution-based scoring and normalized aggregation. Its initial evaluation supports its use for the standardized quantification of relative functional food consumption in adults, while further studies should assess the reliability and external validation criteria in other populations and study designs. Full article

Background: Gadoxetic acid-enhanced MRI is essential for detecting and characterizing focal liver lesions. However, transient severe motion artifacts in the arterial phase can degrade image quality. Gadoxetic acid dilution has been proposed to mitigate these artifacts, but its impact on multiple arterial phase acquisition remains unclear. Objective: To evaluate the effect of gadoxetic acid dilution on image quality across multiple arterial phases in liver MRI, incorporating a phase-by-phase analysis. Methods: This retrospective study included 81 patients (52 men, 29 women; mean age 70.1 years) who underwent serial gadoxetic acid-enhanced MRI with undiluted and diluted contrast (1:1 saline dilution). MRI was performed on 1.5 T and 3.0 T scanners with a standardized injection rate of 1.0 mL/s. Two radiologists independently rated anatomic conspicuity, respiratory motion artifacts, and overall image quality using a Likert scale (1 to 5 with higher scores indicating better quality). A phase-by-phase analysis was conducted after a three-month washout period. Wilcoxon signed-rank tests were used for statistical comparisons, and inter-rater agreement was assessed with quadratic kappa coefficients. Results: Inter-observer agreement was substantial (ƙ = 0.602–0.702). Phase-by-phase analysis revealed significant improvement in image quality for the first three arterial phases (p = 0.003, 0.005, 0.050). Although the diluted method showed higher scores, the differences were not statistically significant in anatomic conspicuity (3.73 vs. 3.59, p = 0.110), respiratory artifacts (3.54 vs. 3.41, p = 0.291), and overall image quality (3.67 vs. 3.51, p = 0.083). Conclusions: Gadoxetic acid dilution improves image quality in early arterial phases of liver MRI, suggesting its potential to reduce motion artifacts. Full article

The grain aphid (Sitobion avenae) is a major pest of winter wheat, causing significant yield losses through direct feeding and as a vector of barley yellow dwarf virus (BYDV). Populations can increase rapidly under moderate temperatures and low rainfall, potentially leading to severe infestations if not effectively monitored and managed. This study develops and validates a UAV-based RGB imaging methodology, which relies on deep learning for accurate detection and assessment of Sitobion avenae in wheat crops. The RGB images are preliminarily filtered using “histogram equalization”, which allows for highlighting the infested areas. An experimental study was conducted under the specific climatic conditions of Southern Dobruja, Bulgaria, to quantify Sitobion avenae infestations. Three neural network architectures were used (DeepLabv3, U-Net, and PSPNet) in combination with three backbone models: ResNet34, ResNet50, and ResNet101. The optimal combination was determined to be the U-Net + ResNet101 model, which achieved an average F1 score of 0.982 and a Cohen’s Kappa coefficient of 0.966. The results demonstrate that UAV-based detection allows precise mapping of infested areas, enabling targeted insecticide applications and effective pest management while substantially reducing chemical inputs. These findings indicate that the proposed framework provides a reliable and scalable tool for precision pest monitoring and control in winter wheat. Full article

Background/Objectives: Artificial intelligence (AI) tools, particularly large language models (LLMs) such as ChatGPT-4o, are gaining prominence in medicine. While their diagnostic capabilities have been explored across various oncologic domains, their role in clinical decision-making within multidisciplinary tumor boards (MTBs) remains largely unexamined in urologic oncology. This study evaluates the performance of ChatGPT-4o as a decision-support tool in a real-world MTB setting by comparing its recommendations with those of expert clinicians. Materials and Methods: A retrospective study was conducted using 98 anonymized clinical cases discussed by a urologic MTB between June 2024 and February 2025. An independent urologist entered the same cases into ChatGPT-4o using a standardized prompt replicating real-world presentation. Two certified urologists independently assessed the model’s responses. Agreement was analyzed overall and by tumor type, disease stage, clinical context, and treatment strategy. Results: ChatGPT-4o fully agreed with the MTB in 56.1% of cases, was correct but incomplete in 23.5%, and provided partially accurate but flawed recommendations in 18.4%. Overall concordance between ChatGPT-4o and the MTB yielded a Cohen’s kappa of 0.61, indicating moderate-to-good agreement. Discrepancies were most common in metastatic prostate cancer, often due to misclassification of tumor burden or errors in treatment sequencing. Highest agreement rates were observed in bladder and renal tumors, and in standardized therapeutic scenarios such as radiotherapy. Conclusions: ChatGPT-4o demonstrated moderate alignment with expert MTB decisions and performed best in well-defined clinical contexts. While it cannot replace multidisciplinary expertise, it may serve as a supportive tool to enhance access to standardized oncologic care. Full article

Background/Objectives: Although Ki-67 is not formally incorporated into the grading system of pulmonary neuroendocrine neoplasms (PNENs), it is widely used as an adjunct marker to reflect proliferative activity and support diagnostic stratification. Manual Ki-67 assessment is subject to interobserver variability and methodological limitations. This study aimed to evaluate the reliability and performance of two artificial intelligence (AI)-based image analysis systems in Ki-67 index assessment and to compare their results with expert pathologist evaluation in pulmonary neuroendocrine tumors. Methods: A total of 63 pulmonary neuroendocrine neoplasm cases, including typical carcinoid (n = 29), atypical carcinoid (n = 13), and large cell neuroendocrine carcinoma (n = 21), were retrospectively analyzed. Ki-67 proliferation indices were independently assessed by four pathologists within predefined hotspot regions, counting approximately 2000 tumor cells per case. The same regions were analyzed using two AI-based image analysis systems (Roche uPath Ki-67 and Virasoft Virasight Ki-67). Interobserver agreement among pathologists was evaluated using the intraclass correlation coefficient (ICC), and concordance between manual and AI-based assessments was assessed using Spearman’s correlation and linear regression analyses. To account for potential scanner/platform effects, slides were digitized using two different whole-slide scanners (VENTANA DP^® 600 and Leica Aperio AT2), and color normalization and quality control procedures were applied prior to AI-based analysis. For clinical interpretability, Ki-67 indices were stratified into categorical groups based on tumor subtype-specific thresholds (0–<10%: low, 10–25%: intermediate, >25%: high), and agreement between manual and AI-based categorical scoring was evaluated using Cohen’s kappa coefficient. Results: Among the 63 pulmonary neuroendocrine neoplasm cases, Ki-67 proliferation indices varied across tumor subtypes, with typical carcinoids showing low, atypical carcinoids intermediate, and large cell neuroendocrine carcinomas high proliferative activity. Interobserver agreement among four pathologists was excellent (ICC = 0.998, 95% CI: 0.996–0.998). Strong correlations were observed between manual Ki-67 assessments and AI-derived indices, with Spearman correlation coefficients of 0.961 (95% CI: 0.918–0.982) for Roche AI and 0.904 (95% CI: 0.821–0.949) for Virasoft AI, and 0.926 (95% CI: 0.842–0.968) between the two AI systems. Bland–Altman analyses demonstrated minimal mean differences and most cases within the 95% limits of agreement, indicating high concordance without systematic bias. Categorical agreement analysis, using subtype-specific Ki-67 thresholds (0–<10%: low; 10–25%: intermediate; >25%: high), showed excellent concordance between manual and AI-based scoring (Cohen’s kappa 0.877 for Roche AI and 0.827 for Virasoft AI; p < 0.001), confirming the clinical interpretability and reproducibility of AI-based Ki-67 assessment. Conclusions: AI-based Ki-67 index assessment shows strong concordance with expert pathologist evaluation and reflects biologically relevant differences among pulmonary neuroendocrine neoplasm subtypes. These results suggest that AI-assisted Ki-67 analysis may serve as a reproducible and objective adjunct to routine diagnostic practice in pulmonary neuroendocrine tumors. Full article

Quality grading of greenhouse lettuce requires rapid external appearance screening and nondestructive internal quality assessment. However, existing detection methods struggle to simultaneously evaluate both external and internal quality while maintaining efficiency, resulting in a lack of scientific and comprehensive integrated evaluation standards for current crop grading. To address this issue, this study leveraged the technical strengths of different sensors to construct separate models: an RGB image-based monitoring model for external quality and a hyperspectral-based estimation model for internal quality. Using a combined objective–subjective weighting method, this approach scientifically integrated external and internal quality monitoring indicators to establish a comprehensive evaluation method for greenhouse lettuce quality. The results demonstrate that features such as canopy projection area, compactness, and color components can be extracted from RGB images. Combined with Ridge regression, this approach achieves high-accuracy estimation of lettuce fresh weight and leaf area (R² ≥ 0.880). For intrinsic quality, by combining hyperspectral data with the CARS and SPA band selection algorithms, a Random Forest (RF)-based inversion model for chlorophyll, soluble sugar, protein, and vitamin C content was developed. The AHP-CRITIC method effectively resolved the weight imbalance caused by an excessive coefficient of variation in appearance indicators, thereby achieving the scientific integration of appearance and internal quality data. The grading outcomes of this integrated evaluation method were highly consistent with industry standards (kappa coefficient: 0.788). This approach establishes an effective link between the rapid monitoring of external and internal quality for comprehensive evaluation, providing a novel technical pathway and scientific basis for nondestructive post-harvest detection and automated grading of greenhouse vegetables. Full article

Oxidative stress (OS) is a central regulator of health and productivity in livestock, emerging from complex interactions between dietary inputs, microbiome composition, environmental stressors, and host metabolism. This narrative review synthesizes current knowledge on OS in cattle, pigs, sheep, and poultry, emphasizing mechanistic pathways, tissue-specific responses, and translational applications. We highlight the central role of redox–inflammatory signaling hubs, including nuclear factor kappa B (NF-κB), nuclear factor erythroid 2–related factor 2 (Nrf2)/Kelch-like ECH-associated protein 1 (Keap1), and inflammasomes, as integrators of metabolic and immune stress. Microbiome–metabolome interactions modulate systemic oxidative responses, influencing liver, mammary gland, gastrointestinal tract, adipose tissue, and reproductive tissues. Oxidative stress-related biochemical and molecular alterations are captured by a range of biomarkers, such as malondialdehyde (MDA), Total Antioxidant Capacity (TOAC), gluthatione peroxidase (GPx), superoxide dismutase (SOD), paraoxonase-1 (PON1), cytokines, and gene expression profiles, measurable in blood, milk, saliva, and tissues. Integrating these markers enables precision diagnostics, early disease detection, and evidence-based nutritional interventions. Furthermore, computational modeling and spatial–socioeconomic perspectives offer novel approaches to translate molecular redox insights into practical livestock management strategies. By framing OS as a regulated, context-dependent process rather than a simple imbalance of reactive oxygen species, this review advances a conceptual, cross-species framework for understanding, monitoring, and mitigating oxidative stress in livestock. This integrative perspective provides a foundation for targeted antioxidant strategies and sustainable production practices, bridging molecular mechanisms with practical applications in animal health and productivity. Full article

As a unique cultural heritage of Chinese ethnic minorities, Dong architecture provides rich historical and cultural information. Rapid and accurate extraction of ethnic building information from remote sensing images in complex terrain and high-density settlement environments is highly important for the protection of architectural heritage and the management of rural space. Huanggang Dong Village in Liping County, Guizhou Province, China, is taken as a case study. This paper develops a multifeature fusion machine learning framework for the automatic recognition of Dong ethnic architecture based on centimeter-level visible images captured by UAV. First, the vegetation index, HSI color features and texture features based on the gray level co-occurrence matrix are extracted from the UAV visible light orthophoto image. Through the random forest feature importance ranking and correlation test, six key features, namely, the VDVI, HSI-S, HSI-I, mean, variance and contrast, are selected to construct a multifeature space. This step constitutes the feature construction stage of the proposed methodology and provides the basis for subsequent classification. Second, on the basis of a support vector machine (SVM) and random forest (RF), classification models are constructed. The effects of different feature combinations and different algorithms on classification accuracy are systematically compared, and the results are evaluated in terms of overall accuracy (OA), the kappa coefficient, user accuracy (UA) and producer accuracy (PA). This second part highlights the classification phase of the methodology, which tests the feature space using different algorithms and evaluates the performance of the models. The experimental data fully show that under the condition of a single feature, the SVM model dominated by texture features performs best, with an OA of 85.33% and a kappa of 0.799; under the condition of multifeature fusion, the RF algorithm has a stronger ability to integrate multisource features. The accuracy of building category recognition based on the total feature and dimensionality reduction feature space is particularly prominent. The total feature and overall accuracy reach 89.00%, and the kappa coefficient is 0.850. The UA and PA reached 89.66% and 94.55%, respectively. Through in-depth comparative analysis, the vegetation index–color–texture multifeature fusion and machine learning classification framework based on UAV visible light images can achieve high-precision extraction of Dong architecture without relying on high-cost sensors. It can effectively alleviate the confusion between water bodies and shadows and between dark roofs and vegetation and effectively separate traditional Dong architecture from roads, vegetation and other elements. It provides a low-cost and feasible way for digital archiving, dynamic monitoring and protection management of the traditional village architectural heritage of ethnic minorities. Full article

Influenza A virus (IAV) infection constitutes a major public health threat. Severe influenza virus infection can induce intense inflammatory responses and lung injury, leading to serious clinical symptoms or even death. The utility of current anti-influenza drugs is often limited by side effects and the emergence of drug-resistant strains. Based on the critical role of L-type voltage-gated calcium channels (L-VGCCs) in influenza virus replication, this study investigates the antiviral activity and mechanism of verapamil, a classic L-type calcium channel antagonist, against H1N1-UI182 virus. Verapamil, an L-type calcium channel blocker, is widely used in the treatment of cardiovascular diseases and has a well-established safety profile. Through molecular dynamics (MD) simulation and network pharmacology analysis, we predicted the stable binding mode of verapamil to the target protein (PDB id: 6JPA) and its potential multi-target network. In vitro, verapamil exhibited antiviral activity against H1N1-UI182 in MDCK cells, enhancing the survival rate of infected cells and reducing viral nucleoprotein (NP) expression. In a lethal H1N1-UI182 infection mouse model, verapamil treatment markedly improved survival rates, alleviated weight loss and lung pathological damage, exhibiting a dose-dependent protective effect. Lung tissue analysis showed that verapamil effectively reduced the lung index and viral load, suppressed the activation of the Nuclear factor kappa B (NF-κB) signaling pathway, and decreased the expression of key inflammatory factors, thereby mitigating the cytokine storm. A comparison of administration regimens indicated that pre-treatment yielded optimal efficacy, suggesting verapamil acts primarily during the early stage of the viral life cycle. This study systematically elucidates that verapamil exerts antiviral and immunomodulatory effects by regulating the NF-κB pathway. Network pharmacology analysis suggested the potential involvement of multiple targets and pathways, including EGFR, SRC, and phospholipase D signaling, providing hypotheses for future mechanistic investigation. This paper supports a drug repurposing strategy against drug-resistant influenza viruses and highlights its significant potential for clinical translation. Full article

Background: Abnormal activation of the NRF2-cGAS-STING-NF-κB pathway can trigger an inflammatory cascade in rheumatoid arthritis (RA). Curcumin (CUR), a polyphenolic compound extracted from turmeric, possesses anti-inflammatory activity, but whether it can modulate this pathway to ameliorate RA remains unclear. This study aims to elucidate whether CUR inhibits the inflammatory response in synovial fibroblasts (MH7A) by suppressing the NRF2-cGAS-STING-NF-κB signaling cascade. Methods: An RA inflammatory model was constructed by stimulating MH7A cells with 20 ng/mL tumor necrosis factor (TNF). Groups included a control group, a model group, a methotrexate positive control group [MTX(methotrexate), 10 μmol/L], and curcumin treatment groups at varying concentrations (10–100 μmol/L). Cell viability was assessed using the CCK-8(Cell Counting Kit-8) assay. Cell migration and invasion capabilities were evaluated via scratch wound healing and Transwell assays, respectively. Apoptosis was detected by flow cytometry. mRNA and protein expression levels of NRF2(Nuclear factor erythroid 2-related factor 2), cGAS(cyclic GMP-AMP synthase), STING(stimulator of interferon genes), and NF-κB(nuclear factor kappa-light-chain-enhancer of activated B cells) were measured using qRT-PCR and Western blot, respectively. Protein localization was determined by immunofluorescence. Results: Compared to the model group (TNF-induced), the cell migration rate in the curcumin (CUR) groups was significantly decreased (p < 0.001), with a particularly marked reduction observed at a concentration of 50 μmol/L. Furthermore, as the concentration of curcumin increased, cell invasion capacity showed a significant dose-dependent decline. The apoptosis rate also significantly decreased with increasing curcumin concentrations, demonstrating a clear concentration-dependent effect. Mechanistically, curcumin treatment significantly upregulated the expression of NRF2 and inhibited the activation of its downstream cGAS-STING-NF-κB signaling pathway. Specifically, both mRNA and protein expression levels of NRF2 were markedly elevated (p < 0.001), while the mRNA and protein levels of cGAS, STING, and NF-κB were all significantly reduced (p < 0.001). Conclusions: Curcumin (CUR) can effectively inhibit the inflammatory response of synovial fibroblasts by activating the expression of NRF2 and subsequently suppressing the cGAS-STING-NF-κB signaling pathway. This study provides a new molecular mechanism target for curcumin in the treatment of RA and offers a theoretical basis for the intervention of autoimmune diseases with natural products. Full article

Land use/land cover (LULC) change significantly influences a range of environmental and socio-economic issues, including climate change, deforestation, biodiversity loss, soil degradation, ecosystem services, and food security, at local, regional, and global levels. In the northwestern Himalayan region, particularly in Rajouri district of Jammu and Kashmir (J&K), LULC change has profound environmental and socio-economic implications. Understanding the temporal and spatial dimensions of LULC change is crucial for assessing the impact of human activities on the region’s environment. The present study aimed to analyze LULC change in Rajouri district of J&K, India over a 30-year period from 1990 to 2020 and to project future LULC dynamics for the next 30 years up to 2050. Landsat imagery with a supervised classification technique was used for classification and generation of LULC maps. Moreover, CA Markov model was used to predict the future LULC status of the area. The model validation exhibited strong performance, with Kappa statistics exceeding 0.90, indicating a high level of reliability in the projections. The results indicate considerable changes in different land use classes from 1990 to 2020. Over the 30-year period, dense forest showed the maximum reduction of about −20.69 Km², followed by open forest (−15.87 Km²) and grassland (−13.75 Km²). Wasteland showed the maximum increase of about +28.24 Km², followed by built-up (+17.90 Km²) and cropland (+12.50 Km²). The cumulative impact of deforestation from 1990 to 2020 amounts to approximately 43.17 Km², while afforestation efforts only managed to reclaim 6.61 Km² of land. The future prediction using the CA Markov model suggests further changes in LULC patterns, with built-up, cropland, and wasteland projected to increase exponentially by 2050, accompanied by sharp declines in forests. Therefore, policymakers should prioritize sustainable land management and forest conservation strategies to mitigate the potential negative impacts of LULC changes on the environment, ensuring balanced and sustainable development. Full article

Approximately 25% of the global population is estimated to have latent tuberculosis infection (LTBI), with a 5–10% lifetime risk of progression to active disease. Although interferon-gamma release assays (IGRAs) are widely used for LTBI diagnosis, their high cost and operational complexity limit large-scale implementation in resource-limited settings. This study evaluated the diagnostic performance of a low-complexity, rapid, fluorescence-based point-of-care assay, ichroma IGRA-TB, for LTBI detection. A total of 300 participants enrolled at TB Screening and Treatment Centers and the Dhaka Hospital of icddr,b were categorized as healthy controls (n = 130), household contacts of TB patients (n = 70), GeneXpert MTB/RIF Ultra-positive active TB patients (n = 80), or individuals with a previous history of TB (n = 20). ichroma IGRA-TB was compared with QuantiFERON-TB Gold Plus (QFT-Plus) across all groups. Overall agreement between ichroma IGRA-TB and QFT-Plus was 91.9%, with a Cohen’s kappa of 0.83, indicating almost perfect concordance. Using culture as a surrogate reference standard, QFT-Plus demonstrated higher sensitivity (74.6%) than ichroma IGRA-TB (69.0%). Overall, ichroma IGRA-TB demonstrates high agreement with QFT-Plus and acceptable sensitivity, supporting its potential as a near-point-of-care tool for LTBI screening in resource-constrained settings. Full article