Abstract: This study addresses the challenge of accurately forecasting electricity load in Pakistan, focusing on the Faisalabad Electric Supply Company (FESCO). The load forecasting problem in this region is exacerbated by the highly volatile nature of the data and the low baseload, further complicated by external factors such as weather conditions. To tackle this issue, we utilized historical electricity load data from FESCO from 2019 to 2022 and weather data from NASA's LaRC POWER Project. Our approach involved comprehensive exploratory data analysis (EDA) to identify significant input features, including temperature, humidity, and lagged predictors like previous hour and previous day readings. We employed a range of deep learning models to develop and test prediction models like long short-term memory (LSTM), bidirectional LSTM (BiLSTM), and gated recurrent unit (GRU) networks. The analysis revealed that lagged predictors significantly enhance prediction accuracy, with BiLSTM models demonstrating the best performance, achieving a remarkably low mean absolute percentage error (MAPE) of 0.2%. Compared to other models, our approach using time-series data arrangement without external weather predictors proved to be more accurate and economical. This model can support effective power system planning and expansion, leading to the development of a competitive bidding-based wholesale energy market in Pakistan.

Abstract: Gravitational fingering often occurs for water flow in the vadose zone and accurate modeling of this important flow process remains a significant scientific challenge. This paper presents the latest theoretical developments of the optimality-based Active Region Model (ARM), a macroscopic framework developed for describing gravitational fingering flow in the vadose zone. ARM divides the soil into active (fingering) and in-active regions, introducing a relationship between water flux and hydraulic gradient derived from the principle of optimality that the system self-organizes to maximize water flow conductivity. Unlike traditional models, ARM’s hydraulic conductivity de-pends on both capillary pressure or water saturation and water flux, reflecting the un-stable nature of fingering flow. The paper provides an updated mathematical derivation of ARM relationships using calculus of variations and extends ARM to account for small water flux in the non-fingering zone, resulting in a dual-flow field model. These new developments should make ARM more rigorous and realistic for field-scale applications.

Abstract: The dystrophin gene encodes multiple dystrophin isoforms with tissue-specific functions, including several shorter isoforms expressed in the central nervous system and retina. While Duchenne muscular dystrophy (DMD) has historically been charac-terized as a primary myopathy resulting from loss of the full-length dystrophin Dp427, increasing clinical evidence indicates that dysfunction of shorter dystrophin isoforms contributes to significant extramuscular pathology, including retinal disease. In par-ticular, loss of the Dp71 isoform has been implicated in retinal inflammation, blood–retinal barrier breakdown, and pathological angiogenesis. In this study, we investigated whether low-level residual expression of Dp71 is sufficient to mitigate retinal inflammation in the mdx3Cv mouse model, which displays reduced—but not absent—expression of multiple dystrophin isoforms. Western blot analysis revealed that mdx3Cv retinas express approximately 4% of wild-type Dp71 protein levels. Despite this marked reduction, mdx3Cv mice did not exhibit the in-flammatory phenotype previously observed in Dp71-null mice. Retinal VEGF protein levels and VEGF receptor (FLT-1 and KDR) mRNA expression were preserved, while VEGF mRNA levels were modestly reduced. Furthermore, expression of inflammatory markers ICAM-1 and ALOX5AP, leukocyte adhesion to retinal vasculature, Aqua-porin-4 expression, and BRB permeability to albumin were all comparable to wild-type littermates. Together, these findings demonstrate that minimal residual expression of Dp71 is sufficient to preserve retinal vascular homeostasis and prevent inflammatory and permeability defects in the mdx3Cv retina. These results further suggest that partial dystrophin restoration—at levels achievable with current exon-skipping or gene-based therapies—may be adequate to prevent or attenuate retinal pathology in DMD, providing a realistic and clinically relevant therapeutic target.

Abstract: T-cell malignancies represent a complex spectrum of clinically and biologically heteroge-neous diseases. Effective translational research and drug development are critically de-pendent on preclinical models that faithfully recapitulate this diversity. This review ana-lyzes the current preclinical landscape, identifying a profound disparity between the clin-ical spectrum of T-cell neoplasms and the available in vitro tools. We demonstrate that the existing armamentarium of cell lines is heavily skewed, with an abundance of models for T-cell lymphoblastic leukemia/lymphoma (T-ALL), cutaneous T-cell lymphoma (CTCL), and anaplastic large cell lymphoma (ALCL). This skew is a direct result of a biological se-lection bias, as these entities are often driven by potent, TME-independent oncogenes (e.g., NOTCH1 mutations, NPM1-ALK fusions) conducive to immortalization. Conversely, the majority of peripheral T-cell lymphoma (PTCL) subtypes, which are frequently TME-dependent and clinically aggressive, remain "preclinical orphans" with few or no authenticated models. This "preclinical void" constitutes a major bottleneck, impeding mechanistic studies and therapeutic progress. We discuss the limitations of 2D cultures and highlight the necessity of adopting advanced platforms, such as patient-derived xen-ografts (PDX) and 3D organoid systems. These "avatar" models preserve vital tumor het-erogeneity and microenvironmental context, offering superior predictive value. The sys-tematic development and integration of these next-generation models are essential to bridge the translational gap and advance precision medicine for all patients with T-cell malignancies.

Abstract: Acne vulgaris remains a common condition, with current topical therapies often limited by suboptimal efficacy and tolerability. This study evaluated the efficacy and safety of two novel 1% hydrogen peroxide (H₂O₂) formulations, a cream-gel for facial and a sprayable lotion for truncal mild to moderate acne. 42 participants presenting facial acne and 41 with truncal acne were treated twice daily for 8 weeks. Efficacy was assessed using the Investigator’s Global Assessment (IGA), the Spanish Acne Severity Scale (EGAE), and lesion counts. After 56 days, facial acne severity improved significantly (IGA −26%, p=0.01; EGAE −31%, p=0.01), with reductions in papules (−45%, p=0.017), porphyrin count (−27%, p=0.04), sebum production (−75%, p=0.005), erythema (−35%, p=0.0001), and desquamation (−22%, p=0.02). Truncal acne severity also improved sig-nificantly (IGA −32%, p=0.001; EGAE −45%, p=0.001), with reductions in inflammatory lesions (−60%, p=0.001), porphyrin size and count (-55% and −48%, both p=0.001), ery-thema (−7%, p=0.005), and desquamation (−27%, p=0.001). Both formulations were ac-cepted by the users, with minimal local irritation and high patient satisfaction. Topical 1% H₂O₂ formulations demonstrated significant and well-tolerated efficacy in both fa-cial and truncal acne, supporting their potential as safe and patient-friendly options for managing mild to moderate acne.

Abstract: Background :Mas-related G-protein-coupled receptor b4 (Mrgprb4)-lineage neurons in the peripheral nervous system, were a type of C fibers in the hairy skin. Our prior work demonstrated that these neurons respond to both noxious and innocuous mechanical and thermal stimuli. Ablating them eliminates the pleasant sensation elicited by gentle pressure on the mouse nape. However, their potential role in mitigating pain and pain-related negative emotions in response to somatic stimuli remains unclear. Methods:Animal experiments investigated the pivotal role of Mrgprb4-lineage neurons in mediating the analgesic and anxiolytic effects of transcutaneous electrical nerve stimulation (TENS) applied to the Zusanli (ST36) acupoint. In vivo calcium imaging of lumbar 4 dorsal root ganglia (DRG) neurons in Mrgprb4-GCaMP6s transgenic mice characterized neuronal encode of distinct TENS intensities. Mechanical pain thresholds and anxiety-like behaviors were assessed in a CFA-induced mouse model of comorbid chronic pain and anxiety. To simulate TENS, optogenetic stimulation was applied to the ST36 acupoint in Mrgprb4-ChR2 mice; intrathecal viral injection specifically ablated L3-L5 Mrgprb4-lineage neurons, and TENS effects were evaluated with their gain- or loss-of-function manipulation. Results: 0.5 mA TENS on ST36 ameliorated pain and anxiety-like behaviors in model mice and activated Mrgprb4-lineage neurons. Photostimulation on ST36 induced analgesic and anxiolytic effect in comorbidity of chronic pain and anxiety model of Mrgprb4-ChR2 mice. Ablating these neurons attenuated the therapeutic effects of 0.5 mA TENS in model mice. Conclusion:These genetic engineering-assisted findings may deepen our understanding of the analgesic and anxiolytic effect and mechanism of somatic stimulation and further improve the clinical efficacy.

Abstract: Ambient gamma radiation is a key component of environmental radiation monitoring and is strongly modulated by atmospheric and meteorological processes. This study presents a long-term analysis of near-surface gamma radiation measured in Ponta Delgada (São Miguel Island, Azores), integrating continuous observations from the Portuguese National Alert Network for Environmental Radioactivity (RADNET) with meteorological data. The dataset spans more than a decade and includes a documented instrumental upgrade in 2020, which introduced enhanced sensitivity and radionu-clide identification capability. Results reveal pronounced variability across daily, seasonal, and interannual time-scales. A clear level shift is observed after 2020, attributable to the instrumental up-grade rather than to physical environmental changes, while the temporal structure and seasonal phasing of the series remain preserved. Seasonal analysis shows higher gamma radiation values during autumn and winter and lower values in late spring and summer, consistent with precipitation-driven washout and boundary-layer dy-namics. Generalized Additive Models (GAMs) highlight precipitation, wind speed, and relative humidity as dominant meteorological drivers acting through non-linear rela-tionships. Overall, the results support the use of ambient gamma radiation as an atmospheric in-dicator of boundary-layer processes and meteorological modulation in remote mari-time environments, extending its role beyond routine environmental surveillance.

Abstract: Background: Prognostic prediction for colorectal cancer patients with peritoneal metastasis remains challenging due to clinical and biological heterogeneity. We aimed to evaluate the utility of machine learning, comparing high-performance boosting models with interpretable regression approaches for overall survival (OS) prediction. Methods: We analyzed a multi-institutional registry cohort of 150 colorectal cancer patients with synchronous peritoneal metastases. A total of 124 variables were included; continuous variables were standardized, categorical variables were one-hot encoded, and missing values were imputed using the median. Models included XGBoost, LightGBM, Ridge regression, Lasso regression, and linear regression. Training was performed with 3-fold cross-validation, and hyperparameters were optimized using Optuna. Evaluation metrics included mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²). Model interpretability was assessed using SHAP values, LIME local explanations, and regression coefficients. Results: Boosting models consistently outperformed linear models. XGBoost achieved the best performance (MAE 424, RMSE 526, R² = 0.04), while LightGBM showed comparable accuracy. In contrast, Ridge, Lasso, and Linear regression yielded high errors (MAE > 900, RMSE > 1200) with negative R² values, indicating poor predictive ability. SHAP analysis highlighted systemic inflammation markers (CRP, BUN), surgical assessment of tumor depth, operative factors (time, bleeding), and peritoneal metastasis characteristics as major determinants of OS. LIME analyses further provided case-specific interpretability, identifying feature contributions in long-, intermediate-, and short-term survivors. Conclusion: Boosting models, particularly XGBoost, demonstrated superior performance compared with traditional regression models in predicting OS for colorectal cancer patients with peritoneal metastasis, although absolute predictive accuracy remains modest. Integration of SHAP and LIME linked model outputs with clinically plausible prognostic factors, enhancing interpretability. Ensemble learning may provide a promising adjunct for prognostic assessment and should be validated in larger, genomically enriched cohorts.

Abstract: The cytoplasmic accumulation of TDP-43 aggregates remains a persistent pathological hallmark of neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), and limbic predominant age-related TDP-43 encephalopathy. The cell’s natural clearance mechanisms, the Ubiquitin-Proteasome System (UPS) and the autophagy-lysosome pathway (ALP), frequently fail due to a ‘vicious cycle’ created by the sequestration of essential downstream components by aberrant TDP-43, which interrupts autophagic flux. Classical autophagic activators (e.g., rapamycin) often initiate the pathway but cannot address downstream bottlenecks due to flux failure. This review revisits classical strategies and discusses newer approaches to modulate TDP-43 clearance, including TFEB activators, PROTACs (proteolysis-targeting chimeras), and antisense oligonucleotides (ASOs). We propose that adopting multi-targeting strategies and developing better biomarkers are vital for clinical success.

Abstract: Elastocaloric cooling is an emerging solid-state refrigeration technology that leverages the latent heat exchange of shape memory alloys under mechanical stress. This study inves-tigates the energy performance of a solid-to-solid elastocaloric cooling heat pump to en-hance heat transfer efficiency and overall system performance. A Matlab based numerical model, developed using the finite volume method, was employed to simulate the system. The energy performances of the elastocaloric heat pump are analyzed by varying the fre-quency of the cycle, the elastocaloric refrigerants and the types of thermal diodes, from ideal up to realistic Peltier switches. The results demonstrate that the strategic use of thermal diodes significantly improves heat flow directionality, reducing thermal losses and enhancing the efficiency of the elastocaloric cooling process. These findings contrib-ute to the development of more efficient solid-state cooling technologies, offering a viable alternative to conventional systems also for electronic circuits cooling applications.

Abstract: Enterprise-scale knowledge management faces significant challenges in integrating multi-source heterogeneous data and enabling effective semantic reasoning. Traditional knowledge graphs often struggle with implicit relationship discovery and lack sufficient semantic understanding for complex question answering. To address these limitations, we introduce a unified construct--align--reason framework, the large ontology model (LOM). We first build a dual-layer enterprise ontology from structured databases and unstructured text, subsequently fusing these sources into a comprehensive enterprise ontology. To enable instruction-aligned reasoning, we propose a unified three-stage training pipeline: ontology instruction fine-tuning to improve structural understanding; text-ontology grounding to strengthen node semantic encoding; and multi-task instruction tuning on ontology-language pairs with curriculum learning to enhance semantic reasoning and generation. We also construct comprehensive training and evaluation datasets covering diverse ontology reasoning tasks. On this benchmark, our 4B-parameter LOM achieves 89.47\% accuracy and outperforms DeepSeek-V3.2 on complex graph reasoning, indicating effective fusion of ontology structure and language.

Abstract: Canada’s fossil fuel production is highly subsidized despite the pollution. In the Province of Alberta subsidies for oil and gas total approximately CAD$1.78 billion/year. This study quantifies the impacts of shifting fossil-fuel subsides towards solar photovoltaic (PV) capital investments. Although solar is already the lowest-cost form of electricity, such a subsidy shift would accelerate the renewable energy transition. This study found such a shift would enable installation of 1.53 GW of new solar PV capacity annually with the current investment tax credit (ITC) or 1.07 GW without it. These new solar PV systems can generate 2.02 TWh/year of clean electricity, if ITC is applied on capital investments and 1.41 TWh/year without it. The solar electricity is cost-competitive with natural gas generation, with levelized costs ranging from $49.01 to $61.97 CAD/MWh with ITC ($63.62 to $80.45 CAD/MWh w/o ITC) across Alberta. High-solar-resource locations in Alberta including Lethbridge ($49.01 CAD/MWh) and Calgary ($49.28 CAD/MWh) achieve lower costs than natural gas ($51.80 CAD/MWh). This excludes carbon externalities, fuel price volatility, and the long-term operational subsidies required to maintain fossil fuel competitiveness, suggesting that solar PV is already an economically rational alternative. Shifting Alberta’s fossil fuel subsidies is a solution for Canada's 2050 net-zero commitments. Solar-fossil fuel generation parity would be achieved by 2040 with ITC credits or 2045 without it. The subsidy redirection can reduce Alberta's grid emission intensity from the current 450 kg-CO₂e/MWh to 68.8 kg-CO₂e/MWh (with ITC) or 119.2 kg-CO₂e/MWh (without ITC) by 2050, representing reductions of 84.7% and 73.5%, respectively.

Abstract: Image segmentation is a fundamental component of vision-based agricultural robotics, enabling accurate fruit localization, disease detection, and automated harvesting. However, real-world strawberry fields present significant challenges due to irregular fruit morphology, dense foliage occlusions, variable ripeness, and strong illumination variability. Moreover, segmentation models trained on a single dataset often fail to generalize across domains, limiting their practical deployment. This paper presents a comprehensive benchmark of classical computer vision methods, convolutional neural networks, instance-based models, and transformer-based architectures across three heterogeneous public strawberry datasets: Db1 (instance segmentation), Db2 (lesion segmentation), and Db3 (semantic segmentation). A unified preprocessing and evaluation framework is adopted to ensure fair comparison using standard metrics, including Intersection-over-Union (IoU), Dice coefficient, Precision, and Recall. Extensive in-domain experiments demonstrate that deep learning models significantly outperform classical approaches, with U-Net and SegFormer achieving IoU values above 0.95 on Db1 and up to 0.83 on Db3. Cross-domain zero-shot evaluations reveal a substantial generalization gap, with U-Net suffering IoU drops of up to 100\%, while SegFormer consistently exhibits improved robustness and reduced cross-domain degradation across most transfer scenarios. To our knowledge, these results establish the first systematic multi-dataset benchmark for strawberry segmentation under domain shift, highlighting the importance of transformer-based architectures for robust agricultural perception and providing practical insights for real-world robotic deployment.

Abstract: Gestational diabetes mellitus (GDM), one of the most common metabolic complications of gestation, affects approximately 10–15% of pregnancies and represents a significant challenge for obstetrics and diabetologists in the attempt of reducing adverse maternal and fetal outcomes. Medical nutrition therapy remains the cornerstone of GDM management, alongside lifestyle modification and pharmacological treatment in presence of unachieved glycemic targets. However, current dietary recommendations primarily emphasize nutrient composition and caloric intake, often without fully considering the temporal aspects of food intake. Chrono-nutrition is an emerging field that investigates the interaction between meal timing, circadian rhythms, and metabolic regulation. Increasing evidence indicates that glucose metabolism and insulin sensitivity exhibit marked diurnal variations, which may be further amplified in women with GDM, resulting in time-dependent differences in postprandial glycemic responses. The narrative review summarizes current evidence on the role of chrono-nutrition in GDM by integrating mechanistic insights with findings from observational and interventional human studies. Although the available literature is limited by heterogeneity and a paucity of well-designed randomized controlled trials, the convergence of biological plausibility and emerging clinical data supports chrono-nutrition as a low-risk refinement of standard medical nutrition therapy. Incorporating temporal aspects of eating into dietary counseling may enhance glycemic management and contribute to more physiologically aligned and personalized nutritional strategies for pregnancies complicated by GDM.

Abstract: Background and Objectives: Procalcitonin (PCT) kinetics have emerged as a promising prognostic marker in sepsis; however, their interpretation is complicated by dynamic changes in renal function during acute illness. Most previous studies relied on a single baseline estimated glomerular filtration rate (eGFR), which may lead to misclassification in patients with evolving acute kidney injury. This study aimed to evaluate the prognostic value of procalcitonin kinetics (ΔPCT) for 30-day mortality in critically ill patients with sepsis or septic shock by incorporating serial kinetic eGFR measurements and renal function–adapted ΔPCT cut-off values based on the mean kinetic eGFR during the first 72 hours of intensive care unit (ICU) admission. Materials and Methods: This retrospective cohort study included 106 adult patients admitted to a general ICU with sepsis or septic shock. Procalcitonin levels were measured serially, and ΔPCT was calculated as the logarithmic ratio of follow-up to baseline values. Renal function was assessed using kinetic eGFR calculated at serial time points from ICU admission, and the mean kinetic eGFR over the first 72 hours was used for renal function stratification. Multivariable logistic regression models incorporating ΔPCT and severity scores (APACHE II and SOFA) were constructed, and discriminative performance was evaluated using receiver operating characteristic (ROC) curve analysis. Results: Thirty-day mortality was 43.4%. ΔPCT was a strong independent predictor of mortality across all models. When stratified according to mean kinetic eGFR, optimal ΔPCT cut-off values expressed as absolute proportional PCT decline differed markedly by renal function: an 81.2% decrease in PCT best discriminated mortality in the overall cohort, whereas renal function–specific thresholds were 63.7% for patients with mean kinetic eGFR <30 mL/min, 87.6% for those with kinetic eGFR 30–59 mL/min, and 92.6% for patients with kinetic eGFR ≥60 mL/min. The combination of APACHE II and ΔPCT demonstrated the highest discriminative performance (AUC 0.946). Conclusions: Procalcitonin kinetics provide robust prognostic information in sepsis when interpreted alongside dynamic renal function. Using serial kinetic eGFR measurements and the 72-hour mean renal function enables renal function–adapted ΔPCT cut-off determination and may improve mortality risk stratification in critically ill septic patients.

Abstract: The classical scientific paradigm, centered on the ideal of a passive observer discovering pre-existing facts, is fundamentally challenged by insights from quantum mechanics and complex systems, where measurement is inherently interventional (Heisenberg, 1927; Barad, 2007). We propose the Ze System framework, a radical epistemological shift that redefines scientific inquiry as the active engineering of predictive conflicts to provoke latent reality into manifesting observable phenomena. The framework posits that a substantial portion of reality exists not as localized facts but as a high-entropy "wave" state of unactualized potentialities. By deploying competing, precise predictive models (e.g., P1 and P2 and applying a minimal, targeted intervention—the Ze probe (π)—this methodology forces a system into a crisis of choice. This forced localization is an entropic transaction: it expends energy, increases disorder, and irrevocably annihilates alternative potentials. Crucially, truth is not found in a model's confirmation but is forged in the structured, interpretable residual error (ϵL) that persists when a "greedy" model, equipped with a "cheating lever" to shape its own data, encounters an unyielding latent structure (L) (Tkemaladze, 2026). This paper details the ontological, methodological, and ethical foundations of this second-order science, framing Ze systems as entropy engines that strategically invest disorder to purchase certainty, thereby recasting the scientist's role from detached observer to accountable architect of co-created facts.

Abstract: Poly(lactic acid) (PLA) is extensively used in food-contact applications due to its bio-based origin, compostability, and transparency; however, its limited resistance to thermo-oxidative degradation remains an obstacle for applications involving repeated thermal exposure. The moderate but repetitive heating conditions commonly encountered during food use and pre-recycling stages were analyzed for the samples filled with algal biomass and rosemary extract, aditives accepted for use in food industry. In this context, the present study introduces a comparative and application-driven approach by evaluating the effect of food-grade fillers—rosemary extract, spirulina biomass, and kelp biomass—incorporated at low loadings (0.5–3 wt%) on the thermal and oxidative behavior of PLA subjected to repeated heating at 80 °C. The presented results show algal biomasses as multifunctional fillers and benchmarks their performance against a well-established natural extract. By combining DSC, FTIR, and chemiluminescence analyses, the study aims to clarify whether such bio-fillers act as stabilizing or destabilizing factors under realistic service-life thermal stress. This strategy provides insight into the suitability of algae-based fillers for food-contact PLA materials from both performance and recyclability perspectives.

Abstract: The quality of governance is a key driver of resource mobilisation in a context marked by successive shocks that exacerbate fiscal imbalances. This study aims to analyse the role of institutional quality in the relationship between public expenditure and tax revenue in a panel of 162 countries, broken down into developed and emerging economies between 2000 and 2023. Using Dumitrescu and Hurlin's (2012) causality tests and the cross-sectional autoregressive model with staggered lags (CS-ARDL) to control for cross-sectional heterogeneity and cross-dependence, the results reveal a bidirectional causality linking expenditure and revenue for the entire panel; emerging countries are more sensitive to fiscal policies; public expenditure significantly stimulates tax revenue in the short and long term, with an effect amplified by institutional quality; long-term sustainability depends crucially on the institutional framework. This study highlights the need for targeted institutional reforms and fiscal rules differentiated according to countries' level of economic development.

Abstract: Acute Myeloid leukemia (AML) is characterized by differentiation arrest, driving blast proliferation and abnormal blood formation. While differentiation therapy revolu-tionized acute promyelocytic leukemia (APL) with all-trans retinoic acid (ATRA) and arsenic trioxide (ATO), its extension into non-APL AML has been limited until recent targeted agents. This narrative review synthesizes preclinical and clinical evidence in-to differentiation-inducing therapy, with a focus on IDH1/2, FLT3 and menin inhibi-tors. Following SANRA guidelines, we searched pubmed (2010-sep,2025) for clinical trials and key preclinical studies, with particular attention to the molecular mecha-nism of differentiation induction, clinical efficacy and management of differentiation syndrome (DS). IDH1/2 inhibitors (ivosidenib, enasidenib, olutasidenib) yield overall response rate (ORR) of 30-94% in AML with DS in 10-19%. Menin inhibitors (re-vumenib, ziftomenib, enzomenib, bleximenib) achieve an ORR of 33-88% in KMT2A-rearranged or NPM1-mutated AML, with DS in 10-25% and QT prolongation as key toxicities. FLT3-inhibitors (gilteritinib, quizartinib) improve survival in FLT3-mutated AML with DS in 1-5%. Resistance mutations limit durability and com-binations enhance efficacy. Differentiation therapy represents a paradigm shift to-wards non-cytotoxic AML management. Improved recognition of DS and rational combination approaches will be essential to maximize therapeutic benefit. Future re-search should address mechanisms of resistance and biomarkers to achieve cure be-yond APL.

Abstract: Plastic manufacturing depends heavily on petroleum-derived monomers like terephthalic acid, the main component of polyethylene terephthalate (PET). However, the depletion of fossil resources and increasing environmental concerns have heightened the need for sustainable alternatives. Lignocellulosic biomass has emerged as a promising resource due to its renewable, abundant, and eco-friendly nature. Understanding its chemical composition enables conversion of this biomass into platform chemicals, such as 2,5-furandicarboxylic acid (FDCA) and lactic acid, derived from cellulose and hemicellu-lose. These can be polymerized into bioplastics such as polyethylene furanoate (PEF) and polylactic acid (PLA), offering greener alternatives to fossil-based plastics. PEF features rigid furan rings that enhance thermal stability, mechanical strength, and barrier proper-ties, and reduce gas permeability compared to PET. PLA is a renewable, biodegradable plastic widely used in packaging and medical applications. This review covers the chem-ical makeup of lignocellulosic biomass cellulose, hemicellulose, and lignin, and various pretreatment strategies, chemical, physicochemical, and physical, to overcome biomass recalcitrance and improve conversion efficiency. It also highlights recent catalytic ad-vances in transforming lignocellulosic carbohydrates into bioplastic precursors such as FDCA and lactic acid. Lastly, the review discusses polymerization pathways for produc-ing PEF and PLA, emphasizing their role in reducing the environmental impact of poly-mer manufacturing and promoting green chemistry principles.