Abstract: Autophagy restricts intracellular bacteria through xenophagy, yet many bacterial pathogens remodel this pathway to avoid degradation or support intracellular persistence. This review compares bacterial modulation of autophagy in mammalian cells and invertebrate vectors, with focusing on initiation, cargo recognition, autophagosome maturation, and autophagosome-lysosome fusion. In mammals, direct effector-host interactions have been defined for several pathogens. Legionella pneumophila RavZ effector removes lipidated ATG8/LC3 from autophagosome membranes. Salmonella typhimurium effectors interfere with AMPK-mTOR signaling, ATG16L1 recruitment, and Rab1A/TRAPPIII-dependent initiation. Shigella flexneri and Listeria monocytogenes mask bacterial surfaces to limit autophagic recognition. Mycobacterium tuberculosis, Anaplasma phagocytophilum, Staphylococcus aureus, and Coxiella burnetii exploit autophagy-related compartments while restricting degradative maturation. In invertebrate systems, especially psyllid vectors of Candidatus Liberibacter asiaticus and Candidatus Liberibacter solanacearum, current evidence indicates pathogen-associated changes in ATG gene expression, endoplasmic reticulum-associated vacuole formation, calcium and ROS signaling, lysosomal activity, and vitellogenin-linked regulation. However, these mechanisms are supported mainly by transcriptomic, microscopy-based, pharmacological, and protein-interaction evidence, and direct effector-mediated control of psyllid autophagy remains insufficiently demonstrated. We propose that bacterial control of autophagy follows conserved logic across host systems. This logic includes suppression of xenophagic recognition, remodeling of autophagosome biogenesis, and uncoupling of autophagosome formation from lysosomal dagradation. Defining which mechanisms are directly effector-driven and which are host-response signatures will be essential for translating autophagy biology into antimicrobial, vector-control, and plant disease-management strategies.

Abstract: This study presents a comprehensive analysis and predictive modeling framework for the axial compressive strength (f_cc) and ultimate axial strain (ε_cu) of concrete columns confined with fiber reinforced polymer (FRP) systems. Large databases comprising 3312 for f_cc and 3319 for ε_cu were compiled from the literature, encompassing a wide range of key variables, including unconfined concrete strength from 7 MPa to 204 MPa and diverse FRP confinement configurations. The datasets were subjected to extensive statistical and multivariate analyses, to identify the primary factors influencing axial behavior and guide feature selection for predictive modeling. Three groups of machine learning (ML) algorithms were subsequently considered: (i) artificial neural networks (including multilayer perceptrons with one and two hidden layers), (ii) kernel-based models (Gaussian process regression and support vector regression), and (iii) tree-based ensemble models (gradient boosting machine, eXtreme gradient boosting, and light gradient boosting machine). Hyperparameters were optimized using grid-search cross-validation, while feature-importance analyses were performed to quantify the contribution of each input variable. Among all ML models, eXtreme gradient boosting demonstrated superior predictive performance, effectively capturing the nonlinear and multivariate interactions governing confinement effectiveness. Comparative analysis with the top-performing regression-based formulations further highlighted the accuracy, robustness, and generalization capability of the eXtreme gradient boosting model. The findings provide a data-driven and interpretable framework for the design and prediction of FRP-confined concrete columns.

Abstract: In this article, we offer a complete, self-contained, and entirely elementary proof of the mean-square estimate for the Chebyshev function. From this we deduce the convergence of the integral is valid, thus proving the validity of the Riemann hypothesis. The proof primarily employs elementary estimates of the Chebyshev function, the Cauchy-Schwarz inequality, and a dyadic decomposition (with Abel summation applied in the appendix), in which the argument results of this article are already optimal within the elementary framework and sufficient to derive the convergence of the required integral that it is a suffficient condition for the Riemann hypothesis. In particular, the appendix of this paper provides a theoretical complement linking integral convergence, pointwise bounds and analyticity, and concludes that the well-known $o$-bound is valid, thereby reconfirming the validity of the Riemann hypothesis. In other words, we give a self-contained elementary proof for the mean-square estimate that $\displaystyle\int_2^{X} \bigl(\psi(t)-t\bigr)^{2}\,dt = O(X^{2}\log^{2} X),$ where $\psi(x)$ is the Chebyshev function. From this we deduce that $\displaystyle\int_{1}^{\infty}\frac{|\psi(x)-x|}{x^{\frac{3}{2}+\varepsilon}}\,dx < \infty$ holds for every $\varepsilon>0,$ thus concluding the integral $\displaystyle \int_1^{\infty} \frac{\psi(x)-x}{x^{\frac{3}{2}+\varepsilon}}\,dx$ converges absolutely for every $\varepsilon>0,$ so that the integral $\displaystyle \int_1^{\infty} \frac{\psi(x)-x}{x^{\frac{3}{2}+\varepsilon}}\,dx$ converges conditionally for every $\varepsilon>0,$ whereas the integral converges conditionally $\iff \text{RH},$ so then the Riemann hypothesis is true. In particular, we conclude that the property of absolute convergence of the integral $\displaystyle\int_1^{\infty} \frac{\psi(x)-x}{x^{\frac{3}{2}+\varepsilon}}\,dx$ for every $\varepsilon>0$ is equivalent to the property of conditional convergence of the integral $\displaystyle\int_1^{\infty} \frac{\psi(x)-x}{x^{\frac{3}{2}+\varepsilon}}\,dx$ for every $\varepsilon>0,$ either of which is equivalent to the property of the $o$-bound: $|\psi(x)-x| = o(x^{\frac{1}{2}+\varepsilon})$ for every $\varepsilon>0,$ and all of them imply the $O$-bound: $\psi(x)-x= O(x^{\frac{1}{2}+\varepsilon})$ is also valid for every $\varepsilon>0,$ thus reconfirming the validity of the Riemann hypothesis.

Abstract: Abstract Background: Scoliosis in children leads to complex physical, psychosocial, and functional impairments, yet evidence from low-resource settings is limited. Objective: To evaluate health-related quality of life (HRQoL) in Ethiopian children with scoliosis using WHODAS 2.0 and EQ-5D-Y, and to identify clinical and demographic correlates of disability. Methods: A hospital-based cross-sectional study was conducted at CURE Children’s Hospital of Ethiopia (March 2024–June 2025). Ninety-seven children aged 6–15 years with confirmed scoliosis were assessed. WHODAS 2.0 (36-item) captured disability across six domains, while EQ-5D-Y measured physical, psycho-social, and pain-related HRQoL. Clinical severity indicators (Cobb angle, trunk rotation, thoracic morphomet-rics, anthropometrics) were recorded. Analyses in Python 3.12 included descriptive statistics, correlation, and multiple regression to examine associations between scoliosis severity and HRQoL outcomes. Results: Ninety-seven Ethiopian children diagnosed with scoliosis (mean age 11.4 years, mean Cobb angle 73.5°) were assessed; congenital scoliosis was most common (45.4%), followed by idiopathic (38.1%) and neuromuscular (16.5%). WHODAS 2.0 revealed substantial disability, particularly in mobility (46.4%) and social participation (62.8%), with life activities and participation as the strongest contributors. Greater deformity, younger age, and smaller body size were linked to worse outcomes, with neuromuscular and very severe thoracolumbar cases most affected. EQ-5D-Y showed marked HRQoL impairments across domains, with psychological distress (mean 2.24/3), pain, and self-care limitations emerging as key burdens. The mean EQ score was 8.89/15 (59.2%), indicating reduced quality of life. Regression analyses confirmed pain, psychological distress, and self-care limitations as the strongest predictors, with higher Cobb angle and ATR degree correlating with poorer HRQoL. Conclusions: Ethiopian children with scoliosis experience significant multidimensional impairments in HRQoL, with mobility, social participation, pain, and psychological distress as dominant burdens. WHODAS 2.0 and EQ-5D-Y proved complementary in capturing these impacts, supporting their use for early detection, clinical care, and public health planning in resource-limited settings.

Abstract: We study a coefficient-space normalization for univariate polynomials: given P(x) = aₙxⁿ + ... + a₁x + a₀, shift to Q(y) = P(y+φ) at a critical point φ satisfying P′(φ) = 0. This eliminates the linear coefficient of Q and, for the companion matrix construction, typically reduces the condition number κ substantially. The method generalizes a classical quadratic identity (the vertex of a parabola sits at −b/2a) to arbitrary degree, and we call it the Vertex Shift Method (VSM). We establish that the shifted companion matrix is similar to the spectrally translated original companion matrix, a direct-conditioning selection rule for choosing among the n−1 available critical points, a dynamic extension (Curved Shift) for time-varying polynomial families together with its breakdown condition and a hybrid tracking algorithm, and a lower bound exposing a structural limitation of similarity-based conditioning transformations in general, including but not limited to diagonal balancing. Re-audited benchmarks on four polynomial families give conditioning gains from 1× (correctly, no gain, on an already-balanced Chebyshev polynomial) to 3519× (Wilkinson W₁₀); five additional legacy instances are reported separately because their exact original data were not preserved, with one of them (Wilkinson W₁₅) independently re-audited under the current method. We report failure modes and the regimes in which balancing outperforms VSM as openly as the regimes in which it does not, and give two validated applications: a singularity-avoiding companion representation for singular Markov generator polynomials, and Kerr black hole geodesic conditioning, whose coefficient vector is reconstructed exactly from explicit physical parameters using the primary-source radial-potential formulas of Compère and Druart and Teo.

Abstract: Listeria monocytogenes is a foodborne pathogen that can survive acid stress. The glutamate decarboxylase (GAD) system is an important system that helps L. monocytogenes to survive in acidic environments. A previous report showed that the two-component system LiaSR negatively regulates the gadT2/gadD2 cluster and is involved in acid resistance in L. monocytogenes 10403S. This study aimed to clarify how LiaSR regulates gadT2/gadD2. We found that the expression of gadT2/gadD2 was positively regulated by the transcription factor MntR. Survival assays showed that mntR deletion significantly reduced the survival rate of L. monocytogenes under different acidic conditions. Moreover, the expression level of MntR was negatively regulated by the two-component system LiaSR under different pH conditions as shown by the reporter assay. The electrophoretic mobility shift assay (EMSA) showed that MntR directly bound the gadT2/gadD2 promoter and LiaR directly bound the mntR promoter. Finally, the liaSR/mntR double deletion significantly downregulated the expression of gadT2/gadD2 under acidic conditions and weakens the acid resistance of L. monocytogenes, indicating that MntR plays an important regulatory role in the LiaSR-MntR-gadT2/gadD2 regulatory axis. In summary, we demonstrated that the two-component system LiaSR negatively regulates MntR to inhibit the expression of gadT2/gadD2 cluster to regulate acid resistance of L. monocytogenes.

Abstract: Cannabis sativa and Curcuma longa have traditionally been used for the management of skin disorders; however, scientific evidence supporting their combined topical use in eczema and psoriasis remains limited. To characterize the phytochemical composition, evaluate the biological activities, investigate potential molecular mechanisms, and assess the preliminary clinical performance of a topical herbal cream containing C. sativa and C. longa extracts. Ethanolic extracts were analyzed using UHPLC and GC-MS. Antioxidant, antibacterial, cytotoxicity, and nitric oxide inhibition assays were performed. Molecular docking studies were conducted against inflammation-related protein targets. A pilot clinical study evaluated the effects of the herbal cream in patients with eczema and psoriasis over four weeks using EASI, PASI, and DLQI scores. The herbal formulation contained cannabinoids, terpenoids, and curcuminoids with measurable antioxidant activity. The extract exhibited low cytotoxicity and moderate inhibition of nitric oxide production in LPS-stimulated macrophages. Docking analyses suggested favorable interactions between selected phytochemicals and inflammation-associated targets. Preliminary clinical observations indicated improvements in EASI, PASI, and DLQI scores among study completers. The herbal cream demonstrated antioxidant and anti-inflammatory properties and showed preliminary clinical potential in inflammatory skin disorders. Larger controlled clinical studies are required to confirm efficacy and establish therapeutic value.

Abstract: Rapid urbanization and inadequate sanitation infrastructure threaten water security in many sub-Saharan African cities. This study presents the first integrated assessment of groundwater, surface water, and wastewater quality in Bangui, Central African Repub-lic, using physicochemical, trace metal, and microbiological indicators. A total of 28 sampling sites were analyzed using standardized methods, including ion chromatog-raphy, ICP-OES, ICP-MS, and membrane filtration. Results revealed a clear contamina-tion gradient. Wastewater showed the highest electrical conductivity, turbidity, chlo-ride concentrations, and microbial loads, reaching 2.41 × 10⁶ CFU/100 mL for total coli-forms and 1.93 × 10⁶ CFU/100 mL for fecal coliforms. Groundwater exhibited high ni-trite levels and low dissolved oxygen, indicating vulnerability to sewage infiltration. Surface waters were characterized by high turbidity and widespread fecal contamina-tion despite relatively good oxygenation. In contrast, trace metal concentrations gener-ally remained below World Health Organization guideline values. Geochemical anal-yses identified distinct elemental signatures for each water type. Microbial contamina-tion emerged as the dominant factor affecting water quality. High fecal coliform/fecal streptococci ratios (13.08-22.16) indicated predominantly human-derived pollution linked to untreated wastewater and inadequate sanitation systems. The association between elevated nitrite concentrations and fecal indicators suggests active contami-nation pathways connecting wastewater, surface water, and shallow aquifers. These findings highlight the urgent need for improved wastewater management, groundwa-ter protection, and long-term monitoring to ensure sustainable urban water security in Bangui.

Abstract: Background:The impressive progress of research in the cardiovascular sciences in the last several decades has contributed to the development of highly advanced therapeutic tools for the management of chronic heart failure (CHF). In spite of whole array of pharmacologic and invasive treatment methods, the mortality rate in CHF is still high. The growing evidence indicates the significant influence of central nervous system (CNS), cognitive functions and thermoregulation on aerobic capacity, symptoms and prognostication in CHF. The goal of our review is to present the recent advancements in the experimental and clinical research on the brain-heart interactions in CHF. Methods: A narrative review was conducted using PubMed, Scopus, Web of Science, and Google Scholar. Results: There is a close link between CNS functions ( cognitive functions, thermoregulation), symptoms and prognostications in CHF, which may contribute to the development of brand-new therapeutic tools and schemes in the nearest future. Conclusions: Brain processing has enormous impact on the course of CHF. The in-depth understanding of CNS mechanisms controlling physical fitness in CHF may contribute to the implementation of brand-new treatment methods and creation of interdisciplinary medical teams targeting various parts and functions of CNS involved in the pathophysiology of the disease.

Abstract: The Hwange Mining Area (HMA) is located in the Mid-Zambezi Karoo Basin in northwestern Zimbabwe, is a major centre for coal mining, processing and power generation. This study evaluates the ecological and public health risks associated with these activities, focusing on radionuclide con-centrations in coal and surrounding soils. Samples were collected from four locations: (i) Hwange Colliery Company (underground and open cast mines), (1) Zambezi Gas open cast operations, (iii) Residential areas and (iv) Hwange Thermal Power station. Radionuclide concentrations were measured using Hyper-Pure Germanium (HPGe) gamma spec-trometry. Results showed lower concentrations in mining areas compared to elevated levels in resi-dential and combustion environments. Radiological hazard indices, including radium equivalent (Raeq), external hazard index (Hex) and internal hazard index (Hin) values remained below recom-mended safety limits, indicating no immediate radiological risk. However, the absorbed dose rates, annual effective doses and excess lifetime cancer risk (ELCR) were higher in residential and combustion areas, with ELCR values exceeding the global average. These findings suggest a potential long-term public health risk associated with chronic exposure. The study underscores the need for continuous environmental monitoring strengthened regulatory control, targeted radiation protection strategies to safeguard workers and nearby communities.

Abstract: Background: Colorectal cancer (CRC) remains a major cause of cancer-related mortality worldwide and ranks third in Colombia. Despite therapeutic advances, limitations such as reduced efficacy, adverse effects, and drug resistance persist. In the search for novel ther-apeutic strategies, molecular hybridization of melatonin and furanochalcone—compounds with antioxidant and antitumor properties—led to the synthesis of a novel hybrid mole-cule Mel-Fur (6f), a promising candidate for CRC treatment. Objectives: The aim of this study was to develop and validate an HPLC-DAD analytical method for quantification of Mel-Fur in serum and murine organ matrices. Methods: Chromatographic analysis used an Agilent Series 1200 system with a diode array detector and a C30 column under opti-mized conditions: acetonitrile: water (85:15, v/v) as mobile phase, flow rate 0.8 mL/min, detection at 342 nm, retention time 4.3 min. Results: The method fulfilled ICH Q2 (R1) and FDA validation guidelines, showing high selectivity, excellent linearity (R² > 0.999), sensitivity, precision, accuracy (RE% and CV% < 15%), recovery above 93%, and analyte stability for up to 8 days. The validated method was applied in a pilot in vivo biodistribu-tion study. Following oral administration of a single dose of Mel-Fur (1000 mg/kg) in BALB/c mice, rapid absorption and elimination were observed, with measurable systemic exposure and effective distribution into peripheral tissues. Pharmacokinetic analysis re-vealed preferential accumulation in lungs and liver, with sustained presence in colon. Conclusions: These findings provide a robust analytical tool and preliminary pharmaco-kinetic insights supporting further preclinical development of Mel-Fur as a potential therapeutic candidate for CRC.

Abstract: Quantitative fluorescence analysis of aromatic pollutants in aquatic environments is frequently compromised by the primary inner filter effect (PIFE) induced by coexisting chromophoric species. In order to address this challenge, we propose a multi-component concentration quantification correction model that integrates transmittance absorbance and lateral (90°) fluorescence intensity. By establishing a coupled model of the excitation light decay dynamics and fluorescence emission, where styrene was used as the target analyte and anthracene/phenanthrene played the role of representative interferents, the inherently nonlinear PIFE was transformed into a tractable linear regression problem. The experiment shows that: under the coexistence of anthracene and phenanthrene, the model reduces the detection deviation of styrene from 40-63% to within 10%, and the correction accuracy of the three-component mixed system is increased by 3-5 times.The model in this work provides a theoretical framework for fluorescence quantitative analysis in complex systems, and also offers a new method for high-precision online monitoring of aromatic organic pollutants.

Abstract: The concept of Industry 5.0 is gaining wide recognition both in academia and in business practice. On the other hand, the assessment of maturity using the maturity model for the implementation of the 5.0 concept is characterized by a much lower degree of recognition. In the face of the increasing complexity of global supply chains and the need to ensure the efficient flow of materials, the authors postulate a model of maturity for the implementation of the Industry 5.0 concept, which will become a key subject of scientific research, and the solutions developed within it will be gradually implemented by companies striving to increase their competitiveness. This study presents the conceptual framework of the maturity model for the implementation of the Industry 5.0 concept in logistics industry enterprises, the aim of which is to enable organizations to assess the current level of advancement in the implementation of the elements of the Industry 5.0 concept and to develop a strategic improvement action plan. This model was created on the basis of a review of the literature on the subject, covering both industry issues and existing models of organizational maturity. Its structure has been designed and empirically verified in such a way as to form the foundation for the development of a set of practical guidelines supporting the logistics transformation towards the concept of Industry 5.0.

Abstract: We derive the fermion mass hierarchy from the weighted Cayley graph of a 44-vector $\mathbb{Z}_3$-triality vacuum lattice, with zero free parameters. Edge weights $A_{ij}=1/\|\mathbf{v}_i-\mathbf{v}_j\|^2$---the unique discrete Laplace--Beltrami prescription---encode vacuum impedance. The mass matrix $M_{ab}=\sum_{h\in\mathcal{H}}G_w(f_a,h)G_w(h,f_b)$ is derived as the zero-momentum fermion self-energy with Higgs exchange on the graph, with the overall normalization locked by the algebraic rigidity condition $y_t=1$. The absolute mass scale emerges from a 12-level nested chain compressing $M_{\rm Pl}$ to $v_{\rm geo}\approx 185$~GeV (RG-evolved to the experimental $v_{\rm EW}=246$~GeV). Shell assignments are uniquely determined by $\mathrm{SU}(3)$ coupling range, $\mathrm{SU}(2)_L$ doublet structure, and color-singlet/hypercharge selection rules---not by fitting. The bare lattice predictions are: $m_t:m_c:m_u=1:0.0162:1.3{\times}10^{-5}$, $m_b:m_s:m_d=1:0.0145:8.9{\times}10^{-5}$, $m_\tau:m_\mu:m_e=1:0.0144:9.9{\times}10^{-7}$. After quantitative 1-loop SM renormalization group evolution from $M_{\rm Pl}$ to $M_Z$, all nine mass ratios converge to within $12\%$--$40\%$ of experiment, with the five-order-of-magnitude hierarchy correctly reproduced. The framework simultaneously determines the absolute electroweak scale ($246$~GeV) and the top quark mass ($174$~GeV) from $M_{\rm Pl}$ as the sole dimensionful input.

Abstract: Purpose: To report a child with severe treatment resistant juvenile idiopathic arthritis associated uveitis (JIAU), a condition that typically presents as chronic anterior uveitis, although severe panuveitis with retinal vasculitis may rarely occur.Methods: We describe the clinical course, treatment response, and challenge, dechallenge, and rechallenge sequence in a child with severe refractory JIAU treated with combined infliximab and tofacitinib therapy.Results: The patient showed marked improvement in ocular inflammation, macular edema, and visual acuity following addition of tofacitinib to ongoing infliximab therapy. Inflammation recurred after holding infliximab despite continuation of tofacitinib and improved again following reintroduction of infliximab.Conclusion: This challenge, dechallenge, and rechallenge sequence suggests that neither infliximab nor tofacitinib alone provided adequate disease control, whereas their combination was associated with sustained suppression of ocular inflammation. Combined tumor necrosis factor alpha and Janus kinase inhibition may represent a rescue therapeutic option for carefully selected patients with severe refractory JIAU, although its long term safety and efficacy require further evaluation.

Abstract: Drought tolerance assessment in maize requires the integration of yield performance and multiple stress-related indicators to identify genotypes combining productivity and environmental adaptation. In this study, twenty maize genotypes were evaluated under optimal and drought-stress conditions using grain yield parameters and fifteen drought tolerance indices. Significant variation was observed for yield potential (Yp), stress yield (Ys), and index-based drought responses. Productivity-related indices, including Stress Tolerance Index (STI), Geometric Mean Productivity (GMP), Harmonic Mean (HM), Modified Stress Tolerance Index (MSTI), and Relative Efficiency Index (REI), showed strong associations with yield performance and effectively identified stable, high-performing genotypes. Correlation analysis revealed a clear separation between productivity-oriented and susceptibility-related indices. Principal Component Analysis explained 97.5% of total variation in the first two components and separated indices into productive adaptation and stress susceptibility groups. Cluster analysis classified genotypes into four distinct groups, identifying genotypes with high yield potential, superior drought adaptation, intermediate performance, and stress sensitivity. Genotypes G11, G12, and G13 demonstrated the highest yield retention under drought, while G3, G15, and G18 combined high productivity with acceptable stress performance. The integration of multi-index analysis and multivariate approaches provides an effective framework for selecting drought-resilient maize genotypes.

Abstract: Korla fragrant pear diseases and pests detection faces challenges such as significant object scale variation, multi-organ target confusion, and limited computational resources for real-time inference on edge devices. To address these issues, this study proposes a lightweight object detection model, KFP-YOLO, based on YOLO26n. A multi-organ dataset, the Korla fragrant pear diseases and pests dataset (KFP-PDD), was collected, covering pear leaves, fruits, and flowers and containing 11 classes including healthy and diseased samples. To reduce computational cost while maintaining effective feature representation, an ADown lightweight downsampling module is introduced. A C3-PD feature extraction module is designed by integrating Partial convolution and SE attention to reduce redundant computation and enhance feature representation capability. Furthermore, a CFA feature enhancement module is proposed, which incorporates coordinate attention into the multi-scale feature fusion process to improve spatial information modeling and fine-grained feature representation. Experimental results show that, compared with YOLO26n, KFP-YOLO reduces parameters and GFLOPs by 23.0 % and 23.7 %, respectively, while achieving an inference speed of 278.97 FPS. Meanwhile, mAP@0.5 reaches 94.65 %, with only a 0.44 percentage point drop. Ablation studies verify the effectiveness and synergistic optimization of each proposed module. Deployment experiments on the Jetson AGX Orin platform demonstrate strong real-time performance and edge computing adaptability, indicating that the proposed method offers an efficient solution for intelligent orchard diseases and pests monitoring.

Abstract: BACKGROUND: Hospitalizations among patients with Alzheimer’s disease (AD) carry substantial mortality risk, but the relationship between length of stay (LOS) and in-hospital death may be non-linear. We evaluated LOS–mortality patterns and compared admission-only versus inpatient-course prediction using explainable machine learning. METHODS: Using the 2017 Nationwide Readmissions Database, we identified AD hospitalizations among adults aged ≥60 years. The outcome was in-hospital mortality. LOS was analyzed in clinically interpretable bins and with restricted cubic splines. Two prespecified models were compared: Model A used admission-only variables, excluding LOS, procedure count, and total charges; Model B added these inpatient-course variables. Performance was evaluated using patient-grouped 5-fold out-of-fold validation and summarized by AUROC and AUPRC. SHAP was used for model interpretation. RESULTS: Among 11,377 AD hospitalizations, 600 in-hospital deaths occurred (5.27%). Mortality was highest for LOS 0-1 day (14.0%), lowest at 4-6 days (3.26%), and increased again with prolonged stays. Model A achieved AUROC/AUPRC of 0.729/0.149, whereas Model B improved performance to 0.794/0.283. Sepsis, acute kidney injury, stroke, older age, and higher diagnostic burden were consistently influential predictors. DISCUSSION: In AD hospitalizations, mortality clusters at LOS extremes. Admission-only models identify meaningful early risk, while inpatient-course variables add prognostic information as complications and care intensity evolve.

Abstract: Background: The persistent challenge of not having a physician or nurse practitioner for ongoing care for millions of residents imposes a burden on the health economy in Canada and demands a shift in focus from mere system registry to the value-based concept of attachment readiness. With a hypothesis that true attachment is a function of attachment readiness, a relational process shaped by the intersection of patients’ preparation, providers’ capacity, and systemic support, this study aims to investigate challenges of attachment creating barriers to meaningful healthcare relationships. Methods: This sequential mixed-methods study was designed to integrate a comprehensive scoping review of academic librarian-guided databases, search engines, grey literature, and snowballing, followed by 360-degree qualitative interviews with experts, service providers, and clients. Qualitative data was analyzed by thematic coding to answer the research questions. Results: Attachment is a function of a multitude of factors from patients and physicians, ‘attachment readiness’ is a multilevel, process-oriented condition shaped by patient preparedness, provider capacity, and system design more than a single episodic event or individual attribute. Facilitators such as team-based care, patient navigation, culturally responsive outreach, and structured onboarding reinforce readiness as a system-enabled condition. However, persistent system-level barriers delay attachment among motivated patients (readiness decay), leading to frustration, disengagement, and reduced trust. Findings further suggest that attachment is constrained not only by physician supply but also by time-sensitive readiness conditions. Conclusions: This study suggests that attachment to primary care is one leg of a three-legged stool, upheld and sustained by the two others- equity and access. To facilitate true attachment and to move beyond fragmented care and reliance on high-cost emergency departments, Canadian healthcare policy must consider attachment readiness through standardizing onboarding (Implementing uniform digital intake processes to reduce repetitive data entry and administrative friction), equity lens for prioritizing vulnerability (Refining centralized waitlist algorithms to account for clinical urgency and social determinants of health), and supporting provider capacity (Providing financial and administrative incentives that recognize the labour involved in establishing new longitudinal relationships for patients with complex needs).

Abstract: As machine learning (ML) becomes increasingly integrated into drug discovery, reliance on legacy datasets and superficial performance metrics threatens to stall genuine progress. This perspective examines common pitfalls in solubility modeling, specifically overreliance on flawed public datasets and insufficient similarity analysis between training and test sets. By comparing performance on "real-world" datasets with consistent experimental conditions, specifically the Biogen and ASAP Discovery sets, we demonstrate that inflated correlations can mask poor generalizability. We propose new guidelines for authors, reviewers, and journals to elevate the standard of ML validation.