Abstract: Convolutional neural networks have transformed visual recognition, yet robust geometric reasoning, reliable out-of-distribution generalization, and recognition from limited data remain substantially unsolved. CNNs draw their architectural inspiration from the mammalian visual cortex, but the translation from biology to engineering was selective and in places imprecise, and those imprecisions have consequences that are well documented. This paper examines where the biological fidelity holds and where it gives way, grounding the analysis in formal results that predate deep learning and in recent empirical findings on CNN failure modes. We identify three diagnosable architectural limitations. First, CNNs conflate visual modalities that the biological system separates structurally at the lateral geniculate nucleus, feeding raw RGB pixels into a single undifferentiated filter bank and entangling orientation, color, and texture signals from the first layer onward. Second, CNNs repeat a spatial subsampling operation across the full depth of the network, far beyond the early visual cortex stages where it has biological warrant. Barnard and Casasent established formally in 1990 that this operation discards positional information irreversibly at every layer where it is applied, and repeating it into regions that correspond to V4 and inferotemporal cortex compounds this loss without the compensating transition to qualitatively different computations that the biological hierarchy performs. Third, the pooling-as-complex-cell analogy that motivated this design reflects a misreading of what complex cells compute. The spatiotemporal energy model formalizes complex cell behavior as geometry extraction: detecting the presence and orientation of a local edge structure robustly, abstracting over photometric accidents of contrast polarity and sub-wavelength phase that are not geometrically meaningful. Pooling is attempting something categorically different, namely object-level position invariance for recognition through spatial subsampling, which achieves its goal by discarding exactly the geometric information that the energy model preserves. Treating pooling as an approximate or scaled-up implementation of the energy model conflates two operations that differ not in degree but in kind, and crucially it removed the principled criterion for confining the S-C operation to early visual cortex: because pooling was understood as a general-purpose invariance mechanism rather than an approximation of a first-stage geometry extractor with a natural biological endpoint at V3, the field had no architectural reason to stop repeating it. We survey how capsule networks, group-equivariant CNNs, PDE-based networks, and vision transformers each address one or two of these limitations while leaving the others intact. We propose six desiderata that a more biologically complete architecture would need to satisfy, and argue that satisfying them requires treating the visual cortex’s solution as a coherent package in which each component depends on the others working correctly, rather than as a menu of independently selectable principles.

Abstract: Amplification of the mesenchymal–epithelial transition factor protooncogene (MET), fibroblast growth factor receptor 2 (FGFR2), and epidermal growth factor receptor (EGFR) genes has been identified in 2–24%, 2–9%, and 27–64% of patients with gastric cancer (GC), respectively. This study characterised carcinogenesis-related alterations and copy number variation (CNV) in 286 genes from four human GC cell lines and analysed differences in the susceptibility of these cells to treatment with pelitinib, tepotinib, and docetaxel. Using a targeted DNA sequencing, we evaluated alterations and CNV in 286 genes from four GC cell lines. We assessed the antitumour activity of pelitinib, tepotinib, and docetaxel in these GC cell lines and in a xenograft model. Docetaxel is a drug commonly used to treat gastric cancer and was used as a positive control in this study. The effects of pelitinib, tepotinib, and docetaxel on cell viability (half-maximal inhibitory concentration), apoptotic cell death, tumour volume, and hematoxylin and eosin staining were evaluated using MTS cell proliferation assays and flow cytometry. Antitumour efficacy was assessed in xenograft mice. Compared to tepotinib, pelitinib inhibited the growth of GC cells, showing dose-dependent amplification of EGFR (CNV > 3, without HRAS, KRAS, or NRAS mutations), MET (CNV > 30), and FGFR2 (CNV = 87), with concomitant cell death induction. In the murine xenograft model, tumour volumes were significantly reduced in the pelitinib, tepotinib, and docetaxel-treated groups when administered by daily oral gavage at doses of 10, 10, and 5 mg/kg/day, respectively. Histologically, necrosis was more pronounced in the pelitinib, tepotinib, and docetaxel groups than in the control group. Pelitinib demonstrated anti-tumour activity, with MET and FGFR2 amplified in all tested GCs and EGFR amplified in GCs without HRAS, KRAS, or NRAS mutations.

Abstract: Gender role beliefs shape healthcare interactions, particularly in societies with deeply rooted traditional norms. This study examined discrepancies between culturally inherited gender role ideologies and actual behavioural patterns within a diverse cohort involved in medical training and practice. Participants, aged 20–52, represented a wide range of clinical exposure, employment status and personal life circumstances, offering a complex sociocultural spectrum rather than a uniform academic group. Using an interpretative phenomenological approach, participants provided written and oral reflections on widely known Romanian gender role axioms. Results revealed endorsement of traditional beliefs—like male authority and female domestic responsibility—yet these convictions frequently contrasted with participants’ lived behaviours, including women serving as primary earners, engaging in advanced studies and balancing multiple roles, and men in caregiving professions. This misalignment suggests unconscious bias and limited self-awareness of one’s own nonconforming practices. These discrepancies have implications for clinical care, as rigid gender role beliefs may influence communication, empathy, the ability to provide equitable healthcare to gender-diverse patients. Findings indicate the need for structured gender-diversity education, reflexivity training and cultural competence development across medical curricula, workplace settings and continuing professional education. Overall, the study demonstrates how traditional gender representations persist despite behavioural change, potentially shaping healthcare attitudes and reinforcing bias unless explicitly addressed.

Abstract: Background: Medical training is characterized by high academic demands and sustained exposure to stressors. Although the literature suggests robust links between personality and mental health, fewer studies have examined how broad personality traits relate to multidimensional psychological well-being (PWB) among medical students. Methods: In a cross-sectional paper–pencil survey conducted among 115 Polish medical students, personality was assessed with the NEO-FFI (Neuroticism, Extraversion, Openness, Agreeableness, Conscientiousness) and psychological well-being with the 84-item Ryff Psychological Well-Being Scales (autonomy, environmental mastery, personal growth, purpose in life, positive relations, self-acceptance). Descriptive statistics, Pearson/Spearman correlations, and multiple linear regression were applied (α = 0.05). Results: Mean global PWB was moderate-to-high (M = 4.22, SD = 0.57; 1–6 scale). The highest subscale means were personal growth (M = 4.48), purpose in life (M = 4.44), and positive relations (M = 4.41); the lowest were autonomy (M = 3.98), environmental mastery (M = 3.91), and self-acceptance (M = 4.09). Conscientiousness and Extraversion correlated positively with PWB dimensions, whereas Neuroticism showed consistent negative associations. In regression models, Conscientiousness (β = 0.482, p < 0.001) and Extraversion (β = 0.347, p < 0.001) jointly explained 38.7% of global PWB variance; Neuroticism alone predicted 32.4% of variance (β = −0.569, p < 0.001). No significant sex differences in PWB were observed. Conclusions: Personality traits—especially lower Neuroticism and higher Conscientiousness and Extraversion—are strongly linked with better psychological well-being among medical students. Screening and tailored, trait-informed preventive programs (e.g., emotion regulation for high Neuroticism; study planning for lower Conscientiousness; social connectedness for lower Extraversion) may support mental health in medical schools.

Abstract: Background and Objectives: Suicide represents a major global public health concern, involving complex interactions between sociodemographic and clinical factors. Understanding these characteristics at a regional level is essential for the development of targeted prevention strategies. Materials and Methods: We conducted a retrospective observational study including 210 confirmed suicide deaths recorded at a single forensic center between 2023 and 2025. Sociodemographic variables (age, sex, education, marital status, and employment) and toxicological findings (alcohol presence) were collected. Descriptive statistics and inferential analyses, including chi-square tests and multivariate logistic regression, were performed to examine associations between these variables and suicide characteristics, particularly the method of suicide. Results: The study population was predominantly male (82.86%). Hanging was the most frequent method of suicide. Alcohol was detected in 43.81% of cases. Although variations were observed across demographic groups, multivariate logistic regression did not identify statistically significant independent predictors of suicide method (male sex: OR = 1.98, p = 0.122; age: p = 0.579; alcohol presence: p = 0.728). Conclusions: Sociodemographic and toxicological factors contribute to the characterization of suicide deaths; however, no independent predictors of suicide method were identified. These findings highlight the complexity of suicide behavior and underscore the importance of integrating clinical, behavioral, and public health approaches to suicide prevention, including the responsible communication of suicide-related findings.

Abstract: DNA/cell mass homeostasis is a pervasive feature of living organisms. As the cell grows in response to nutrient availability, it must duplicate each chromosome once and only once each division cycle. Across the eukaryote Tree of Life, cells differ in their sizes in a manner that depends directly on the amount of DNA they harbor, what has been termed the “nucleotypic effect”: cell size expands or contracts as DNA content increases or decreases. In eukaryotes, any deviation from DNA/mass homeostasis results in the deregulation of the developmental program and the initiation of carcinogenesis and other genetic pathologies. In bacteria, deviation from, or perturbation of, DNA/mass homeostasis alters important physiological features such as the cell cycle timing of DNA replication initiation and the co-ordination of initiation with replication termination and cell division. In prokaryotes, the timing of initiation occurs at a relatively constant and growth rate invariant mass, termed the initiation mass (Mi), and depends strictly on DNA replication fork rates and membrane biogenesis. Complex “machines”, frequently referred to as hyperstructures or factories, mediate the phase transitions that define the different periods of the bacterial cell cycle. The following will examine how the cell size and DNA/mass homeostasis maintains a balance between replication initiation and elongation, that gate the phase transitions that organize the cell cycle in time and space.

Abstract: Background: Skin grafts including split-thickness skin grafts (SSG) and full-thickness skin grafts (FTSG) are widely used in reconstructive surgery. Infection following grafting can compromise graft take and prolong hospitalisation, yet contemporary cohort data describing incidence, microbiology and graft-specific risk factors remain limited. Methods: We conducted a retrospective observational cohort study of 977 consecutive skin graft procedures performed in 116 patients at two hospitals in New South Wales, Australia, between 1 July 2021 and 13 August 2024. Post-graft infection was defined as a clinician-diagnosed graft site infection with microbiological confirmation. Infection incidence was estimated with exact 95% confidence intervals. Associations between graft characteristics and infection were explored using chi-square testing and binomial regression to estimate relative risks. Length of stay (LOS) was assigned to the index admission corresponding to each procedure and analysed using negative binomial regression to account for overdispersion. Results: Among 977 graft procedures, 66 infections occurred, giving an overall infection incidence of 6.8% (95% CI 5.3–8.5%). Median LOS was substantially longer in infected cases than non-infected cases (34 vs 3 days, p < 0.001). Full-thickness grafts to the face (RR 0.083, 95% CI 0.008–0.827) and nose (RR 0.038, 95% CI 0.004–0.378) were associated with a reduced incidence of infection, although estimates were imprecise because of sparse data. Among infections, Staphylococcus aureus accounted for approximately 47% of cases and Pseudomonas aeruginosa for approximately 20%. In a nested antimicrobial audit cohort of 111 split-thickness skin graft procedures, peri-operative prophylaxis was common, postoperative antibiotics were frequently prescribed, and postoperative antibiotic prescribing was not associated with reduced infection, although the analysis was underpowered. Conclusions: Post-graft infection occurred in 6.8% of procedures. This rate is comparable with contemporary literature and was associated with substantial morbidity. S. aureus and P. aeruginosa predominated. These findings support consideration of targeted preventive strategies, microbiology-informed empiric therapy and antimicrobial stewardship, while highlighting the need for prospective studies with more comprehensive risk adjustment.

Abstract: Background: During menopause, many women deal with a "double whammy" of hot flashes and depression. While we know each issue drives up doctor visits and costs, we still don't have a clear picture of the total financial toll when they occur together. Using nationwide U.S. data to break down this combined economic burden is an area that's wide open for more research. Methods: We conducted a retrospective cross-sectional analysis of pooled 2017–2022 Medical Expenditure Panel Survey (MEPS) data, including 22,042 U.S. women. Participants were categorized as VMS with depression, VMS without depression, or no VMS/no depression (reference). Outcomes included total annual healthcare expenditures, outpatient, emergency, and prescription expenditures, and healthcare utilization (outpatient visits, emergency department visits, inpatient hospitalizations). Survey-weighted generalized linear models with a gamma distribution and a log link were used for expenditures. In contrast, survey-weighted linear and count models were used for utilization, adjusting for demographic, socioeconomic, insurance, regional, year, and comorbidity burden. Results: Among 22,042 women, 529 (2.4%) had VMS with depression, and 268 (1.3%) had VMS without depression. The sample included 22,042 women with no VMS or depression, 529 with VMS and depression, and 268 with VMS without depression. Women with VMS and depression had the highest annual healthcare expenditures compared with women without VMS or depression. In multivariable analyses, women with VMS and depression had healthcare costs approximately 1.90 times higher than the reference group (β=0.64, SE=0.08, p< 0.0001; $11,404 vs. $6,002). Those with VMS without depression faced costs approximately 55% higher (β = 0.44, SE = 0.10, p < 0.0001; $9,303 vs. $6,002). Outpatient costs were also significantly higher among women with VMS and depression (β=0.31, SE=0.05, p< 0.0001), corresponding to approximately 36% higher outpatient costs over those with no VMS/no depression, respectively; $2,354 vs. $1,730). Women with both vasomotor symptoms (VMS) and depression had prescription drug spending that was about 2.61 times higher than those without VMS or depression (β = 0.96, SE = 0.13, p < 0.0001; $3.452 vs $1,323). Emergency department costs did not differ significantly between groups. Older age, unemployment, and higher comorbidity burden were also associated with increased healthcare costs. In utilization models, women with VMS and depression had 4.80 additional outpatient visits per year (SE = 0.50, p < 0.0001) and higher emergency department visit rates (β = 0.26, SE = 0.11, p < 0.05), corresponding to approximately 28% higher ER use, whereas inpatient hospitalizations were not significantly different. Women with VMS without depression also had more outpatient visits (β = 3.77, SE = 0.66, p < 0.0001) but no significant differences in emergency or inpatient utilization. Conclusions: VMS and depression are strong independent predictors of increased healthcare expenditures and outpatient utilization among midlife women in the United States. The excess economic burden is driven primarily by outpatient care and prescription use rather than hospitalization, suggesting that integrated menopause and mental health care may improve efficiency and reduce healthcare costs.

Abstract: The establishment of a reference population for genomic selection in Korean beef cattle is an ongoing process. There is a high likelihood of sex-specific differences in the composition of the reference and test populations. This study evaluates the accuracy of Genomic Estimated Breeding Values (GEBVs) for carcass traits in Hanwoo cattle, specifically investigating the efficacy of cow-based reference populations. The effectiveness of genomic selection (GS) is heavily dependent on the composition and size of the reference population. Utilizing genotype data from a Hanwoo 50k SNP chip and phenotypic data from 19,168 steers and 6,233 cows, the study estimated GEBV accuracies for carcass weight (CWT), eye muscle area (EMA), backfat thickness (BF), and marbling score (MS) using the GBLUP method. Results demonstrate that steer-based reference populations achieved the highest accuracy (0.64–0.88), averaging 0.78, likely due to standardized management and higher trait heritability (0.39–0.51) compared to cows. In contrast, cow-based reference populations exhibited prediction accuracies (0.55–0.75) in four traits using adjusted residual phenotype, averaging 0.64, but remained highly practical alternatives. While growth traits (CWT and EMA) showed significant bias in cross-sex predictions, fat deposition traits (BF and MS) remained stable across sexes. The study concludes that although steer-based populations provide optimal accuracy, incorporating cows into the reference population is strategically vital in Hanwoo.

Abstract:

The degradation of the health state of Proton Exchange Membrane (PEM) water electrolyzer, caused by power supply variability, operating temperature changes, and other chemical factors, represents a major challenge for green hydrogen production efficiency. This paper presents an advanced hybrid system combining a digital twin and machine learning, enabling real-time anomaly detection of a PEM electrolyzer. This intelligent approach allows for the real-time prediction of operating parameters, namely current, voltage, and hydrogen flow rate, via Azure Machine Learning, and their visualization within the system's digital twin via Azure Digital Twins. Furthermore, the comparison between simulated data from the digital twin and those predicted by machine learning enables the anticipation of PEM electrolyzer anomalies. The selected prediction models rely on the Extreme Random Trees algorithm for current and voltage estimation, and on the Elastic Net algorithm for hydrogen flow rate prediction. The obtained results confirm the robustness of the proposed approach, with coefficients of determination of 0.99820, 0.99693, and 0.99665 for current, voltage, and hydrogen flow rate respectively, associated with Normalized Root Mean Square Errors (NRMSE) of 0.00870, 0.011278, and 0.11087. This high accuracy provides the digital twin with the capability to anticipate failures and extend the PEM electrolyzer's lifespan, with a view to optimizing the global efficiency of green hydrogen production.

Abstract:

We present a mathematically rigorous formulation of the Fundamental Speed Theory (FST), a dimensionally consistent vector–tensor theory featuring a dimensionless vector field \( \nu^{\mu} \). We introduce characteristic scales \( L_0 = 10 \) kpc and \( M_0 = \hbar/(cL_0) \) and keep \( \hbar \) and \( c \) explicit throughout. In dimensionless form, the galactic field obeys

\( \frac{d^2\tilde{\nu}}{d\xi^2} + \frac{2}{\xi}\frac{d\tilde{\nu}}{d\xi} = \beta_{\mathrm{eff}}\tilde{\nu}^3 \),\( \qquad \beta_{\mathrm{eff}} \equiv -\frac{\lambda\nu_0^2}{6c_1} = 2.0\times 10^7 \ (\lambda<0). \)

We validate the theory on the SPARC sample using three primary hierarchical levels (Levels 1–3): Level 3 (zero free parameters) fits 65.7% of galaxies with mean \( \chi_{\nu}^{2}=0.809 \); Level 2 (estimated \( M, r_d \), no fitting) reaches 93.6% with mean \( \chi_{\nu}^{2}=0.347 \) for the 160 galaxies with \( \chi_{\nu}^2<3 \); and Level 1 (fitted \( M, r_d \)) fits all 171 galaxies with mean \( \chi_{\nu}^{2}=0.170 \) (91.2% with \( \chi_{\nu}^{2}<0.5 \)). We further report two derived formulations: Level 4 (coefficient-free) and Level 5 (unified), the latter showing that the field parameters unify into a single acceleration scale

\( A_0 = \frac{(c_1+c_3)\nu_0^2 c^2}{L_0} = 2.42\times 10^{-10}\ \mathrm{m/s^2}, \)

which reproduces the full formulation identically for all galaxies. A full three-dimensional numerical experiment with disk-like (anisotropic) boundary forcing confirms that the converged 3D field profiles and rotation-curve fits remain essentially unchanged relative to the 1D quasi-spherical approximation for the tested cases. We also perform an explicit sign-convention robustness check: running the full pipeline with the alternative (negative-sign) convention yields identical fits within numerical tolerance when implemented consistently. Solar System constraints are satisfied because the relevant acceleration arises from the galactic field gradient, giving a local FST acceleration at Earth of \( \sim 8\times 10^{-15} \) of the Newtonian value. All code is archived on Zenodo, and supplementary materials (including complete fit results and both sign-convention implementations) are provided. Extension of FST to cosmological scales is left as future work.

Abstract: This study aimed to characterize metabolomic changes in the eye lens of senescence-accelerated OXYS rats in comparison with control Wistar rats, and to identify biochemical shifts associated with genotype, age, and cataract progression. Cataract severity was clinically graded. Rats' lenses were analyzed using quantitative ¹H NMR spectroscopy at 3.6 and approximately 4.5 months of age. A total of 43 metabolites were quantified. We found that at 3.6 months of age, OXYS lenses exhibited a significant accumulation of 17 metabolites, primarily amino acids, compared to Wistar rats, suggesting an imbalance between amino acid uptake and crystallin biosynthesis. However, by 4.5 months, OXYS lenses exhibited rapid metabolic changes characterized by significant decreases in amino acid, glucose, and key energy/antioxidant markers, including NAD, adenylate energy charge, and hypotaurine. Clinical cataract grade (Grade 2 vs. 3) had a negligible impact on the overall metabolomic profile. Our results indicate that profound metabolic reorganization, including an initial amino acid excess followed by energy and antioxidant depletion, precedes the morphological manifestation of cataracts in OXYS rats. We suggest that a biochemical "point of no return" occurs early in cataractogenesis, while subsequent increase in lens opacification is a secondary consequence of preexisting metabolic disturbances.

Abstract: The field of international relations confronts significant research gaps as established theoretical frameworks struggle to address the multidimensional challenges of the twenty-first century. This study presents a comprehensive qualitative analysis of four priority research domains requiring urgent scholarly attention: artificial intelligence (AI) governance and global power dynamics, climate security and interstate conflict, digital sovereignty in the Global South, and non-state actors in hybrid warfare. Employing systematic literature review and thematic analysis grounded in interpretivist epistemology and critical realist ontology, this research identifies critical theoretical and empirical deficiencies in existing scholarship while proposing integrated frameworks for addressing them. Findings reveal that traditional international relations theories—including realism (Waltz, 1979/2010; Mearsheimer, 2001), liberalism (Keohane, 1984; Ikenberry, 2011), and constructivism (Wendt, 1999; Finnemore & Sikkink, 1998)—require substantial adaptation to address challenges posed by technological transformation, environmental change, and evolving security paradigms. The analysis identifies significant cross-domain interconnections: AI governance intersects with hybrid warfare through autonomous weapons systems and cyber operations, while climate security connects with digital sovereignty through environmental data governance. The paper concludes with evidence-based recommendations for future research agendas, emphasizing interdisciplinary collaboration, methodological innovation, and policy-relevant scholarship. This analysis contributes to advancing international relations scholarships in an era of unprecedented global complexity.

Abstract:

Leukocyte recruitment from blood into tissues involves sequential adhesive steps, including rolling and integrin-dependent arrest. The integrin VLA-4 is known to mediate firm adhesion, but can also support rolling. CD44–hyaluronan interactions have also been implicated in leukocyte rolling. Here, we used parallel-plate flow chamber assays to compare the contributions of CD44 and VLA-4 to monocyte rolling on different cellular monolayers. Monocytoid WEHI 78/24 cells rolled and adhered through CD44 on hyaluronan-presenting ECV304 monolayers, whereas VLA-4 dominated adhesion on endothelial monolayers expressing functional VCAM-1. Primary human monocytes showed similar CD44-dependent rolling on ECV304 monolayers. Blocking CD44, adding soluble hyaluronan, or removing surface hyaluronan with hyaluronidase reduced rolling and adhesion. These results show that CD44 can support monocyte rolling when VLA-4/VCAM-1 adhesion is not the dominant interaction. This cell-based flow model distinguishes CD44/hyaluronan-mediated rolling from VLA-4/VCAM-1-rolling and may help analyze monocyte rolling on hyaluronan, including tumor-derived monolayers.

Abstract: Stroke, a leading cause of global disability and mortality, exhibits significant spatiotemporal associations with environmental pollutants. Predicting daily stroke admissions becomes increasingly important as the population ages. Current prediction research on stroke-related medical services mainly relies on point prediction, which lacks the ability to quantify uncertainty. In this study, we try to develop parametric probability prediction models of stroke admissions based on machine learning and deep learning algorithms. We collected stroke data and environmental data from February 11, 2019 to May 26, 2023 in Chengdu, and employed prediction models encompass negative binomial regression, natural gradient boosting (NGBoost), long short-term memory networks (LSTM), and transformer. For performance assessment, mean absolute error (MAE) is used to evaluate point prediction accuracy, while continuous ranked probability score (CRPS) is applied to assess the quality of distribution fitting.We find that models with the ability to capture and process time-series information demonstrate greater advantages in probabilistic prediction, and among the four evaluated models, the transformer model proves to be the one that delivering more reliable and precise outcomes in both point prediction of admission counts and distribution fitting performance. This probabilistic forecasting approach provides robust evidence-based decision support for healthcare administrators to optimize resource allocation and staffing arrangements, and ultimately helps elevate the quality of medical care for stroke patients.

Abstract: Point-of-care testing (POCT) for cardiac troponin is increasingly used to support rapid clinical decision-making, particularly in resource-limited settings. However, while many central laboratories, including those in Sri Lanka, now use high-sensitivity cardiac troponin I (hs-cTnI) assays, commonly available POCT platforms continue to use conventional methodologies with different analytical characteristics and decision thresholds. We evaluated the analytical agreement and clinical concordance of two POCT troponin assays against a central laboratory hs-cTnI assay using paired samples obtained during routine clinical care in a Sri Lankan hospital. Both POCT devices demonstrated strong correlation with the laboratory assay (Pearson r ≈ 0.90). However, Bland–Altman analysis showed substantial positive bias and wide limits of agreement, indicating poor interchangeability at the individual sample level, with proportional bias observed in one device. Clinically relevant discordance was also identified, with 26.9% and 30.4% of samples classified as negative by POCT despite being positive by the reference assay. Regression-based recalibration did not significantly improve concordance. These findings highlight that strong correlation does not ensure diagnostic agreement, emphasizing the need for local validation before integrating POCT troponin assays into established hs-cTnI diagnostic pathways.

Abstract: Rare diseases, though individually uncommon, collectively represent a major global health challenge, affecting millions worldwide and increasingly recognized in India as a significant contributor to pediatric and adult morbidity. Cystic fibrosis (CF), a multisystem autosomal recessive disorder caused by pathogenic variants in the Cystic Fibrosis Transmembrane Conductance Regulator (\textit{CFTR}) gene, exemplifies this burden, with delayed diagnosis and diverse mutational spectra complicating clinical management in South Asian populations. To advance rare disease genomics, quantitative analysis of CFTR sequences across multiple species is essential, as evolutionary conservation highlights residues and motifs critical for channel function, while divergence reveals lineage-specific adaptations relevant to disease mechanisms. In the present study, we performed integrative analyses encompassing amino acid composition, sequence homology, frequency-dominant residue patterns, hydropathy-based n‑gram distributions, hydropathy profile continuity, and intrinsic disorder architectures across various CFTR sequences from multiple species. The quantitative signatures derived from amino acid composition, sequence homology, hydropathy-based n‑grams, hydropathy profiles, and intrinsic disorder analyses carry significant translational impact, as they provide a unified framework for identifying conserved motifs, resolving disorder-prone domains, and guiding the precise mapping of pathogenic mutations and their functional consequences. Collectively, our findings demonstrate how cross-species quantitative protein analysis of CFTR bridges evolutionary biology with clinical investigation, providing translational insights that strengthen rare disease research and therapeutic development in cystic fibrosis.

Abstract: Accurate fault location is essential for rapid service restoration in distribution networks. How-ever, modern active distribution networks (ADNs) with high penetration of distributed energy resources (DERs) challenge conventional methods through multi-source fault contributions, bi-directional power flows, and converter-limited fault currents. This paper presents a time-domain fault-location method for both passive distribution networks (PDNs) and ADNs, based on a three-sample apparent-inductance estimator that uses local voltage and current measurements. The estimator exploits the strong correlation between line inductance and fault distance, with reduced sensitivity to fault resistance compared to classical impedance approaches. Its performance is evaluated on a 22 kV, 20 km distribution feeder, covering three fault types, four fault resistance levels (5–500 Ω), four fault locations, EN 50160 standard-compliant harmonic distortion, and DER penetration levels from 0 to 80%. Under ideal sinusoidal conditions, relative location errors remain below 2% for low-resistance faults. In ADNs, the method achieves errors be-low 5% for low-resistance faults across all fault types, with accuracy decreasing for high-resistance faults at high DER penetration. A sensitivity analysis confirms practical robustness across SNR, load current, THD, and DER penetration.

Abstract: Most AI-for-science systems (agents4science) are evaluated as task-specific automation rather than persistent work environments. This leaves an important and pragmatic systems-question unresolved: what infrastructure enables scientific agents to be trustworthy, steerable, and reproducible? We present ApexClaw, a persistent workspace for human-supervised AI agents in scientific discovery. The system provisions isolated Linux environments with scientific computing libraries, a browser-based IDE, and a registry of reusable scientific skills. Rather than pursuing full autonomy, ApexClaw emphasizes human oversight through an interface that exposes agent reasoning, tool calls, and workspace files, allowing scientists to guide agents at key junctures. We validate this approach with telemetry from 81 users operating 243 workspaces across 252 conversations (March--May 2026): 87\% of users reconnected to existing workspaces, and humans intervened selectively on 3.4\% of interactions. These findings demonstrate that hybrid autonomy with persistent context is ordinary practice in real agentic science, and that durable workspaces, explicit artifacts, and auditable traces are necessary infrastructure for reproducible and steerable AI scientists.

Abstract: Background: Neuroinflammation contributes to etiopathology and symptom severity in neurodegenerative and neuropsychiatric disorders. Glial cells, especially microglia and astrocytes, play a crucial role in neuroinflammation. It has been reported that ginseng and its bioactive component ginsenoside Rg1 exhibit anti-inflammatory effects and improve cognitive performance in various models. However, the exact underlying mechanisms remain unclear. Methods: An astrocyte-microglia co-culture model simulating physiological (M5, 5-10% microglia) and pathological/inflammatory (M30, 30-40% microglia) conditions was treated with different concentrations of ginsenoside Rg1 (15, 30, 45 µM), ginseng extract (derived from Korean red ginseng) at low-dose (12.5, 25, 37.5 µg/ml) or high-dose (125, 250, 375 µg/ml) for 24 hours. Cell viability was assessed by MTT assay. Microglial reactivity was examined by immunocytochemistry. Astrocytic gap-junctional coupling was investigated using scrape loading method and connexin 43 (Cx43) expression was analyzed by immunocytochemistry and Western blot. Results: Both Rg1 and low-dose ginseng extract reduced microglial activation under inflammatory conditions by promoting a phenotypic shift from activated to homeostatic (resting) microglia. Rg1 preserved astrocytic gap-junctional function by preventing the inflammation-induced downregulation of Cx43 expression and enhancing Cx43-mediated gap-junctional intercellular communication. Rg1 caused a significant reduction of glial cell viability only at high concentrations (30 and 45 µM) under inflammatory conditions. High-dose ginseng extract showed significant concentration-dependent cytotoxicity, reducing glial cell viability under physiological and pathological conditions, without comparable anti-inflammatory benefits. Conclusions: This study suggests that low-dose ginseng and its active compound Rg1 exert anti-inflammatory effects by modulation of astrocytic coupling and microglial reactivity. These results provide a novel therapeutic perspective for ginseng in the treatment of neurodegenerative and neuropsychiatric diseases related to neuroinflammation.