Abstract: Quantitative structure–activity relationship (QSAR) modeling has traditionally relied on expert-designed molecular descriptors to encode chemical structures. DeepSnap is a descriptor-free QSAR approach that converts three-dimensional molecular structures into image representations and feeds them directly into convolutional neural networks for activity prediction. The method generates a conformer for each molecule, renders it as a color-coded molecular image, and captures omnidirectional snapshots from systematically varied viewing angles. This review traces DeepSnap from its introduction in 2018 to its current state. The method has been applied to 35 nuclear receptor endpoints from the Tox21 10K library (mean AUC 0.884), 59 molecular initiating event models spanning the full Tox21 target panel, rat hepatic clearance (ensemble AUC 0.943), and blood–brain barrier penetration (ensemble AUC 0.936). An ensemble strategy combining image-based and descriptor-based predictions has consistently outperformed either approach alone. The computational pipeline has evolved from a DIGITS/Caffe/Jmol system to a TensorFlow/Keras/PyMOL framework. Limitations include endpoint-dependent parameter sensitivity, class imbalance effects, the absence of direct comparisons with graph neural networks, and an interpretability gap addressed in part by CAM-family visualization in the AI-SHIPS platform and S-COPHY. Future directions include systematic application of explainable AI methods, automated hyperparameter optimization, and integration with graph-based approaches.

Abstract: Aim. This study aimed to evaluate the impact of cone-beam computed tomography (CBCT) on preoperative surgical decision-making and risk assessment for mandibular third molar (MM3) extractions in cases identified as high-risk by orthopantomograph (OPG). Materials and Methods. This prospective clinical study utilized the purposive sampling method, recruting50 MM3s from 33 patients (mean age 24.24 ± 6.77 years). Samples were categorized into five distinct radiographic groups based on the proximity of roots to the inferior alveolar canal (IAC) on OPG. The methodology involved a comparative 3D analysis to determine neurovascular contact, spatial orientation, and the presence of cortical border. Surgical strategies, specifically the necessity for coronectomy or the lingual split technique, were reassessed following 3D evaluation. Postoperative neurosensory outcomes were recorded. Statistical analysis was performed using the Fisher-Freeman-Halton and Kruskal-Wallis tests. Results. CBCT identified direct IAC contact in 74% of the cases. In 18% of the cases initially deemed high-risk by OPG, CBCT revealed a safe distance, thereby altering the surgical approach. Tooth angulation (P = 0.012) and Pell and Gregory classification (P = 0.0024) were significant predictors of contact. Temporary neurosensory disturbances occured in 4% (n = 2) of the sample, specifically in cases where CBCT had confirmed loss of canal cortication. Conclusion. In accordance with the study aim, CBCT provides essential 3D data that refines surgical planning in nearly one-fifth of high-risk cases. The findings justify selective CBCT use, guided by the ALADA principle, to minimize iatrogenic injury.

Abstract: Major depressive disorder remains a leading cause of disability, and decades of monoam-ine-centered pharmacology have yielded delayed and often incomplete relief. Rapid-acting antidepressants reshaped the field by linking swift symptom improvement to glu-tamatergic plasticity, yet durable benefit depends on how newly reconfigured circuits are stabilized and tuned. This review synthesizes evidence that antidepressant efficacy arises from the coordinated engagement of synaptic plasticity, spanning induction and consoli-dation, and intrinsic excitability, which provides gain control, and proposes an integrated framework to guide future discovery. It first outlines induction through N-methyl-D-aspartate receptors (NMDARs) and α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs), exemplified by ketamine and esketamine, followed by consoli-dation mediated by tropomyosin receptor kinase B (TrkB) signaling, translational disinhi-bition via eukaryotic elongation factor 2 kinase (eEF2K), and presynaptic stabilization in-dexed by synaptic vesicle glycoprotein 2A (SV2A); together, these processes transform transient potentiation into persistent network change. It then highlights intrinsic excitabil-ity, emphasizing voltage-gated potassium channel subfamily Q (Kv7), hyperpolarization-activated cyclic nucleotide-gated (HCN), and G protein-gated inwardly rectifying potassi-um (GIRK) channels as circuit-level governors that normalize firing and limit relapse-prone hyperexcitability. Finally, it presents a phase-aware Induction–Consolidation–Maintenance (ICM) roadmap, supported by SV2A positron emission tomography (PET) and electroencephalography (EEG)/functional magnetic resonance imaging (fMRI) bi-omarkers, to personalize treatment timing and combinations. This dual-target strategy re-frames antidepressants as network reprogrammers and suggests broader relevance for circuit repair across neurology and psychiatry.

Abstract: Interdisciplinary collaboration is crucial for addressing complex, real-world challenges, particularly in high-stakes fields such as nuclear research. The UK’s Zero-Power Reactor Project exemplifies this approach, aiming to develop innovative reactor technology that supports the nation’s net-zero carbon objectives. The project brought together a culturally diverse, interdisciplinary team of researchers, whose collaboration was central to its progress. Reflecting on the team’s journey, we sought to understand the dynamics that shaped their experience. This led us to ask: What were the experiences of the Zero-Power team, and what factors enabled or hindered their collaboration? We employed a qualitative methodology combining constructivist grounded theory with collaborative autoethnography, enabling a deep exploration of the lived experiences of team members. Reflections were gathered at the project’s conclusion to examine how the team functioned, learned, and evolved. Through iterative cycles of inductive, deductive, and abductive reasoning as well as using constant comparative analysis, we identified key patterns in how individuals build trust, overcome disciplinary and cultural differences, and co-create a productive team environment. Three overarching themes emerged: psychological safety, including belonging and value, learning and self-reflection, team spirit and motivation, and Innovation and discovery. Additionally, strategies for team building and addressing challenges and uncertainty were discussed. This study contributes to empirical evidence of existing knowledge and suggests actionable insights for cultivating high-performing teams in complex scientific environments and real-world challenges by demonstrating a role model of ‘interscience’.

Abstract: This study examines how family background shapes individual occupational status within a behavioral science framework, using pooled data from the 2018 and 2020 waves of the China Family Panel Studies (CFPS). Grounded in New Human Capital Theory, it further investigates the moderating roles of cognitive and non-cognitive abilities in this relationship. The results indicate that family background exerts a significant and persistent positive effect on both initial and current occupational status, suggesting the enduring influence of intergenerational advantage. Robustness checks using alternative indicators, including father’s occupational status and mother’s education, confirm the stability of the findings. In addition, digital skills, appearance investment, and selected Big Five personality traits—agreeableness, openness, and conscientiousness—significantly strengthen the positive association between family background and occupational outcomes. These findings suggest that, beyond structural advantages, individual behavioral and psychological characteristics play a critical role in enabling individuals to effectively transform family resources into labor market success. Overall, the study provides empirical evidence on how behavioral factors interact with family background to shape occupational inequality in contemporary China.

Abstract: We present a new whole-rock geochemical dataset for intrusive rocks of the late Variscan Serre batholith (Calabria, southern Italy), a well-exposed section of tilted con-tinental crust emplaced between ~305 and 292 Ma. The dataset includes major, trace and rare earth element (REE) analyses for 74 samples collected from the main plutonic units, ranging from tonalites and quartz-diorites at deeper structural levels to peraluminous granites at shallower levels, as well as leucosomes from associated migmatitic metase-diments. Analytical data were obtained using X-ray fluorescence (XRF) and inductively coupled plasma mass spectrometry (ICP-MS). The dataset integrates new and previously published geochemical data into a con-sistent and reusable format, including sample locations (WGS84), lithological classification and stratigraphic attribution. Sampling sites are also provided as a downloadable geo-spatial (.kmz) file for visualization in GIS platforms. The dataset is made available as a supplementary Excel file. These data are intended to support a wide range of applications, including petro-genetic studies of granitoid magmatism and investigations of water–rock interaction processes in crystalline aquifers. The dataset represents a valuable resource for both fundamental and applied geoscientific research.

Abstract: In order to enhance the amphibious mobility of robots in water-land environments, this paper proposes a frog-inspired hybrid drive amphibious robot, based on the amphibious locomotion characteristics of frogs. Distinct from existing single-mode frog-inspired jumping or swimming robots, the proposed robot innovatively integrates hybrid propulsion to simultaneously achieve both frog-like swimming and jumping capabilities. On land, the robot utilizes an explosion-driven hind limb actuation mechanism, paired with a linkage-based forelimb posture control system, to achieve high-performance frog-like jumping. In water, a rope-driven hind limb mechanism facilitates extension and retraction movements, while controllable soft-actuated flippers enable swinging and opening/closing motions, thereby achieving efficient frog-like swimming. In addition, an amphibious dynamic model was developed, and the robot's amphibious locomotion capabilities were evaluated and analyzed. Finally, an experimental prototype platform was built to test the amphibious locomotion performance of the designed robot, and a comparative analysis was conducted with the theoretical model. The experimental results not only validated the correctness of the amphibious dynamics and motion theory, but also confirmed the effectiveness of the designed amphibious terrain-crossing mechanism.

Abstract: This systematic review of 25 original studies examined the efficacy of density gradient centrifugation (DGC) combined with magnetic-activated cell sorting using Annexin V (MACS) for improving sperm quality in infertile men undergoing ICSI. The evidence consistently demonstrates that DGC-MACS significantly reduces sperm DNA fragmentation, with reported reductions ranging from 2.82% to 21.9% in absolute terms, and relative reductions of 39%-83%. The combination of DGC followed MACS achieved superior outcomes compared to either technique alone, reducing DNA fragmentation index (DFI) 4.1 to ± 1.3% compared with 8.1 ± 4.1% for DGC alone and 7.4 ± 3.9% for MACS alone. The treatment improved sperm motility, membrane integrity and overall spermatozoa health by reducing protamine deficiency and chromosomal abnormalities. Clinical results in ICSI cycles showed that although fertilization rates were similar between the treated and control groups, DGC followed by subsequent MACS treatment significantly improved embryo quality (72.5% vs. 51.47% top-quality day-3 embryos), blastocyst formation rate (69.69% vs. 48%), pregnancy rates (60.7% vs. 51.5%, p=0.014), and live birth rates (47.4% vs. 31.2%, p=0.001) with a reduced miscarriage rate (14.7% vs. 20.6%, p=0.034). The technique proved most beneficial in patients with high baseline DNA fragmentation (≥30%) and in those with asthenozoospermia or asthenoteratozoospermia. Studies suggest, and consistently supported by our study results, that performing DGC before MACS (DGC-MACS) may yield superior results compared to the reverse sequence. This is because DGC leads to a primary separation of sperm based on density, motility, and morphology and thus producing a high-density fractions enriched in morphologically normal sperms. MACS by specifically binding to phosphatidylserine (PS) residues on sperm membranes selectively removes PS‑positive/apoptotic sperm in a subsequent purification step. Overall, the literature strongly supports the use of combined DGC-MACS as an effective sperm preparation technique for ICSI in infertile men with elevated sperm DNA fragmentation.

Abstract: Cancer cells, like yeast, use fermentation despite the presence of oxygen, a phenomenon called aerobic glycolysis. The advantage is that it maintains most of the C-C bonds of glucose, allowing highly proliferating cells to produce the biomolecules that are necessary for cytokinesis. However, aerobic glycolysis is less energy-efficient than respiration, and it must operate at a higher frequency and produces more toxic by-products like methylglyoxal, which damages DNA. Cancer cells, like yeast cells, developed efficient systems to repair their damaged DNA. This makes cancer cells resistant to radiotherapy, which requires a combination with chemotherapy using drugs that inhibit DNA repair. However, this converts healthy cells to cancer cells, indicating that more research is required regarding the relationship between glycolysis and cancer. Using yeast as a model, we have discovered that the glycolytic enzymes TPI1 and GAPDH interact with the DNA damage-dependent checkpoint Rad9p. Furthermore, we have isolated TPI1 and GAPDH mutant strains that are unable to repair their damaged DNA. The TPI1 mutant strain has lower TPI enzymatic activity, suggesting that it accumulates methylglyoxal, while the GAPDH mutant strains have normal GAPDH enzymatic activity, confirming that GAPDH moonlights in the DNA Damage Response.

Abstract: Early-onset colorectal cancer (EOCRC), defined as colorectal cancer diagnosed before the age of 50 years, has become an emerging global health concern due to its steadily increasing incidence. Compared with late-onset colorectal cancer, EOCRC often presents with more advanced disease and exhibits distinct clinical and biological characteristics. Notably, younger patients are more likely to present with synchronous metastases and multi-organ dissemination at the time of diagnosis. This mini-review summarises current evidence regarding metastatic patterns and the biological mechanisms underlying multi-organ metastasis in EOCRC. The liver remains the most common metastatic site in EOCRC, primarily due to portal venous drainage from the colorectal region. However, studies suggest that EOCRC patients have a higher likelihood of additional metastatic involvement of the lungs, peritoneum, and distant lymph nodes, with occasional spread to the bone and brain. Several biological mechanisms may contribute to this aggressive metastatic behaviour. Distinct molecular alterations, including KRAS and BRAF mutations and microsatellite instability, have been reported in EOCRC and may influence tumour progression and metastatic potential. In addition, tumour microenvironmental changes such as epithelial–mesenchymal transition and angiogenesis play critical roles in facilitating tumour invasion, intravasation, and colonisation of distant organs. Hereditary predisposition, lifestyle-related risk factors, and gut microbiome alterations have also been implicated in EOCRC pathogenesis. A clearer understanding of the metastatic cascade and its molecular drivers in EOCRC is essential for improving early detection and developing targeted therapeutic strategies. Future research integrating molecular profiling and clinical outcomes may help optimize personalised treatment approaches and improve prognosis in this increasingly recognised patient population.

Abstract: Cancers are thought to result from the interaction of a small set of mutations in genes that regulate cell growth. A sixty-five year series of incidence data is available for cancer of the pancreas, spanning the period over which incidence increased threefold. These data were analysed using a multi-hit mutation model. The analysis yielded as parameters the averages of the number of interacting genes (with the number four being within the range found), the rate of their mutation, the age at which mutations began to accumulate and the number of persons at risk. Only this last parameter showed a significant increase over the years, a period which coincided with increased consumption of sugars. The effect of glucose on the proliferation of cells of the pancreas is suggested as a basis for these findings.

Abstract: To meet the requirements of high accuracy in image edge localization and strong noise resistance for computer vision calibration and precise measurement, an improved Zernike moment subpixel high-precision measurement method for circular hole-like workpieces is proposed. Firstly, the Canny operator is used as a coarse edge detection algorithm, with the traditional Gaussian filter in the Canny operator replaced by an improved Laplacian edge-adaptive median filter. This approach demonstrates improved edge preservation compared to traditional and adaptive median filtering, especially under high-concentration noise. Then, a subpixel edge detection algorithm is applied to refine the edges, thus enhancing the edge localization accuracy. An improved Zernike moment subpixel detection algorithm is employed for precise edge point detection. The improved algorithm selects a Zernike moment parameter template with higher detection accuracy. Finally, the inner and outer diameters of the circular hole-like part are measured by fitting the profile using the least squares method. Experimental results on several different workpieces demonstrate that the proposed algorithm achieves higher accuracy than the traditional Zernike moment subpixel method, with an error reduction of 75.1%, meeting the precision requirements in modern industrial part manufacturing processes.

Abstract: This scoping review examined how external clinical notes are obtained and used before scheduled primary care visits in high-income countries, with attention to continuity of care, workflow integration, value, healthcare delivery, policy, and risk. Within the health data ecosystem, the value of external clinical data depends not only on exchange capability but also on whether information is timely, easy to find, and usable in practice. Guided by Arksey and O’Malley, Joanna Briggs Institute guidance, and PRISMA-ScR, the review searched PubMed, CINAHL, and Google Scholar for English-language, peer-reviewed studies published from 2021 to 2026. Of 330 records identified, 15 studies were included and assigned Johns Hopkins evidence levels and quality ratings. The evidence base was dominated by Level III studies, indicating stronger support for conclusions about workflow barriers, usability, and care coordination than for causal or economic effects. Three patterns emerged: technical exchange alone did not ensure continuity of care, workflow integration shaped whether external information was useful, and the literature described clinical and operational value more clearly than direct financial return. Using the Sittig and Singh sociotechnical model, the review shows that value is produced or lost across infrastructure, clinical content, interface design, people, workflow, organizational conditions, external rules, and monitoring. Overall, external clinical notes function as high-value data only when they are available before the visit, routed appropriately, and usable within the routine primary care workflow. Future research should use stronger workflow-specific measures and assess cost implications and return on investment more consistently.

Abstract: The growing complexity of enterprise resource planning (ERP) systems necessitates intel-ligent approaches for dynamically identifying and evaluating key performance indicators (KPIs) that accurately reflect organizational performance. This paper proposes a mul-ti-agent architecture for dynamic KPI management over Oracle E-Business Suite (EBS). The core design combines a dynamic multi-agent analytics layer, an extendable dedicated EBS KPI Model Context Protocol (MCP) server layer, and a data layer. The dynamic multi-agent analytics layer defines a set of independent large language model (LLM) agents, each re-sponsible for a specific task determined by the business requirements of a particular com-pany. The EBS KPI MCP server layer defines the tools required to access and transform Oracle EBS data and exposes them to the AI agents in the upper layer. Above these layers is the user layer, where the user actively participates in the process through a hu-man-in-the-loop approach. Based on this general architecture, we proposed and imple-mented, as a proof of concept (PoC), a multi-agent system for dynamic business KPI selec-tion, evaluation, and quantification, in which three distinct agents for KPI selection, KPI quantification, and KPI forecasting were instantiated within the multi-agent analytics lay-er. This demonstrates the practical applicability of the proposed general architecture. The study contributes to intelligent business analytics by showing how coordinated LLM agents can automate KPI lifecycle activities within ERP ecosystems, enabling adaptive, data-driven performance management aligned with evolving organizational needs.

Abstract: Modern infocommunication, sensing, and cyber-physical systems increasingly rely on heterogeneous data streams originating from channels of different physical nature, sampling rates, reliability levels, and uncertainty characteristics. Direct fusion of such data in conventional artificial intelligence pipelines often yields decision outputs that are difficult to interpret, calibrate, and trust, especially in safety-related or security-related applications. This work proposes an event-probabilistic approach to the unification of heterogeneous sensor data for decision-support systems. The main idea is to transform heterogeneous sensor observations into a common space of event-oriented probability estimates, which can then be integrated using reliability-aware weighting. In this form, the system can generate not only a final recommendation, but also supporting metrics, including event likelihood, risk level, uncertainty, data quality, and inter-channel conflict. The paper formulates the conceptual and architectural basis of the proposed framework and discusses its compatibility with further Bernoulli encoding and stochastic processing. An illustrative numerical experiment involving four sensor channels and three representative scenarios is used to demonstrate the behavior of the framework. The results show that adaptive reliability-aware weighting improves the stability of the integrated event probability under channel degradation, while explicit conflict assessment prevents unjustified automatic decisions under contradictory sensor evidence. The proposed framework may serve as a basis for future stochastic and photonic-stochastic decision-support systems in access control, industrial monitoring, transport infrastructure, and critical-infrastructure applications.

Abstract: Background/Objectives: Acute pancreatitis (AP) is a heterogeneous disease with outcomes ranging from mild to severe. Early risk stratification is essential, but commonly used scoring systems are often complex for routine use. This study aimed to evaluate the predictive value of systemic inflammatory indices derived from complete blood count parameters in assessing disease severity in AP. Methods: This retrospective study included 454 patients with AP. Demographic, clinical, and laboratory data were obtained from electronic medical records. Systemic inflammatory indices (NLR, PLR, MLR, dNLR, SII, SIRI, and AISI) were calculated from admission laboratory values, with logarithmic transformation applied to selected indices. Disease severity was classified according to the Revised Atlanta Classification, and patients were grouped as mild or moderate-to-severe. Statistical analyses included ROC curve analysis and logistic regression. Results: Among 454 patients, 371 (81.7%) had mild and 83 (18.3%) had moderate-to-severe AP. Patients with more severe disease were older and showed significant differences in several laboratory parameters. NLR, PLR, SII, dNLR, and Log-SII were significantly higher in the moderate-to-severe group. However, all indices demonstrated limited discriminative performance, with dNLR showing the highest AUC (0.612). In multivariate analysis, only age and C-reactive protein (CRP) remained independent predictors of disease severity. Conclusions: Systemic inflammatory indices are associated with disease severity in AP; however, their predictive performance is limited. Conventional parameters such as age and CRP remain more reliable for risk stratification.

Abstract: Piles with continuous helix (referred to herein as "screw pile") is a new configuration of helical piles. It features a continuous helix spiraling several pitches around a smooth shaft forming a "threaded shaft". This study investigates the compressive capacity and behavior of helical and screw piles using 3D numerical models calibrated and validated against full-scale field testing. The bearing capacity factor, Nc, for helical piles is back-calculated from the numerical results and compared against standard theoretical assumptions to evaluate their accuracy in predicting ultimate capacity. Parametric studies are conducted considering screw piles configuration, including shaft diameter, pitch size, helix diameter, as well as soil strength. The results reveal that shaft resistance accounts for up to 89% of the total capacity. Analysis of load distribution, shear contours, and displacement contours at failure allowed for the identification of different failure modes of soil adjacent to the pile’s threaded shaft: Individual Bearing Mode (IBM), Cylindrical Shear Mode (CSM), and a combined mode. The study identifies specific parametric thresholds for these modes in both sand and clay layers. Furthermore, varying clay strength is found to alter the development of the shear surface, transitioning from localized bearing to continuous shearing along the threaded shaft. Finally, apparent shaft resistance factors, α and β, are back-calculated to provide ractical parameters for evaluating the resistance of threaded shafts in layered soil.

Abstract: This study presents an approach to quantifying impact-induced damage severity in composites, focusing on synthetic carbon fibre reinforced polymer (CFRP), natural flax fibre reinforced polymer (FFRP) and hybrid fibres reinforced polymer (HFRP) composite of carbon and flax. The investigation aims to quantitatively characterise impact damage under energies ranging from 10 to 70 J through acousto-ultrasonics (AU) testing, proposing an efficient technique for evaluating the integrity of various FRP composites under in-service conditions. AU testing was performed at azimuthal angles of 0°, 30°, 45°, 60° and 90°, utilising acousto-ultrasonic waveform indices (AUWIs), such as wave velocity, peak amplitude, energy content, centroid frequency and skewness factor. Damage severity index is correlated with the damage mode. The findings establish that wave velocity is a reliable parameter for quantifying damage severity across all composite material types considered, with high adjusted R² values of 0.92 for CFRP, 0.89 for FFRP and 0.90 for HFRP. Peak amplitude also shows considerable sensitivity. Finally, this research highlights the limitations of traditional non-destructive evaluation (NDE) techniques and demonstrates the potential of combining multi-damage metrics with advanced imaging methods, such as X-ray micro-computed tomography (X-ray µCT) and scanning electron microscopy (SEM), to provide comprehensive assessment of damage in various composite materials. The proposed methodology offers a promising approach for quantifying the impact damage severity in composite structures, as applicable to wind turbine blades, amongst other structural components.

Abstract: Schistosomiasis intermediate host snails’ data in the Democratic Republic of the Congo are limited and geographically dispersed. The objective of this study was to characterize snail habitats and identify environmental determinants of their presence. Monthly malacological surveys were conducted at 72 water contact sites. The morphological identification of the snails was complemented by the sequencing of the mitochondrial cox1 gene in order to guarantee confirmation of the species. The physicochemical parameters of the water, as well as human activities on the site, were recorded. The associations between environmental characteristics and snail presence were evaluated using generalized estimating equation models to account for repeated measurements. A total of 172,491 snails were collected, including 4,899 Schistosoma intermediate hosts (Bulinus spp., n = 3,812; Biomphalaria spp., n = 1,087). Biomphalaria pfeifferi, Biomphalaria sudanica, Bulinus truncatus, and Bulinus forskalii were identified. Biomphalaria species were detected in stagnant or slow-flowing waters; however, they occupied distinct habitats. The presence of snails was found to be independently associated with stagnant water and inversely associated with cassava retting, dishwashing/laundry, and river crossing. These findings provide baseline evidence on the distribution and ecological determinants of the Schistosoma intermediate host in Kimpese, supporting targeted malacological surveillance and integrated control strategies.

Abstract: Connexin-43 (CX43) lateralization in ventricular myocardium has been associated with abnormal impulse propagation and increased arrhythmia susceptibility. Its quantitative assessment in histological sections remains challenging because of the difficulty of segmenting individual cardiomyocytes and the reliance of previous methods on geometric rules applied to segmented cell profiles. Here, we present CLARISA, a deep learning framework for classifying CX43-positive regions as either terminal or lateralized directly from fluorescence images, without requiring cardiomyocyte segmentation. An expert-annotated dataset was generated from left-ventricular cryosections of Wistar rat hearts, in which CX43-positive regions were labeled according to their distribution pattern. A dual-stream convolutional classifier based on EfficientNetV2-S was trained to capture both the local and contextual morphology of each region. In addition, an inference module applicable to whole tissue sections was developed to generate spatial lateralization probability maps and global percent lateralization estimates consistent with expert annotation. On the test set, CLARISA achieved a ROC-AUC of 0.905 and a PR-AUC of 0.810. These results support the feasibility of automated assessment of CX43 distribution patterns without explicit cardiomyocyte segmentation. The complete codebase is publicly available, together with access to the pretrained model and the image data used in this study. The Hugging Face model card reports the same held-out test metrics and states that the checkpoint is intended to be used with the main repository.