Abstract:

A hypothesis was proposed that continuous dual-circuit mixing can be achieved by equipping a feed mixer-distributor with two leveling–mixing finger shafts, which, after lifting the feed mass to a certain height, collect it in the central part of the hopper and divide it into two flows directed toward the end walls of the hopper. In this case, continuous dual-circuit mixing is performed during each rotation of the leveling–mixing shaft. A structural and technological scheme, engineering documentation, and an experimental prototype of the feed mixer-distributor were developed. The machine consists of a 3.0 m³ hopper, two horizontal augers, two leveling–mixing finger shafts, a loading conveyor, and a drive mechanism. Theoretical investigations were conducted, and analytical expressions were obtained to determine the circumferential velocity of the fingers of the leveling–mixing device, which should ensure the movement of the feed mixture without scattering and provide the release of the feed mass from the finger surface at a finger rotation angle of 30°. Calculations based on the obtained analytical expressions showed that the critical circumferential velocity of the fingers was 0.8 m/s, while the rotational speed of the finger shaft was 19 min^-1. An analytical expression was also obtained to determine the velocity of feed mixture movement along the finger surface. Based on the calculations, the optimal value of this velocity was found to be 0.7 m/s. This value corresponds to the rational velocity of feed mixture transportation toward the end walls of the hopper. Laboratory experiments were carried out using the feed mixer-distributor at a leveling–mixing finger shaft rotational speed of n = 20 min^-1. The optimal mixing time required to achieve the target mixture uniformity was 5.5 min, which is 15.4% lower than that of existing machines. Comparative experiments also showed that incorporating the leveling–mixing device into the feed mixer-distributor reduced the power consumption of the mixing process by 34%.

Abstract: Microbiology laboratories generate extensive experimental outputs that are often in-sufficiently translated into applied innovation and technology development. This study presents a Routine-to-Research-to-Innovation (R2R) framework integrating routine labor-atory workflows with bioactivity validation, formulation development, and intellectual property (IP) mapping. Lactic acid bacteria isolated from Thai fermented foods demon-strated strong bacteriocin activity and storage stability, while secondary metabolites de-rived from Streptomyces and Brevibacillus exhibited antibacterial, antioxidant, and an-ti-inflammatory activities with prototype formulation potential. Red palm oil-based sys-tems enriched with microbial bioactives also showed favorable physicochemical stability under accelerated conditions. Patent landscape analysis (Thailand, 2020–2025) demon-strated translational alignment between laboratory outputs and existing innovation do-mains, supporting the potential application of the R2R framework in translational micro-biology, technology transfer, and early-stage innovation development.

Abstract: Introduction- Undergoing drastic metabolic and behavioral transformations, carcinogenesis is a cumulative process which leads to excessive proliferation in an unusual manner, camouflage to escape surveillance by the immune system. Aim-The aim of this review is to provide an in-depth exploration of immunology of cancer, highlighting the mechanisms and various aspects immune system, how it interacts with cancer cells and the challenges coming in its way due to tumor cell immune evasion. Method-A comprehensive literature survey and search was made across major electronic databases, which include PubMed, Scopus, and Web of Science, covering publications up to June 2025. The search strategy employed combinations of keywords and medical terms relevant to tumors. Conclusion-Representing one of the most significant advances in the field of oncology the evolving field of cancer immunotherapy offers promising treatment options thereby harnessing the body’s immune system to target cancer cells. Justification-it is not our intention to revisit many of the issues relating to tumor immunology, which have already been covered in detail previously in the literature. Rather this article focuses on the aspects that by compiling desperate foundational knowledge and parallel newer advances in this rapidly evolving field, the review offers a holistic framework of worth to our researchers, clinicians, and students working in the area of cancer immunology and oncology.

Abstract: While aspect-based sentiment analysis (ABSA) has gained significant progress in the identification of explicit opinion targets, the more challenging case, implicit aspects, has not been sufficiently studied. Implicit aspect extraction is particularly challenging as it relies on contextual and semantic cues and requires systems to infer what reviewers mean rather than just say. In this paper, we propose a four-component hybrid solution for explicit and implicit aspect extraction that formulates aspect extraction as a controlled text generation task. The solution combines: (i) a fine-tuned decoder-only large language model as a generative baseline, (ii) an iterative residual generation strategy that recovers multiple aspects through successive regeneration passes, (iii) paraphrase-based input transformation to broaden the contextual signal, and (iv) domain-specific knowledge graphs activated by linguistic signals to infer implicit aspects. The novelty is not in the individual components themselves, but in the principled orchestration of these components and the gating logic for when each stage is activated. Extensive experiments are conducted on eight benchmark ABSA datasets in both English and Arabic including SemEval 2014, 2015, 2016, ACOS and M-ABSA for English and SemEval 2016, HAAD, and M-ABSA for Arabic. The proposed solution consistently outperforms strong baseline methods and recent state-of-the-art models on English datasets with F1-scores of 0.8533, 0.713, 0.7859, 0.793 and 0.664 respectively, and F1-scores of 0.7336, 0.4765 and 0.7601 on Arabic datasets respectively. These results demonstrate the effectiveness of generative modeling, iterative generation, paraphrasing and structured knowledge for aspect extraction, and the potential of the proposed approach for implicit aspect identification in particular for morphologically rich and low-resource languages such as Arabic.

Abstract: It is shown that the existence of a maximal proper acceleration implies a bound for the acceleration in FICS.

Abstract: The use of Artificial Intelligence (AI) and Machine Learning (ML) in cybersecurity, especially for creating Intrusion Detection Systems (IDS), has become increasingly important. These systems are essential for detecting malicious behaviour, identifying network issues, and stopping cyberattacks in real time. Although extensive research has been conducted on various ML and Deep Learning (DL) models for IDS, the current literature remains incomplete. It has many different datasets, methods, and evaluation standards. As cyber threats become more advanced, it is crucial to conduct a thorough analysis of ML techniques for intrusion detection. The goal of this Systematic Literature Review (SLR) is to give a full picture of the most recent academic articles on ML-based IDS. The study addresses important research questions about the most widely used algorithms, the types of attacks and network environments covered, the methodological problems that remain unsolved, and the new trends that should shape future research. Following the PRISMA framework, we conducted a systematic review of peer-reviewed articles published between January 2022 and May 2025. We searched IEEE Xplore, ACM Digital Library, and SpringerLink, yielding 22,558 initial records. After carefully applying strict inclusion criteria, 125 papers were selected for the final analysis. We created a standardised data extraction form (i.e., using MS Excel) to gather bibliographic details, research emphasis, methodological strategies, datasets, evaluation criteria, and recognised constraints. We employed thematic analysis to develop a clear taxonomy. We identified five main research themes in our analysis: (1) ensemble and hybrid learning pipelines focused on performance optimisation (30 papers), (2) context-specific IDS designs for Internet of Things (IoT), cloud, and Software-Defined Networking (SDN) environments (34 papers), (3) data-centric engineering that deals with class imbalance and feature selection (20 papers), (4) deep neural architectures for representation learning (31 papers), and (5) trustworthiness concerns like adversarial robustness, zero-day detection, and Explainable AI (XAI) (10 papers). Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Random Forests are the most commonly used algorithms, often combined. Nonetheless, significant deficiencies remain: about 2% of papers incorporate XAI, only 4% focus on adversarial robustness, and none validate their models in real-world production settings. Denial of Service (DoS) and Distributed DoS (DDoS) are the most common types of attacks in the literature, while Web attacks, ransomware, and advanced persistent threats remain poorly studied. The number of publications grows at an average of 30.2% annually, but the field still relies on legacy benchmark datasets rather than operational validation.

Abstract: The embryological basis for the lamination of the retroperitoneal fascia has long remained an anatomical paradox. Classical peritoneal fusion theories cannot account for either the highly organized multilaminar architecture of the mature fascia or the striking temporal lag between early visceral fixation (gestational weeks 9–18) and the abrupt, synchronized emergence of definitive fascial laminae around week 20. Integrating recent advances in fetal biomechanics, we propose that this developmental lag reflects a system-level mechanical transition driven by the geometric constraints of scaling and the evolutionary demands of obligate bipedalism.

Abstract: Coastal biodiversity conservation is challenged by fragmented datasets and the limited integration of environmental conditions into marine spatial planning (MSP). This study develops an operationalized adaptive Marine Spatial Planning (MSP) to support biodiversity conservation by linking remote sensing data, IoT-based water quality measurements, and spatial optimization within Spatial Decision Support System (SDSS). The Tidung Islands are used as a case study, where benthic habitats are mapped from 3 m PlanetScope imagery. Water quality observations are processed into the Nemerow Pollution Index (NPI) and subsequently interpolated through an ensemble approach that combines inverse distance weighting, random forest, and gradient boosting. A key innovation of this study is the incorporation of the Nemerow Pollution Index (NPI) as a dynamic environmental cost layer within Marxan-based conservation prioritization. These data were incorporated alongside anthropogenic pressures to evaluate multiple conservation scenarios The ensemble interpolation demonstrated strong predictive performance (R²=0.76;MAE=0.0306), enabling reliable spatial representation of environmental conditions. The results show that integrating environmental quality into MSP significantly improves spatial efficiency, reduces fragmentation, and enhances ecological representation compared to conventional approaches based on static variables. Moderate conservation targets (≈30%) produced the most optimal solutions (~2,200 cost; ~11 km boundary), while more ambitious targets resulted in fragmented and inefficient spatial configurations. The proposed framework offers a transferable approach for data-limited coastal regions, contributing to the advancement of adaptive biodiversity conservation strategies.

Abstract: Abiotic stresses, including drought, salinity, alkalinity, temperature extremes, flooding, heavy metals, and emerging pollutants, challenge plant growth and productivity by disturbing water relations, ion balance, redox homeostasis, membrane stability, energy metabolism, and developmental progression. Although substantial progress has been made in identifying stress-responsive hormones, second messengers, kinases, transcription factors, transporters, and metabolic regulators, plant stress adaptation cannot be fully explained by linear signaling cascades or single tolerance genes. A major unresolved question is how early molecular events are reorganized into coordinated physiological and developmental outputs that support survival, recovery, and productivity. In this review, we propose an intermolecular interaction–driven adaptive remodeling framework for plant abiotic stress responses. This framework emphasizes that stress tolerance emerges from dynamic changes in receptor–ligand recognition, protein–protein interactions, calcium decoding, redox-sensitive modification, phosphorylation networks, transcriptional regulation, chromatin-associated control, and metabolite-mediated feedback. We discuss how these interaction networks converge on core signaling hubs, including abscisic acid, reactive oxygen species, Ca²⁺, and kinase/phosphatase systems, and how they remodel stomatal behavior, root architecture, ion and pH homeostasis, redox buffering, metabolism, development, and reproductive resilience. We further highlight how natural variation, multi-omics, genome editing, high-throughput phenotyping, and field validation can translate interaction-centered stress biology into crop resilience. This perspective provides a conceptual bridge between molecular stress perception, network behavior, physiological adaptation, and climate-resilient agriculture.

Abstract: Long‑acting injectable (LAI) formulations have transformed adherence in several psychiatric conditions, yet no depot antidepressant currently exists. People with major depressive disorder (MDD) remain at risk of accidental overdose from prescribed oral medications, particularly during periods of cognitive impairment or crisis. Artificial intelligence (AI)–driven molecular modelling now enables the design of antidepressant compounds optimised for slow‑release, water‑based depot systems that avoid the fibromas and granulomatous reactions associated with oil‑based injectables. This study outlines an AI‑enabled workflow for generating a novel antidepressant molecule with favourable receptor‑binding properties, low toxicity, and compatibility with biodegradable, aqueous depot carriers. The resulting formulation has the potential to reduce overdose risk, improve adherence, and decrease the burden of frequent GP prescribing.

Abstract: Since its emergence in 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected a wide range of animal species, including wildlife. Although SARS-CoV-2 infection has been widely reported in wildlife, particularly in white-tailed deer (WTD; Odocoileus virginianus) across the United States, data on viral circulation in New England wildlife remain limited. Here, we evaluated SARS-CoV-2 infection and serological evidence of previous exposure in free-ranging wildlife from the northeastern United States. We examined samples from 1,646 animals representing 28 wildlife species, collected through wildlife rehabilitation centers, clinics, and hunter harvests in New England and Virginia between 2022 and 2025. SARS-CoV-2 RNA was detected in three WTD from Massachusetts and Vermont. Phylogeographic analysis of Vermont WTD sequences indicated a human SARS-CoV-2 lineage as the most likely source, consistent with a single human-to-deer spillover event followed by subsequent circulation within deer. Serological screening using ELISA detected SARS-CoV-2 antibodies in 12 individ-uals from three species, including Eastern cottontail (Sylvilagus floridanus), Eastern coyote (Canis latrans), and raccoon (Procyon lotor), although neutralizing antibodies were found in only one Eastern cottontail. Overall, these findings reveal ongoing but limited SARS-CoV-2 circulation in northeastern wildlife and highlight the importance of continued surveillance to detect spillover events, monitor viral evolution, and evaluate potential risks posed by wildlife.

Abstract: Accurate rainfall estimates are essential for managing water resources and planning for climate risks in semi‑arid regions, yet long‑term gauge networks in these environments are often extremely limited. In this study, we evaluate three widely used multi‑source precipitation datasets; CHIRPS, IMERG, and ERA5‑Land, against long‑term observations from Ed Dueim and Kosti, the two main reference stations in White Nile State, central Sudan. The assessment covers monthly and annual scales across each product’s available record (1952–2022) and uses a broad set of metrics, including Pearson and Spearman correlations, NSE, KGE, RMSE, MAE, percent bias, and categorical detection scores (POD, FAR, CSI). All three datasets capture the region’s single‑peak June–October monsoon pattern, but their accuracy differs sharply when it comes to rainfall amounts and year‑to‑year variability. CHIRPS performs best overall, with monthly NSE values around 0.77 and KGE between 0.79 and 0.88, along with a consistent dry bias of 5–13%—a predictable error that can be corrected operationally. IMERG shows strong monthly correlations but consistently overestimates rainfall by 25–42%, which leads to unreliable annual totals (NSE = −1.93 to −2.21). ERA5‑Land performs worst across nearly all metrics, with monthly NSE near or below zero, annual NSE dropping to −15.34, and frequent false alarms during the dry season. Taken together, the evidence points to CHIRPS as the most reliable dataset for routine hydro‑climatic monitoring in White Nile State, while IMERG and ERA5‑Land may still be useful in more specialized or time‑specific applications.

Abstract: Autonomous Haulage Systems (AHS) are becoming increasingly popular in recent years as mining operations seek to improve productivity and remove workers from hazardous environments. The integration of this technology in a systematic manner implies not only change management in operations, but also deeper perspective into mine planning implications. Currently, existing literature describes AHS and their implementation guidelines with focus on operational safety and autonomous system architecture, without systematically addressing required planning-level adaptations. This study aims to identify how mine planning frameworks must evolve to accommodate autonomy in open-pit metal mining operations. A systematic review is conducted using the PRISMA methodology with emphasis on identifying the principal aspects of AHS that must be considered in mine planning strategies. Findings reveal major shifts in workforce dynamics, communication infrastructure, and haul road geometry, alongside ongoing debates regarding optimal road width and load channelization. The study highlights the need for (i) holistic approaches to haul road and mine design, that are aware of technology, geotechnical, and mineral aspects with a data driven perspective (ii) human-systems integration and new needs in human-autonomous collaboration, and (iii) empirical validation of workforce transition strategies for more effective and safe deployment.

Abstract: Mitochondria regulate cellular energetics, redox balance, apoptosis, and inflammatory signaling in oral, airway, and systemic tissues. Hypoxia is a powerful modulator of mitochondrial function, with effects ranging from adaptive hormesis to overt injury. Cyclic altitude training, most delivered as intermittent hypoxic exposure or intermittent hypoxia training (IHT), has been proposed as a strategy to improve mitochondrial efficiency and exercise performance. By contrast, obstructive sleep apnea (OSA) exposes patients to uncontrolled chronic intermittent hypoxia (CIH), a pattern increasingly linked to endothelial dysfunction, ceramide-mediated mitochondrial dysfunction, insulin resistance, systemic inflammation, oral dysbiosis, and periodontitis. This narrative review covers intermittent hypoxia, mitochondrial biogenesis, hypoxia-inducible factor signaling, OSA, periodontitis, oral microbiome shifts, nitric oxide biology, and smoke-related mitochondrial injury. Appropriately dosed IHT can increase mitochondrial biogenesis, improve mitochondrial morphology, and augment oxidative capacity through pathways involving PGC-1alpha, hypoxia-inducible signaling, mitochondrial dynamics, and reactive oxygen species-dependent hormesis. In contrast, CIH in OSA promotes oxidative stress, sympathetic activation, endothelial injury, and inflammatory signaling and is associated with worse periodontal status and altered salivary microbiome profiles. Controlled IHT and OSA-related CIH, therefore, represent opposite ends of a hypoxia continuum, and mitochondrial health connects sleep-disordered breathing, periodontal inflammation, environmental exposures, and systemic cardiometabolic risk within a single conceptual frame. Sphingolipid signaling—particularly hypoxia- and toxicant-driven ceramide accumulation—connects CIH, inhaled environmental exposures, mitochondrial fragmentation, and the development of insulin resistance.

Abstract: Fibroblast growth factor (FGF) signaling plays a pivotal role in the development, maintenance, and regeneration of musculoskeletal tissues. While its transcriptional regulation has been extensively characterized, accumulating evidence indicates that FGF activity is also modulated by a diverse array of post-transcriptional mechanisms. In this review, microRNAs, long non-coding RNAs, alternative splicing, and RNA modifications are examined as key regulators of FGF ligands and receptors across bone, cartilage, muscle, and tooth. Enhancer RNAs and RNA-binding proteins are also discussed as potential modulators of FGF transcript stability and translation. By integrating both established and emerging layers of RNA-level regulation, this review outlines a complex, tissue-specific architecture that fine-tunes FGF signaling in development and repair. To highlight this layered regulatory dimension, the concept of a pathway-specific RNA regulome is introduced, referring to the network of RNA-based mechanisms that modulate signaling cascades, such as FGF, across distinct biological processes. The therapeutic implications of targeting post-transcriptional nodes, particularly through non-coding RNAs and epitranscriptomic marks, are highlighted as promising avenues for future musculoskeletal interventions.

Abstract: Chemotaxis describes a number of mechanisms by which living organismsnavigate a concentration field, allowing them for exampleto migrate towards a molecule source. In amoeboid chemotaxis, theexternal concentration signal must first be converted to an intracellularone, which modulates cell crawling. Typically, the leadingedge of the cell forms protrusions and adheres to their adjacentsurfaces, while the trailing edge contracts and detaches from surfaces.Therefore, first a polarity with respect to the extracellularsignal has to be established to define what leading and trailing edgeactually is. In this paper we develop a chemical reaction networkthat is capable of generating the respective polarity signal over thewide range of concentration levels typically observed in biologicalsignaling. Based on the system’s equilibrium, we derive designguidelines for the network and verify them by dynamic simulationsof the overall system.

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.