Abstract: Improper management of domestic organic solid waste (DOSW) poses an environmental challenge, and composting offers a low-cost valorization alternative, though the effect of substrate composition and microbial inoculation on process performance remains unclear. This study evaluated the effect of substrate composition and lactic acid bacteria inoculation on the dynamics and physicochemical and microbiological quality of compost from DOSW via horizontal composting, and its agronomic performance in lettuce (Lactuca sativa L.). A completely randomized design with four treatments (T0: 100% DOSW; T1: 50% DOSW + 50% rice husk + bacteria; T2: 75% DOSW + 25% rice husk + bacteria; T3: 100% DOSW + bacteria) and three replicates ran 103 days, monitoring temperature, moisture, pH, color, and odor; final compost was characterized per NTC 5167 and evaluated for effects on lettuce growth and soil. No treatment reached a thermophilic phase, though rice husk improved porosity and reduced odor persistence. All composts were free of Salmonella spp. and enterobacteria, meeting safety criteria. T2 showed the best balance of stability, microbiological activity, and nutritional quality, while inoculation did not improve lettuce yield, with T0 yielding the highest biomass. Findings confirm horizontal composting of DOSW with structuring materials is a viable, safe, low-cost alternative.

Abstract: The new yearly 1.1 million cancer surges and considerably increasing mortality, post-decades of decline, require urgent and effective therapy. Targeted anticancer therapy is an attractive strategy for treating cancer by targeting critical receptors. Homologous Mouse Double-Minute RINGs (MDM2-MDMX RING) negatively regulate the p53 tumour-suppressing role by promoting proteasomal degradation. Interventively, Hinok., MMRi62, MMRi64, and MMRi71 exert a therapeutic downregulation against MDM2-MDMX RING heterodimer by activating p53’s apoptotic arm, but their binding mechanisms remain poorly understood due to the RING domain’s high structural flexibility and lack of co-crystal structures. This study reveals the underlying dynamic conformational stability and flexibility of the MDM_2G443T-MDMX interface, and the potential mechanistic inhibitor binding using the all-atom MD simulations. The MMRi64-bound and Hinok-bound systems exhibited the lowest RMSD values (2.28 Å and 2.34 Å, respectively), indicating significant protein stabilization. MMRi71-bound (1.03 Å) and MMRi64-bound (1.30 Å) complexes displayed the lowest mean RMSF values, demonstrating that these ligands lock the protein into a rigid conformation. Conversely, the MMRi62-bound system showed the highest average RMSD and the highest RMSF (1.74 Å), identifying it as the most flexible complex. The ligand-bound MDM2G443T-MDMX complex underwent notable secondary structure transitions. MMGB/PBSA calculations revealed favorable binding free energies (-22.69, -26.42, and -20.30 kcal/mol) for the key interactions. Arg444, Pro445, Lys473, and Pro476 provided the highest binding free energy contributions to the RING domains. Over the 200 ns simulation, Hinok demonstrated dual interactions with both MDM2G443T-MDMX RING domains, highlighting a potential dual-inhibition mechanism, whereas MMRis interacted selectively with MDM2.

Abstract: Psychological stress can cause or exacerbate irritable bowel syndrome (IBS). According to the Rome V framework, functional gastrointestinal disorders, including IBS, are classified as gut–brain interaction disorders. These disorders involve information processing by the central nervous system. Therefore, to understand IBS pathophysiology, it is necessary to focus on the central neural circuits regulating visceral sensation and colorectal motility. In this review, we first outline the autonomic outflow that controls the colorectum and identify the descending neural pathways regulating these outflow circuits and the sensory input circuits from the colorectum, as well as the hypothalamic, midbrain, limbic, and cortical regions. Finally, we outline their effects within the context of stress.

Abstract: Centenarians, semi-supercentenarians and supercentenarians (i.e. oldest centenarians) display complex and heterogeneous immune remodelling. In this setting, immune at-trition, memory, cytotoxic defence, inflammatory regulation and tissue surveillance remain sufficiently balanced to support survival with relatively preserved health. This review examines these processes within an integrated framework encompassing sex and gender differences, genetic background, the exposome and immunobiography. We consider how haematopoietic stem cell ageing, age-related myeloid bias, thymic invo-lution, immune ageing and inflammaging contribute to immune-system remodelling across the life course. We then review evidence on immunity in centenarians and in semi- and supercentenarians, including NK cell expansion, T and B cell remodelling and the persistence of long-term immunological memory, as characterised by conventional flow cytometry and, more recently, single-cell transcriptomic analyses. Available data suggest that exceptional longevity does not reflect the maintenance of a youthful immune system, but rather the capacity to accommodate and compensate for age-related immune re-modelling through preserved cytotoxic surveillance, controlled inflammation, main-tained immune memory and favourable lifelong adaptation to antigenic exposures. Understanding these mechanisms may help identify immune trajectories associated with healthspan, delayed morbidity and resistance to age-related diseases.

Abstract: Deep-sea hydrothermal vents and cold seeps sustain highly productive animal communities through chemosynthetic symbioses, among which bathymodioline mussels are prominent examples. Bathymodioline gill bacteriocytes accommodate intracellular chemosynthetic symbionts, including sulfur- and/or methane-oxidizing bacteria depending on the host species, while remaining sheltered in an epithelium continuously exposed to environmental microorganisms, creating a fundamental immunological problem: how can an innate defense system remain effective without eliminating the microbial partners on which host nutrition depends? This review examines this problem through Bathymodiolus azoricus, integrating two decades of work on its cellular immunity, gill transcriptome, microbial challenge responses and symbiosis biology with recent mechanistic studies from related bathymodiolines. Central to the present synthesis are previously reported B. azoricus observations showing that gill tissue can mount local transcriptional responses to bacterial challenge, while hemolymph serum differentially modulates immune-gene expression following exposure to symbiont preparations or non-symbiotic Vibrio. Immune-gene expression also varies along the anterior–posterior gill axis, with lower expression in the posterior budding zone than in mature anterior filaments. We interpret this zonation primarily as a feature of tissue maturation rather than demonstrated active immune suppression, consistent with evidence that newly formed filaments are initially aposymbiotic and become colonized only after formation. Together, these observations evoke a host–symbiont–pathogen triad in which local gill-tissue responses, systemic humoral modulation and gill development constitute interacting levels of immune organization and compartmentalization. We consider this tissue-level model alongside comparative evidence for putative symbiont-uptake mechanisms, post-engulfment microbial discrimination, lysosomal regulation, symbiont digestion and bacteriocyte turnover, including the mTORC1-dependent phagosome-digestion checkpoint demonstrated in Bathymodiolus japonicus. Rather than assuming that these mechanisms are conserved across species, we distinguish explicitly between findings established in B. azoricus, evidence from other bathymodiolines and canonical pathways used as mechanistic context. We conclude by identifying unresolved components of B. azoricus immunity, including the prophenoloxidase system, the broader antimicrobial-peptide repertoire and the relationship between cellular checkpoints and tissue-level gill zonation, and consider the prospective biotechnological relevance of mechanisms that tolerate persistent microbial symbiosis without loss of immune vigilance.

Abstract: Bordetella pertussis elicits both innate and adaptive cellular responses, yet their relative contribution to peripheral IFN-γ measurements and the temporal behavior of circulating T-cell immunity in adults remain incompletely defined. We investigated B. pertussis-reactive cellular immunity in peripheral blood in healthy and convalescent adult donors using IFN-γ ELISpot assays after stimulation with whole-cell B. pertussis or bacterial components (LPS and B. pertussis-derived LOS), and HLA-A02 genotyping. Short-term stimulation of unfractionated PBMCs produced a prominent IFN-γ response that was not clearly associated with previous vaccination or pertussis history. LOS and LPS induced strong responses in PBMCs but little detectable IFN-γ secretion by T cells, whereas whole-cell B. pertussis retained T-cell-associated IFN-γ response. Circulating B. pertussis-reactive T-cell responses were highest after recovery, declined substantially within approximately 3 months, and approached baseline by ≥1 year. CD8+ IFN-γ responses were preferentially detected in HLA-A*02-positive donors. These findings indicate that adult peripheral immunity to B. pertussis comprises cellularly and temporally distinct IFN-γ-producing components and that cellular context, time after infection, and HLA class I genotype influence their detection and interpretation.

Abstract: Background: This study was conducted to determine the relationship between microorganisms found in the hands, noses, and throats of music students and bacterial contamination on the wind and string instruments they use, and to evaluate the effect of the cleaning process on instrument contamination. Methods: Swab samples were taken from the hands, noses, and throats of the participants; from the mouthpieces, inner surfaces, and keys of the flutes; and from the fingerboards and bows of the string instruments. The effectiveness of the cleaning process was evaluated by comparing samples taken from the flutes after use and cleaning. The samples were examined using microbiological culture methods, and the isolated microorganisms were identified. In addition, the students’ participation in the orchestra and instrument-cleaning habits were evaluated. Results: There were varying relationships between the hand and upper respiratory tract microbiota of the students and bacterial contamination on the instruments, depending on the instrument type and the sampling area. Significant relationships were found between the upper respiratory tract microbiota and instrument contamination in flute-playing students; pathogen growth was completely eliminated, and flora growth was significantly reduced after cleaning. Furthermore, orchestra participation and less frequent instrument-cleaning habits were associated with pathogen colonization in the upper respiratory tract. Conclusions: These findings suggest that musical instruments may be susceptible to bacterial contamination related to the user’s microbiota, and that regular cleaning practices can play a significant role in reducing this contamination. Accordingly, it is recommended to develop standard practices for instrument hygiene and increase hygiene awareness in music education settings.

Abstract: Sixth-generation (6G) wireless systems target ubiquitous connectivity by integrating terrestrial (TNs) and non-terrestrial networks (NTN) cooperation, including low-Earth orbit (LEO) satellites, high-altitude platform stations (HAPS), and unmanned aerial vehicles (UAVs). In such a vertically stratified architecture characterized by massive multi connectivity, the selection of the most suitable tier or tier-combination for each mobile user is a complicated task, as the decision depends jointly on the propagation environment, user requirements, and the instantaneous characteristics of the topology. This paper proposes an altitude-aware Deep Learning (DL) framework that casts multi-tier network selection as a seven-class classification problem spanning standalone TN, LEO, HAPS, and UAV access as well as their TN-assisted multi-connectivity combinations. A fourteen-feature physics-based dataset is generated through extensive simulations based on standardized channel and geometry models, including 3GPP TR 38.901 terrestrial pathloss and line-of-sight probability, satellite-constellation elevation angles, and air-to-ground link geometry for HAPS and UAV. A deep neural network (DNN) is trained on the aforementioned dataset and assessed using stratified five-fold cross-validation. The proposed model achieves an overall classification accuracy of 88.1% with a macro-averaged F1-score of 0.88 and a single-sample inference latency of approximately 2 ms, well within typical 6G handover budgets. The results demonstrate that requirement- and geometry-aware tier selection can be performed accurately, enabling real-time decision-making in ambiguous multi-connectivity configurations for 6G networks.

Abstract: South American tomato leaf miner Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae) is responsible for significant biological invasions of tomato crops, perturbing global food production. The larvae feed within leaves, stems, and fruits, creating characteristic mines that reduce photosynthetic capacity by causing leaf necrosis and rendering fruits unmarketable. Synthetic pesticide applications used to suppress pest populations have detrimental impacts on the environment and human health and incur high production costs to farmers. These prevailing situations highlight the urgency of sustainable pest management practices. AI-driven pest monitoring systems assist farmers by enabling early pest detection and predicting expected pest outbreaks before their devastating attacks on crops. These data-driven insights facilitate farmers in implementing proactive pest control strategies, permitting reduced but impactful pesticide use through tracking pest population levels in the field. To develop smart pest monitoring systems to control T. absoluta outbreaks in tomato crops in greenhouse settings, this study highlights the use of AI-based pest detection and population prediction models to detect pest incidences and predict outbreaks in advance. A real-time image dataset was collected in greenhouse conditions to train various object detection models such as YOLOv10, YOLOv11, and YOLOv26 for the early detection of T. absoluta. The YOLOv26 model outperformed its counterpart approaches in terms of pest detection accuracy and inference speed by delivering 98.4% (mAP50) and 0.053 seconds, respectively. For early prediction of pest outbreaks, abiotic parameter data were collected through IoT sensors alongside pest biological data to generate early forecasts. Feature engineering and feature selection approaches were used for data preparation to model pest population dynamics. Various ML and ensemble learning approaches were implemented to model pest dynamics in relation to different meteorological parameters, where a blended ensemble learning technique resulted in the best prediction performance by delivering 4.99517, 62.415492, and 7.900348 values for MAE, MSE, and RMSE metrics, respectively.

Abstract: Among the mechanisms involved in disease progression, apoptosis has emerged as a central process contributing to immune dysregulation, lymphocyte depletion, organ dysfunction, and poor clinical outcomes. This review summarizes the current evidence regarding the molecular pathways of apoptosis in sepsis and discusses the potential role of apoptosis-related biomarkers in improving early diagnosis, prognostic stratification, and therapeutic decision-making. The limitations of conventional clinical assessment and currently available biomarkers are highlighted, emphasizing the need for more comprehensive approaches capable of reflecting the temporal evolution of the disease. Recent advances in bioengineering, including biosensors, microfluidic platforms, multi-omics technologies, and continuous physiological monitoring, offer new opportunities for real-time assessment of critically ill patients. Furthermore, artificial intelligence and machine learning provide powerful tools for integrating heterogeneous clinical, physiological, and molecular data into predictive models that support personalized management strategies. The convergence of apoptosis research, bioengineering innovations, and artificial intelligence has the potential to transform sepsis care by enabling earlier recognition of disease progression, more accurate risk stratification, and the development of precision medicine approaches tailored to individual patients.

Abstract: Nocturnal Hypoglycemia (NH), which occurs when blood glucose levels fall below 70 mg/dL during sleep, remains a common complication in people with Type 1 Diabetes. Despite advances in Continuous Glucose Monitoring (CGM), controlling both glucose and glycemic events remains challenging. Artificial Intelligence (AI) models are promising for identifying CGM patterns associated with NH and supporting its prediction, thus reducing its occurrence and complications. However, two main challenges limit the performance of AI models for NH: (i) the class imbalance due to the low number of NH events in data; and (ii) the high-dimensionality caused by the extraction of CGM-derived features. To address this, we propose a two-stage methodology that combines ensemble Feature Selection (FS) methods and synthetic data augmentation. We performed CGM-derived feature extraction by using a time-window approach with overlapping and non-overlapping approaches. Model interpretability is provided through feature analysis using Shapley additive explanations, enabling the identification of associated NH factors. We employed CGM data from 52 individuals with T1D, collected at Complejo Hospitalario Insular-Materno Infantil de Las Palmas de Gran Canaria, Spain. Among the temporal configurations evaluated, the 6-hour window yielded the highest predictive performance. With ensemble FS retaining the top 40% of features, the best-performing model achieved an Area Under the Receiver Operating Characteristic Curve of 0.68 using a logistic regression and overlapping windows. Our findings highlighted the importance of window-based extraction in improving predictive performance. Our interpretable methodology provides a clinically relevant approach for the early prediction of NH, supporting decision-making and enhancing the effectiveness of CGM-based monitoring systems. The proposed methodology may support timely preventive interventions, reducing the occurrence of NH while improving patient safety and confidence in diabetes management.

Abstract: Background and Objectives: Diabetes mellitus (Type 2 diabetes) is a significant and rapidly growing global healthcare challenge and a leading cause of severe complications, including cardiovascular disease (CVD), chronic kidney disease and other conditions. In Latvia more than 103 000 individuals have been registered in the Latvian Diabetes Patient Register, of whom 90% had type 2 diabetes. Since early diagnosis can reduce complication risk, considerable effort has been devoted to the development of numerous CVD risk prediction tools using traditional statistics as well as data science-based methods. This article augments these efforts with predictive models built specifically for Latvia’s diabetes patient population. Materials and Methods: Models used anonymized diabetes patient data stored at Latvia’s Center for Disease Prevention and Control containing passively collected information on patients’ utilization of publicly funded healthcare services. No clinical data was included. In accordance with best practices in model development, the 96 302 patient records were randomly partitioned into a 70% training data portion and 30% for out-of-sample validation. We tested three modeling methodologies—logistic regression, Random Forests, and XGBoost. Results: The binary dependent variable was defined as the occurrence of a CVD-related hospitalization during 2023 or 2024. The predictor variables were limited to data from the prior two year period, 2021-2022. The three model types all produced similar fit to the data—area under the Receiver Operating Curve (ROC) of 0,70-0,71 in the validation data, which is comparable to or better than results obtained in other studies, including SCORE2. Conclusions: We find CVD risk prediction models based on passively collected Latvian electronic health records to be feasible. However, they are primarily useful for predicting, not explaining outcomes. Our model identifies a group of 8 000—10 000 diabetes patients with an elevated (40%-45%) risk of experiencing a CVD-related hospitalization over the next two years. We recommend they be targeted for monitoring and proactive, preventative care.

Abstract: Several studies show that sleep deprivation (SD) reduces hippocampal long-term potentiation (LTP) by disrupting glutamatergic transmission. Reorganization of glutamate receptor complexes through post-translational modifications, mobility, and protein-protein interactions in the postsynaptic area is crucial for regulating synaptic plasticity. The cognitive impairment caused by SD may result from a temporary shift of components of the multiprotein complex from the synaptic to the extrasynaptic region, possibly linked to a short-term decrease in glutamatergic transmission. Our results indicate that the levels of three main scaffold proteins—PSD-95, Shank, and Homer—that associate with glutamatergic synaptic organization decrease in synaptic membranes and increase at extrasynaptic sites during SD. This redistribution is associated with increased attachment of NMDAR subunits, mGluR1, and D1 receptors to these scaffold proteins in extrasynaptic regions. Analysis of NMDAR subunits shows that NR2B increases more than NR2A, thus changing the NR2B/NR2A ratio in extrasynaptic membranes during SD. Furthermore, the heterocomplex containing NR1, PSD-95, and D1R accumulates at extrasynaptic membranes but remains stable at synaptic membranes. The distribution of PSD-95 transiently increases in extrasynaptic membranes during SD and decreases during recovery periods. Additionally, phosphorylation of Fyn tyrosine kinases correlates with the extrasynaptic movement of proteins during sleep deprivation. The redistribution of glutamatergic proteins in the hippocampal synaptic membrane likely accounts for the transient reduction in synaptic plasticity during SD.

Abstract: Academic opportunities are not equally visible to all researchers. Scholars working within well-resourced institutions may benefit from research offices, specialized newsletters, professional networks, institutional alerts, administrative support, and stable digital infrastructures. Researchers working in peripheral, resource-constrained, geographically remote, or low-connectivity environments may instead depend on fragmented searches across heterogeneous websites and intermittent flows of information. This produces what this paper defines as academic opportunity asymmetry: the unequal capacity of researchers to identify, interpret, and act upon career-relevant scientific opportunities. This Technical Note introduces KUAREN, a research-intelligence infrastructure designed to reduce such asymmetries through the organization, personalization, prioritization, and low-friction delivery of heterogeneous academic opportunities. KUAREN is not primarily conceived as a search engine. It operates as an informational intermediary between the increasing complexity of global science and the limited attention, connectivity, and monitoring capacities available to individual researchers. The system combines opportunity normalization, persistent identification, personalized matching, interaction history, multilingual access, and low-bandwidth dissemination. Particular attention is given to asynchronous email delivery, allowing relevant opportunities to reach researchers without requiring continuous platform access or permanent high-quality Internet connectivity. KUAREN follows four design principles for low-connectivity research environments: low interaction cost, low bandwidth, asynchronous accessibility, and information compression. Initial technical validation of an operational corpus containing 155 normalized opportunity records produced 155 unique persistent SHA-256 identifiers, with no detected identifier collisions. Persistence tests also confirmed continuity of opportunity identity and researcher-associated states following repeated processing. The paper adopts a controlled-disclosure model: the sociotechnical architecture, design principles, research problem, and validation results are presented, while implementation-sensitive mechanisms remain undisclosed. KUAREN is proposed as a form of low-bandwidth Research Intelligence capable of contributing to the reduction of informational inequalities within increasingly global but unevenly infrastructured scientific systems.

Abstract: Antibiofilm strategies targeting Candida glabrata increasingly focus on membrane-active compounds capable of disrupting biofilm formation and mature biofilm structure. Building on previous work identifying alkylamidobetaine C9 (AAB C9) - [(3-decanoylmethylamino)propyl]dimethylammonium acetate - and β-escin as antibiofilm agents against C. glabrata, this study examined the biochemical and physiological basis of their efficacy in a reference strain (ATCC 90030) and a clinical isolate (2586). Oxidative stress was assessed microscopically after two hours of exposure, and proteome changes were quantified after six hours, for each compound alone and in combination. C. glabrata cells retained membrane integrity under all treatment conditions, indicating that antibiofilm activity does not stem from membrane lysis. β-Escin induced the strongest mitochondrial and superoxide ROS response in the reference strain, whereas the AAB C9–β-escin combination was most effective in the clinical isolate, revealing strain-dependent sensitivity. Proteomic analysis identified altered abundance of proteins involved in protein quality control (FES1), transcriptional regulation (SPT4), DNA replication initiation (SLD3), and glucose transport (HXT10). Notably, β-escin reduced abundance of the virulence-associated β-mannosyltransferase, and AAB C9 reduced the multidrug transporter TPO1_1, linked to azole and antimicrobial peptide resistance. These findings suggest that AAB C9 and β-escin act through selective, strain-dependent remodeling of membrane-associated proteins coupled with oxidative and proteostatic stress, supporting their potential as antibiofilm and antifungal agents against C. glabrata.

Abstract: Environmental, Social and Governance frameworks are increasingly invoked in health policy, yet their three pillars are almost always studied in isolation and rarely against outcomes that primary care can influence. Using the World Bank Sovereign ESG Data Portal, this study decomposes the determinants of under-five mortality — the canonical primary-care-sensitive outcome and the target of SDG 3.2 — across the full ESG structure. Three equations share the same dependent variable and differ only in their regressor block: Environment (3,533 observations, 174 economies, 2000–2020), Social (2,032 observations, 99 economies, 2001–2023) and Governance (3,592 observations, 176 economies, 2000–2022). Each block is analysed three ways: panel econometrics comprising pooled OLS, one-way and two-way fixed effects, random effects, Between, WLS and dynamic GMM estimators; unsupervised taxonomy comparing six clustering algorithms across eleven internal validation indices; and supervised validation comparing six learners under both random and country-grouped cross-validation. Safely managed drinking water is the only predictor significant in all six static estimators including two-way fixed effects, with a ten-point coverage increase associated with 21% lower mortality within countries, while particulate exposure is robust on both the cross-country and within-country margins. Institutional quality becomes significant across all estimators only after the four Worldwide Governance Indicators, correlated between 0.80 and 0.94, are orthogonalised — a collinearity independently confirmed by unsupervised dimension reduction and by out-of-fold permutation importance. Within-country explanatory power ranks governance ahead of social and environmental blocks, while out-of-sample prediction ceilings run in the opposite order. Machine learning reproduces the econometric hierarchy, reveals a U-shaped renewable-energy relationship and a threshold in institutional capacity, and shows that random train-test splits overstate accuracy by up to 23 points of R2 through country memorisation. ESG determinants of child survival are pillar-specific in both magnitude and functional form: service access dominates, institutional quality matters above a capacity threshold, and environmental exposures act through structural conditions rather than year-on-year change.

Abstract: Translating adsorption performance from batch experiments to continuous-flow operation is a key step toward practical water-treatment applications. In this study, a laboratory-scale continuous adsorption system based on a water-insoluble β-cyclodextrin-epichlorohydrin (β-CD-EPI) polymer was validated from hydrodynamic, adsorptive, and operational perspectives. Downflow operation caused progressive bed compaction and excessive pressure development, whereas a 90 mm column operated in upflow expanded/fluidized-bed mode remained stable at superficial linear velocities below approximately 12 m h⁻¹. Under these conditions, removal was strongly contaminant-dependent: cyproconazole exceeded 90%, acetaminophen reached 72–77%, hydrochlorothiazide 40–65%, ciprofloxacin 24–50%, and furosemide remained below 30%. The relative performance of furosemide and hydrochlorothiazide differed from that predicted by previous batch-derived adsorption parameters, demonstrating that batch results cannot be directly extrapolated to dynamic operation. Competitive adsorption in binary and ternary mixtures reduced removal, while cyproconazole removal decreased from >90% in tap water to 48–55% in secondary-treated wastewater. Desorption with 220 mM acetate buffer at pH 4.0 recovered >80% of retained cyproconazole within 10 min, followed by a two-stage rinse restoring operational pH. These results define hydrodynamic, adsorption, matrix, and regeneration criteria for subsequent scale-up of β-CD-EPI adsorption processes.

Abstract: Background: The demographic crisis observed in industrialized countries has been accompanied by a decline in the reproductive potential of human populations. One of the causes for the global deterioration in men's reproductive health is adiposity and sedentary lifestyle, leading to reduced fertility and a decrease in androgen status. The aim of the study was to assess the association between abdominal obesity and semen parameters, including sperm DNA fragmentation, as well as key indicators of hormonal and metabolic status in young men from the general population. Methods: Our cross-sectional study was conducted among Russian and Belarusian men living in six cities. The examination of men (n=1283, median age 23 years) included questionnaires, anthropometry, collection of peripheral blood and semen samples. Semen analysis was conducted according to WHO guidelines (WHO, 2010; 2021). Sperm DNA fragmentation index (DFI) was evaluated using the SCSA method. The serum and seminal zinc concentration was determined using spectrophotometry and direct calorimetry. Reproductive hormonal and metabolic levels were measured by commercial kits. The waist circumference (WC) was used as a surrogate marker of adiposity. Participants were allocated to three phenotypes: non-obese (control, WC< 94 cm), excess abdominal fat (94≤WC< 102 cm) and abdominal obesity (WC≥102 cm). Results: In our study population, 85.8% of participants were young (under 30 years of age), 8.6% had excess abdominal fat, 5.9% - abdominal obesity (AO); 65.2% - normozoospermia. Men with AO were characterized by a decrease in semen volume, total sperm count, sperm concentration and progressive motility compared to the non-obese control. DFI was significantly higher in obese compared to non-obese men. Obese participants had significantly lower testosterone and inhibin B levels, but higher FSH and leptin levels compared to non-obese men. Men with AO had elevated serum zinc levels, but reduced seminal zinc content. Abdominal obesity was also associated with significant metabolic changes, in particular, with increased levels of triglycerides, total cholesterol, low-density lipoproteins, fasting glucose and uric acid. Obesity-related testosterone deficiency (serum testosterone level ≤12.1 nmol/L) was detected in 38.4% of obese men and significantly aggravated the negative impact of adiposity on DFI, FSH, testosterone, leptin levels, and the metabolic status. Physically active obese men (who engaged in recreational sports or physical labor) demonstrated reduced anthropometric indicators of obesity, increased testosterone levels, as well as improved carbohydrate and lipid metabolism compered to sedentary obese men. Conclusion: AO is accompanied by a decrease in the activity of Leydig and Sertoli cells, as well as an imbalance of the hypothalamic-pituitary-testicular axis and metabolic status. AO associated with testosterone deficiency or a sedentary lifestyle is a potential risk factor for weakening male reproductive health. However, its negative effects can be partially mitigated by moderate physical activity. In the long term, without lifestyle correction and with persistent abdominal obesity, obese men are not protected from infertility.

Abstract: A recently proposed local measurement axiom (Postulate M) replaces the instantaneous global projection of textbook quantum mechanics with branch selection triggered by completed local decoherence, while a companion ψ-ontic analysis identifies continuous spontaneous localization (CSL) as a concrete local mechanism for the selection itself. Here we test this two-stage account quantitatively in the Einstein–Podolsky–Rosen (EPR) scenario. We integrate the nonlinear CSL stochastic Schrödinger equation for an entangled spin pair in which one particle couples to a local spin bath with random couplings, bath self-dynamics, and intra-bath interactions, while the noise field and collapse operator act only at the measurement site. Analytically, we derive an exact law for the maximal Clauser–Horne–Shimony–Holt (CHSH) value under local decoherence, \( S_{\max}(t)=2\sqrt{1+\sin^{2}(2\theta)\,|z(t)|^{2}} \), with z(t) the decoherence factor and θ the entanglement angle: the CHSH excess over the classical bound is consumed, never the measurement-basis probabilities, which are exactly conserved. We further show that the selection stage is governed by an exact martingale: the branch weight obeys a scalar Itô equation whose derivation requires only the orthogonality of the branches—supplied by the spin structure, not by the environment—so the Born rule emerges as an absorption probability irrespective of the residual bath coherence. Numerically, the law holds to machine precision (∼ 10⁻¹⁵) for all three bath classes, including interacting environments, and through finite-size recurrences; stochastic trajectories confirm Born statistics to within sampling error, with all realizations fully resolved. The ensemble-averaged reduced state of the distant particle is invariant under the local collapse-operator axis and rate—parameter independence as a structural theorem, verified to machine precision in the unitary sector—and branch amplitudes are invariant across all environmental initial conditions and dynamics (∼ 10⁻¹¹), confirming that outcome randomness is not of environmental origin. In the symmetric configuration, with independent noises at both wings, the two local noises provably drive a single global branch martingale at the summed rate: selection is one joint event whose absorbing states are the correlated branches—mismatched outcomes are dynamically excluded (zero events in 300 realizations, against ∼ 25% each for a product-state control)—the joint statistics are invariant under swapping which wing completes first (93% vs 9% orderings, distributions equal within sampling error), and sampled outcomes reproduce the singlet’s measurement correlation E(β) = − cos β—with β the angle between Alice’s and Bob’s measurement axes—including a CHSH value |S| = 2.710±0.073, consistent with the Tsirelson bound 2√2 within sampling errors. Decoherence and local stochastic selection jointly reproduce the empirical content of quantum measurement in the EPR scenario with no nonlocal dynamical element: the only nonclassical resource is the nonseparability of the initial state. Nonlocal projection is thus dispensable not only axiomatically and ontologically but operationally.

Abstract: The global food system currently stands at a crossroads. On one hand, nearly 1.05 billion tonnes of food are wasted annually—roughly one-third of everything produced for human consumption—while on the other hand, this very waste stream represents one of the most underutilized reservoirs of valuable biological resources on the planet. This paradox has given rise to the field of food waste valorization, a discipline that seeks to transform what was once considered refuse into a spectrum of high-value products. The present review article offers a comprehensive, critically assessed synthesis of the current state of knowledge in this rapidly evolving field. I begin by quantifying the magnitude of the global food waste challenge, drawing on the most recent data from the United Nations Environment Programme and the Food and Agriculture Organization, which collectively underscore the environmental, economic, and ethical imperatives for action. From there, I undertake a detailed characterization of the major categories of agro-industrial by-products—those arising from fruit and vegetable processing, cereal milling and brewing, dairy manufacturing, and seafood processing—highlighting their compositional richness and the specific bioactive molecules they harbor. The review then provides an in-depth examination of the advanced green extraction technologies that have been developed to recover these compounds efficiently and sustainably, including ultrasound-assisted, microwave-assisted, supercritical fluid, enzyme-assisted, and pressurized liquid extraction, as well as emerging approaches such as pulsed electric field processing and natural deep eutectic solvents. I subsequently explore the biological conversion routes—anaerobic digestion, fermentation for platform chemicals, bioplastics production, enzyme fermentation, and single-cell protein cultivation—and the thermochemical technologies—pyrolysis, hydrothermal carbonization, and gasification—that together form the technological backbone of the biorefinery concept. Four original tables synthesize data on waste types and valorization pathways, extraction technology comparisons, biological conversion routes and yields, and the market landscape for value-added products. Three original figures provide visual frameworks for understanding the integrated biorefinery, green extraction technologies, and the value-added product cascade. I critically assess the techno-economic feasibility and life cycle environmental performance of these approaches, identify the persistent challenges that impede widespread commercialization, and cast a forward-looking gaze toward the emerging trends—artificial intelligence, nanotechnology, synthetic biology, and supportive policy frameworks—that are poised to accelerate the transition to a circular bioeconomy. Throughout, our aim is to provide not merely a catalog of technologies and products, but a human-centered narrative that connects scientific innovation with the urgent societal need for a more sustainable, equitable, and resilient food system.