Search Results (3,431)

Background: Inborn errors of metabolism (IEMs) represent a diverse group of genetic disorders affecting biochemical pathways. Despite advances in diagnostic technologies, comprehensive understanding of their historical evolution, classification systems, and diagnostic approaches remains fragmented. Objectives: This systematic review and meta-analysis aimed to synthesize evidence on the historical development, classification frameworks, and diagnostic modalities for IEMs, diagnostic accuracy, and prevalence estimates, providing a comprehensive resource for clinicians and researchers. Methods: Following PRISMA 2020 guidelines, we conducted a systematic search of seven electronic databases (PubMed/MEDLINE, Embase, Scopus, Web of Science, Google Scholar, SciSpace and ArXiv) from January 2000 to March 2026. Studies addressing historical perspectives, classification systems, or diagnostic approaches for IEMs were included. Two independent reviewers performed screening, data extraction, and quality assessment. Meta-analyses were conducted using random-effects models for diagnostic accuracy and prevalence estimates. Results: From 1342 identified records, 54 studies met the inclusion criteria, encompassing 8,234,567 individuals across 35 countries. Historical analysis revealed 16 major milestones from Garrod’s 1902 “chemical individuality” concept to the current AI-powered diagnostics. Four major classification systems were identified: pathophysiological (intoxication, energy deficiency, complex molecule disorders), biochemical pathway (amino acid, organic acid, urea cycle, carbohydrate, fatty acid oxidation, mitochondrial, peroxisomal, lysosomal disorders), organelle-based, and the integrated Society for the Study of Inborn Errors of Metabolism (SSIEM) nosology. Meta-analysis demonstrated high diagnostic performance of tandem mass spectrometry (MS/MS) with a pooled sensitivity of 99.1% (95% CI: 98.6–99.5) and specificity of 99.8% (95% CI: 99.7–99.9%). The pooled global prevalence of IEMs was 50.9 per 100,000 live births (95% CI 45.2–56.8). Next-generation sequencing achieved a diagnostic yield of 42.8% (95% CI: 38.2–47.5%) in suspected cases. Emerging AI-powered diagnostic tools demonstrated high discrimination performance with area under the curve (AUC) values exceeding 0.95 for specific IEM, though external validation remains limited. Newborn screening expanded from single-disease to comprehensive panels detecting over 50 disorders. Conclusions: This comprehensive review demonstrates that IEMs have evolved from rare curiosities to systematically diagnosable conditions through technological advances. Integration of metabolomics, genomics, proteomics and artificial intelligence promises further diagnostic improvements. Standardized classification systems and evidence-based diagnostic algorithms are essential for optimal patient care. Future directions include artificial intelligence-enhanced diagnostics, expanded screening, and personalized medicine approaches. Full article

This review analyses and logically structures modern intelligent sensor technologies in the context of animal husbandry, feed production, and veterinary medicine. The main research discussed in the article focuses on machine learning based on modern neural network models, computer vision, and sensor systems that are transforming the methods for assessing the health, behavior, and nutrition of farm animals. The first part examines modern approaches to quality control and optimization of mineral and vitamin premixes, including visual inspection using visual sensors and neural networks. Key roles are played by precise dosing, component stability (minerals, vitamins), and the transition to more bioefficient organic forms of micronutrients to reduce environmental impact. Improvements in feed and premix production are analyzed, including automation, energy management, and the use of machine learning for non-destructive quality control, defect detection, mixing homogeneity assessment, and vitamin stability prediction. The second part analyzes methods for animal location and behavior detection. This article presents computer vision-based systems, including modifications of YOLO, for automatically tracking and classifying key behavioral patterns (lying down, standing, feeding, and aggression) in cattle and pigs, even in crowded conditions. It also discusses the use of ultra-wideband (UWB) systems and accelerometers combined with machine learning for high-precision positioning and detection of specific behavioral anomalies, such as lameness and playfulness. The third section focuses on the application of machine learning in veterinary diagnostics, including the automated interpretation of medical images (X-ray, ultrasound, and MRI) as sensor data streams for the diagnosis of cardiovascular, oncological, and orthopedic diseases in farm and small animals. Furthermore, the article examines the use of machine learning models for proactive disease diagnosis in farm animals and poultry based on multimodal data and image analysis. Considerable attention is given to methods and tools for radiometric diagnosis of animal diseases at an early stage using microwave sensors, as well as laser therapy and surgery in veterinary medicine. The review concludes that the integration of intelligent systems enables a transition to data-driven livestock management, significantly improving animal welfare and, consequently, the efficiency and sustainability of agricultural production. Full article

Background: Buddleja globosa Hope (matico) is a Chilean medicinal plant traditionally used in Mapuche and Aymara ethnomedicine. However, no systematic synthesis of its phytochemical composition and pharmacological evidence has been previously reported. Methods: A PRISMA 2020-compliant systematic review was conducted using Google Scholar, PubMed, EBSCOhost, and Springer Nature databases from inception to March 2026. Studies reporting phytochemical characterization and/or biological activities of B. globosa were included. Methodological quality was assessed using an adapted five-criterion tool for non-clinical studies. The protocol was registered in OSF. Results: Fourteen studies (1989–2026), mainly from Chilean research groups, identified 27 bioactive compounds across leaves, roots, and flowers. These included phenylethanoid glycosides (e.g., verbascoside/acteoside, echinacoside, forsitoside B, and linarin), flavonoids (luteolin 7-O-glucoside, apigenin 7-O-glucoside, myricetin, catechin, and epicatechin), pentacyclic triterpenes (α/β-amyrins and β-sitosterol), iridoid glycosides, and clerodane diterpenoids (buddledines A–C), as well as four newly reported phenylethanoids. Antioxidant activity was the most frequently evaluated endpoint (11/14 studies), mainly mediated through hydrogen atom transfer and single-electron transfer mechanisms linked to caffeoyl and flavonoid structures. Anti-inflammatory effects (five studies) involved COX and 5-LOX inhibition and reduced PGE₂ production in LPS-stimulated macrophages. Additional reported activities included antihepatotoxic, antiplatelet, wound-healing, antibacterial, and antifungal effects. Conclusions:B. globosa exhibits a coherent phytochemical profile supporting strong preclinical antioxidant and anti-inflammatory activities. The main limitation for clinical translation is the low oral bioavailability of phenylethanoid glycosides. Nanoformulation strategies, investigation of colonic metabolites, and topical delivery systems represent promising approaches to bridge the preclinical-to-clinical gap. Full article

Reverse transcription quantitative real-time PCR (RT-qPCR) is a highly efficient and sensitive technique for quantifying gene transcript levels. The accuracy of gene expression analysis depends critically on the selection of appropriate reference genes for normalization, which is essential to minimize technical variation arising from differences in RNA quality, reverse transcription efficiency, and sample handling. Schisandra chinensis is a medicinally important plant with a long history of use in traditional Chinese medicine and has gained increasing global recognition. In recent years, a growing number of studies have employed molecular biology approaches to investigate the molecular mechanisms underlying secondary metabolite biosynthesis in S. chinensis. However, systematically validated reference genes for RT-qPCR analysis in this species have not yet been established. In the present study, the expression stability of eleven candidate reference genes was evaluated across different tissues and under various abiotic stress conditions in S. chinensis using four statistical algorithms: geNorm, NormFinder, BestKeeper, and RefFinder. Comprehensive analysis revealed that PP2A15 and UBC2 were the optimal reference gene combination for leaves; UBC2 and UBC11 for stems; RPL6 and PP2A15 for roots; RPL21 and RPL6 for fruits; and RPL6 and UBC11 as the best-performing pair across all tissue types. Under abiotic stress conditions, UBC11 and UBC2 exhibited the highest stability in both leaves and roots under salt stress; UBC2 and GPN1 proved most stable under alkaline stress; UBC2 and RPL6 were identified as the most suitable combination under drought stress; and UBC2 and UBQ12 demonstrated consistently stable expression across all three abiotic stress treatments. The reliability of these reference gene combinations was further validated by examining the expression profiles of three target genes. Collectively, these findings establish a validated reference gene toolkit for future gene expression studies in S. chinensis, particularly for the functional characterization of genes involved in lignan biosynthesis and abiotic stress responses. Full article

Background/Objectives: Artificial Intelligence (AI) integration in healthcare promises improved diagnostic accuracy, patient safety, and operational efficiency. However, AI acceptance among healthcare workers remains limited due to knowledge gaps, risk concerns, and governance challenges, particularly in developing countries like Saudi Arabia, where rapid healthcare modernization faces unique infrastructure, organizational, and cultural challenges. This research investigates the factors influencing AI acceptance among medical practitioners, nurses, administrators, and students in Saudi Arabian hospitals to identify key determinants and barriers to adoption. Methods: This cross-sectional study employed an extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework integrated with ethical considerations from the Model for Ethical Assessment and Analysis of AI in Medicine (MEAAM). A structured bilingual questionnaire was administered to 119 healthcare professionals and students across Saudi Arabia, measuring constructs including Awareness and Knowledge, Performance Expectancy, Effort Expectancy, Facilitating Conditions, Social Influence, Trust, Perceived Risk, Ethical Governance, and Price Value. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed for quantitative analysis, supplemented by thematic analysis of open-ended qualitative responses. Results: The PLS-SEM analysis explained 59.8% of variance in behavioral intention to adopt AI (R² = 0.598). Awareness and Knowledge emerged as the strongest predictor (β = +0.505, p < 0.001), followed by Performance Expectancy (β = +0.229, p < 0.05) and Social Influence (β = +0.123). Perceived Risk functioned as the primary barrier (β = −0.185, p < 0.05). Qualitative findings identified infrastructure gaps, regulatory ambiguities, and training deficiencies as major implementation barriers, while emphasizing opportunities in diagnostic accuracy and remote monitoring. Conclusions: AI acceptance in Saudi healthcare is primarily driven by knowledge, with perceived usefulness and peer support as secondary facilitators, while safety and accountability concerns remain substantial obstacles. Successful AI integration requires coordinated efforts in education, transparent governance frameworks, and institutional support. This study contributes theoretically by validating extended UTAUT in a non-Western healthcare context and practically by providing evidence-based strategies for sustainable AI adoption that enhance healthcare quality while respecting professional roles and ethical principles. Full article

Background: Recovery after major head-and-neck reconstruction extends beyond flap survival and wound closure, involving swallowing, psychological adaptation, body image, and overall quality of life. Integrated multidimensional assessments remain limited in routine reconstructive outcomes research. Aim: The aim of this study was to characterize and compare six-month multidimensional recovery—clinical, functional, nutritional, psychological, and body-image outcomes—between microvascular free-flap and regional pedicled-flap reconstruction and to identify factors that stratify risk for persistent functional and psychosocial impairment. Methods: We conducted a single-center prospective cohort study at the “Victor Babeș” University of Medicine and Pharmacy, Timișoara, Romania, enrolling 87 adults undergoing major reconstructive surgery after ablative treatment of head-and-neck defects (52 microvascular free flaps; 35 regional pedicled flaps). Patients were assessed at baseline and 6 months using the SF-36, WHOQOL-BREF, Body Image Scale (BIS), HADS, PHQ-9, GAD-7, Functional Oral Intake Scale (FOIS), speech intelligibility, and PEG/tracheostomy dependence. Results: At 6 months, most SF-36 and WHOQOL-BREF domains improved with moderate effect sizes (d = 0.3–0.7; all p ≤ 0.009), and body image distress decreased significantly (ΔBIS −2.9 ± 4.6; p < 0.001), whereas social functioning showed no robust gain (p = 0.098; not surviving false-discovery-rate correction). Pedicled reconstruction was associated with higher PEG dependence (37.1% vs. 9.6%; p = 0.005) and worse FOIS (4.7 ± 1.4 vs. 5.6 ± 1.2; p = 0.003). Major complications were linked to blunted or worsening psychological trajectories and a threefold higher rate of clinically significant depression (HADS-D ≥ 11: 66.7% vs. 18.7%; p = 0.001). In a reduced four-predictor multivariable model, pedicled flap (aOR 4.6), adjuvant radiotherapy (aOR 2.8), major complication (aOR 3.3), and lower baseline FOIS (aOR 0.5 per point) were independently associated with PEG dependence (optimism-corrected AUC 0.79). Clustering identified three recovery phenotypes—functional/emotional responders, psychological/body-image responders, and global slow recovery—with significantly different PEG rates (5.9%, 21.4%, 40.0%; p = 0.006). Exploratory mediation analysis suggested that the association between reconstruction technique and mental quality-of-life recovery was partly statistically accounted for by swallowing and body-image improvement. Conclusions: Recovery after major head-and-neck reconstruction is multidimensional and heterogeneous. Baseline swallowing function, reconstruction technique, radiotherapy, and major complications jointly stratify risk for persistent functional and psychosocial impairment, supporting risk-adapted multidisciplinary rehabilitation and early psycho-oncologic screening. Full article

Background/Objectives: Absent septum pellucidum (ASP) is a rare fetal midline brain finding that may occur in isolation or alongside broader central nervous system (CNS) malformations, genetic disorders, or septo-optic dysplasia (SOD). Accurate prenatal diagnosis and counseling remain challenging because apparently isolated ASP may be reclassified following fetal magnetic resonance imaging (MRI), postnatal neuroimaging, or specialist assessment. This structured narrative review aimed to synthesize current evidence on prenatal imaging findings, associated anomalies, genetic evaluation, and postnatal outcomes in fetuses with ASP. Methods: This structured narrative review used PRISMA-informed reporting. PubMed and Google Scholar were searched for full-text English-language studies published from 2014 through the updated search date (8 June 2026). Data on gestational age at diagnosis, imaging classification, associated anomalies, genetic testing, postnatal assessment, and neurodevelopmental, ophthalmological, and endocrine outcomes were extracted. Study methodological quality was appraised using Joanna Briggs Institute tools. Results: Seven studies comprising 342 fetal ASP cases were included. Of these, 94 cases (27.5%) were classified as isolated ASP prenatally, but only 57 remained isolated postnatally when follow-up data were available. SOD was confirmed after birth in 11 of 94 (11.7%) fetuses with prenatally isolated ASP. As definitions, imaging protocols, genetic testing strategies, and follow-up duration differed substantially across studies, these pooled values are descriptive observations rather than formal quantitative estimates. Conclusions: ASP is a heterogeneous prenatal finding. The prognosis is most favorable when ASP remains isolated following a detailed prenatal and postnatal evaluation. Multidisciplinary follow-up involving fetal medicine, neuroradiology, genetics, ophthalmology, endocrinology, and neurology is essential for risk stratification and counseling. Full article

Background: Insomnia and stress-related sleep disorders are increasingly recognized as systemic conditions linked to the microbiota–gut–brain axis (MGBA). With growing clinical interest in natural products that modulate the gut environment, this systematic review evaluates the efficacy and mechanisms of non-pharmacological interventions, specifically probiotics, prebiotics, dietary indices, and botanicals, in alleviating insomnia, restoring circadian rhythms, and modulating neurochemical markers. Methods: In strict accordance with PRISMA 2020 guidelines, we searched PubMed, ScienceDirect, Scopus, and The Cochrane Library for English language studies published from inception to March 31, 2026. Eligibility was restricted to studies with rigorously controlled designs, specifically randomized controlled trials (RCTs) and controlled in vivo animal studies. Interventions had to target the gut microbiota, with primary outcomes measuring sleep quality (subjective or objective) or sleep-related neurochemical markers. We excluded uncontrolled, single-arm, or observational designs; in vitro studies; non-original research; and studies involving subjects with severe medical or psychiatric comorbidities (e.g., cancer, ADHD, severe psychiatric disorders) to prevent confounding variables, though mild-to-moderate anxiety and depression were permitted. Risk of bias was assessed using the Cochrane RoB 2.0 and SYRCLE tools. Due to significant methodological heterogeneity, a narrative synthesis stratified by intervention and population was conducted. This review was not registered in PROSPERO. Results: A total of 56 studies (33 humans, 23 animals) met the inclusion criteria. Taxonomic nomenclature was updated to reflect 2020 reclassifications (e.g., Lactiplantibacillus plantarum). In human trials, interventions significantly improved subjective sleep metrics (PSQI, ISI). Recent additions demonstrated the efficacy of the Dietary Index for Gut Microbiota (DI-GM) and the improvement in N3 sleep latency by yeast mannan. Furthermore, whole-food patterns (e.g., the MIND diet) and Traditional Chinese Medicine (TCM) decoctions successfully enriched beneficial taxa, such as Bacteroides coprophilus, and increased short-chain fatty acid (SCFA) production. Animal models demonstrated that “psychobiotic” strains (Bifidobacterium breve, Lacticaseibacillus paracasei), prebiotics (GOS/PDX), and TCM formulas effectively restored GABA/5-HT profiles, lowered morning cortisol, and facilitated REM rebound in PCPA-induced models, while also consolidating non-rapid eye movement (NREM) sleep and downregulating clock genes (Per1/Per2). Conclusions: Psychobiotics, prebiotics, and botanicals represent a highly viable non-pharmacological strategy for treating insomnia. However, current evidence is constrained by a heavy reliance on subjective human questionnaires, short follow-up durations limiting insight into long-term stability, and a substantial translational gap between mechanistic rodent models and human clinical outcomes. Full article

This review aims to evaluate the potential of endometriosis models, especially patient-derived iPSC models, to gain deeper insights into the disease, thereby advancing our understanding and treatment of endometriosis. This comprehensive narrative review utilized a structured search of the PubMed, Scopus, and Web of Science databases, primarily covering literature published between January 2000 and May 2025. An expansive search strategy was employed to capture the full breadth of the field using keywords such as “endometriosis,” “induced pluripotent stem cells (iPSCs),” “patient-derived organoids,” “disease modeling,” and “epigenetics” without restrictive filtering, ensuring the integration of both foundational theories and emerging biotechnological advances. In total, over 170 peer-reviewed publications were analyzed, ranging from landmark genomic meta-analyses that have identified significant risk loci to state-of-the-art 3D-culture systems for modeling patient-specific endometrial disease. By synthesizing these diverse sources, the review bridges the gap between traditional anatomical classifications and modern molecular modeling to evaluate the potential of iPSC platforms for personalized medicine and therapeutic discovery. Endometriosis is a multifactorial gynecological condition that affects 176 million women worldwide and can significantly impair quality of life. It occurs when endometrium-like tissue grows outside the uterus, responsive to ovarian hormones, causing inflammation, pain, and discomfort, and leading to fibrotic tissue. World Health Organization estimates indicate that 6–10% of women suffer from this disorder, which can cause infertility and increase the risk of developing various types of cancer and autoimmune disorders. The use of patient-derived iPSC models serves to gain deeper insights into the disease by mimicking the endometrial tissue or lesions observed in affected individuals, thereby advancing our understanding and treatment of endometriosis. Full article

Red clover (Trifolium pratense L.) is an important forage legume that is also a valuable source of bioactive compounds with potential health-promoting properties. This study evaluated the variability among diploid (2n) and tetraploid (4n) red clover cultivars in forage productivity, quality-related parameters, polyphenol and flavonoid content, and antioxidant activity, in order to identify promising ideotypes for dual-purpose breeding. A total of 90 cultivars were assessed under field conditions; green matter yield, dry matter yield, crude protein content, and protein yield were analyzed together with total polyphenols, total flavonoids, and antioxidant activity. Spearman correlation and principal component analysis (PCA) were used to relate the traits and identify cultivars with contrasting characteristics. Cultivar differentiation was pronounced within each ploidy group, whereas diploid and tetraploid cultivars overlapped substantially in the multivariate space, indicating that ploidy alone is not a reliable predictor of forage or medicinal value. At the group level, tetraploids tended toward higher biomass, protein-related traits, and total polyphenol concentration, while total flavonoids and antioxidant activity were broadly comparable between groups. Forage- and medicinal-related traits were only weakly correlated and thus behaved as largely independent selection targets—which is precisely why integrated multi-trait evaluation is required to identify cultivars combining both. Several cultivars did combine favorable agronomic and phytochemical characteristics, supporting within-group selection of red clover germplasm with dual forage and medicinal potential for sustainable agricultural systems. Full article

Background: Cancer pain affects approximately 44.5% of all patients with malignancy and up to 55–65% of those with advanced or metastatic disease; a substantial proportion remain inadequately controlled with conventional pharmacological approaches alone. Peripheral nerve stimulation (PNS), a minimally invasive neuromodulatory strategy, has emerged as a potential opioid-sparing analgesic option for the perioperative management of oncologic surgical patients. Objectives: This narrative review synthesizes current evidence on the application, mechanisms, clinical efficacy, safety, and integration of temporary and permanent PNS systems in cancer patients, with specific focus on cancer-specific pain syndromes, key clinical studies, opioid-sparing immunological implications, evidence quality, and directions for future research. Methods: As a narrative review, this work was structured in accordance with the Scale for the Assessment of Narrative Review Articles (SANRA) to ensure methodological transparency. A focused, non-systematic literature search of PubMed/MEDLINE, Embase, and the Cochrane Library was performed from database inception through March 2026, supplemented by hand-searching of reference lists and targeted retrieval of clinical practice guidelines. Sources were selected on the basis of relevance to PNS or closely analogous peripheral neurostimulation modalities in oncologic, perioperative, or chronic pain contexts. Evidence was synthesized narratively, with each cited study graded using the Oxford Centre for Evidence-Based Medicine (OCEBM) 2011 Levels of Evidence framework to enable transparent calibration of confidence. Results: Available preliminary and largely extrapolated evidence supports PNS as a promising but not yet established useful adjunct in oncologic perioperative care; because cancer-specific data rest substantially on a single pilot study (n = 12), one retrospective review (n = 15), and extrapolation from non-cancer populations, these conclusions should be regarded as hypothesis-generating. Randomized controlled trial data from non-cancer cohorts demonstrate opioid consumption reductions of approximately 80–90% in the PAINfRE trial, while the post-amputation trial demonstrated ≥50% pain-relief responder rates and reductions in pain interference, with clinically meaningful improvements in pain and function. Oncologic-specific pilot and retrospective evidence confirms feasibility and a 58–67% success rate across diverse cancer pain subtypes. Conclusions: The opioid-sparing properties of PNS carry additional biological plausibility for preserving perioperative antitumor immune function. High-quality prospective trials specifically designed for oncologic surgical populations remain needed to establish evidence-based recommendations. Full article

Ocimum species (family Lamiaceae) are among the most extensively utilized medicinal plants across low- and middle-income countries (LMICs), yet their pharmacological evidence base has not been comprehensively synthesized within an LMIC healthcare framework. A systematic review was conducted following PRISMA 2020 guidelines and a prospectively registered protocol (PROSPERO). Five electronic databases, PubMed, Scopus, Embase, Web of Science, and Google Scholar, were searched from January 2010 to December 2025. Studies reporting ethnobotanical, phytochemical, or pharmacological data on any Ocimum species were eligible. The study selection, quality assessment and data extraction were done by two independent reviewers utilizing Rayyan software. Findings were synthesized using a narrative approach. Ninety-seven studies were included. O. basilicum, O. tenuiflorum, and O. gratissimum were most studied. Key bioactive constituents rosmarinic acid, eugenol, linalool, β-caryophyllene, and ursolic acid, demonstrated consistent antimicrobial [minimum inhibitory concentration (MIC): 0.31–1.25 mg/mL], antioxidant [2,2-diphenyl-1-picrylhydrazyl (DPPH) IC₅₀: 12.5–89.3 µg/mL], anti-inflammatory (35–55% edema reduction), and antidiabetic (α-glucosidase IC₅₀: 0.3–1.5 mg/mL) activities. Larvicidal efficacy exceeding 90% against Anopheles spp. was demonstrated in field trials. The safety profile was broadly favorable (LD₅₀ > 5000 mg/kg). Ocimum species represent a pharmacologically credible and preclinically well-supported botanical resource with practical relevance for LMIC health systems, particularly in antimicrobial, antidiabetic, anti-inflammatory, and vector-control applications. To realize their therapeutic potential, future research must prioritize LMIC-contextualized randomized controlled trials, standardized phytochemical reporting, and chemotype-aware product development. Full article

Class imbalance in datasets is a common issue across various fields, including banking, medicine, and information security. Data augmentation is a frequently used approach to address this problem by generating additional samples of the minority class to rebalance the dataset. Other studies have employed methods such as Generative Adversarial Networks (GAN) and Synthetic Minority Over-sampling Technique (SMOTE)for this purpose. Therefore, this study aims to compare the differences between the two oversampling techniques, GAN and SMOTE, in handling class imbalance problems. The results of this study show the accuracy of distinguishing between real and generated data to determine which method offers a greater advantage. The method demonstrates better performance in multi-class classification tasks. The GAN model can be effectively applied to both binary classification and the generation of diverse samples from minority and majority classes, even in extreme cases where the number of minority samples is tiny. Moreover, in terms of classification accuracy and the quality of generated samples, GAN outperforms SMOTE in data augmentation and oversampling. It maintains strong performance even when the number of instances in the minority class is limited. Full article

Alfalfa (Medicago sativa L.) is widely recognized as a major forage crop, yet its role as a multifunctional biological input in sustainable horticultural production remains underexplored. This review evaluates alfalfa as a biological nitrogen source, organic fertilization resource, and biofertilizer-supporting crop within vegetable, medicinal, and perennial horticultural systems. Due to its high capacity for biological nitrogen fixation, alfalfa can supply substantial amounts of plant-available nitrogen, reducing dependency on synthetic fertilizers and supporting environmentally sound nutrient management. When used as green manure, cover crop, intercrop, mulch source, compost feedstock, or processed organic fertilizer, alfalfa enhances the soil organic carbon (SOC), improves soil structure, and increases the water-holding capacity properties particularly critical in intensive horticultural production. Higher SOC levels also contribute to the improved tolerance of horticultural crops to drought and heat stress through enhanced soil moisture retention and rhizosphere buffering. Alfalfa-based organic inputs stimulate rhizosphere microbial biomass, enzymatic activity, and functional genes associated with nitrogen cycling, strengthening plant–microbe interactions that underpin biofertilizer effectiveness. Evidence from vegetable and perennial systems indicates that alfalfa-derived amendments and rotations increase soil nitrogen availability, support yield stability, and improve soil health over the long-term. In orchards and vineyards, alfalfa cover cropping contributes to carbon sequestration, erosion control, and enhanced soil biological functioning. Overall, alfalfa emerges as a strategic species for integrating organic fertilization and biofertilizer-based approaches into modern horticultural systems, supporting reduced mineral fertilizer inputs while sustaining productivity, soil health, and environmental quality. Full article

Background: Type II workplace violence by patients, relatives, or visitors is an occupational health and patient-safety concern in primary care. Cranston Ridge Medical Clinic (CRMC), a single urban family medicine and walk-in primary care clinic in Calgary, Alberta, plans to implement a universal, risk-informed workplace-safety bundle that is based on observable behaviour, situational risk, and documented safety concerns rather than demographic profiling. Methods: This article describes a single-site internal quality improvement and workplace-safety evaluation protocol. The comparison is CRMC usual practice during the pre-implementation baseline period; there is no concurrent external control group. The planned evaluation will use aggregate, de-identified operational data from a 12-month pre-implementation baseline, a four-week implementation period, and 12 months of post-implementation monitoring. All clinic staff will receive workplace-safety training as part of routine implementation. No staff, patients, or visitors will be recruited as research participants, and the evaluation will not use individual-level staff survey, interview, or focus-group data. Patient/visitor information will be used only as aggregate operational monitoring data when needed to assess safety, access, patient flow, and complaints. Intervention and analysis: The bundle includes worksite analysis, staff training, a brief arrival safety screen, a response algorithm, standardized reporting, monthly safety huddles, and post-incident support. The primary metric will be the Type II workplace-violence incident rate per 1000 clinic visits. Planned analyses include run charts, pre–post rate ratios, and Poisson or negative binomial segmented regression if monthly counts are sufficient. Implementation learning will be summarized from routine training records, safety-huddle summaries, post-incident debrief themes, and other aggregate de-identified operational indicators. Expected contribution: The protocol contributes a transparent, equity-sensitive, and operationally feasible model for balancing staff safety with patient access in primary care. Full article