Search Results (892)

Schizophrenia (SCZ) is a complex psychiatric disorder with heterogeneous molecular underpinnings that remain poorly resolved by conventional single-omics approaches, limiting biomarker discovery and mechanistic insights. To address this gap, we applied an artificial intelligence (AI)-driven multi-omics framework to an open access dataset comprising plasma proteomics, post-translational modifications (PTMs), and metabolomics to systematically dissect SCZ pathophysiology. In a cohort of 104 individuals, comparative analysis of 17 machine learning models revealed that multi-omics integration significantly enhanced classification performance, reaching a maximum AUC of 0.9727 (95% CI: 0.8889–1.000) using LightGBMXT, compared to 0.9636 (95% CI: 0.8636–1.0000) with CNNBiLSTM for proteomics alone. Interpretable feature prioritization identified carbamylation at immunoglobulin-constant region sites IGKC_K20 and IGHG1_K8, alongside oxidation of coagulation factor F10 at residue M8, as key discriminative molecular events. Functional analyses identified significantly enriched pathways including complement activation, platelet signaling, and gut microbiota-associated metabolism. Protein interaction networks further implicated coagulation factors F2, F10, and PLG, as well as complement regulators CFI and C9, as central molecular hubs. The clustering of these molecules highlights a potential axis linking immune activation, blood coagulation, and tissue homeostasis, biological domains increasingly recognized in psychiatric disorders. These results implicate immune–thrombotic dysregulation as a critical component of SCZ pathology, with PTMs of immune proteins serving as quantifiable disease indicators. Our work delineates a robust computational strategy for multi-omics integration into psychiatric research, offering biomarker candidates that warrant further validation for diagnostic and therapeutic applications. Full article

Background/Objectives: COVID-19 is a systemic viral infection that may lead to serious complications including acute kidney injury that requires kidney replacement therapy. The primary aim of this study was to evaluate urinary SerpinA3 (uSerpinA3) excretion as a biomarker of kidney recovery at 90 days, and the mortality in patients with critical COVID-19 and AKI requiring kidney replacement therapy (KRT). Methods: The study included patients with critical COVID-19 on invasive mechanical ventilation (IMV) requiring KRT. Blood and urine samples were obtained when KRT was initiated (day zero), and thereafter on days 1, 3, 7, and 14 post-replacement. uSerpinA3, kidney injury molecule-1 (uKIM-1), and neutrophil gelatinase-associated lipocalin (uNGAL) were measured in urine, and interleukin-6 (IL-6), interleukin-10 (IL-10), and tumor necrosis factor alpha (TNF-α) in peripheral blood. In addition, metabolomics in sample days zero and 3, and in the survivors on sample day 90 was performed by employing gas chromatography coupled with mass spectrometry. Results: A total of 60 patients were recruited, of whom 29 (48%) survived hospitalization and recovered kidney function by day 90. In the survivors, 79% presented complete recovery (CRR) and the remaining (21%) recovered partially (PRR). In terms of uSerpinA3, levels on days 7 and 14 predicted CRR, with AUC values of 0.68 (p = 0.041) and 0.71 (p = 0.030), respectively, as well as mortality, with AUC values of 0.75 (p = 0.007) and 0.76 (p = 0.015), respectively. Among the other biomarkers, the excretion of uKIM-1 on day zero of KRT had a superior performance as a CRR predictor [(AUC, 0.71 (p = 0.017)], and as a mortality predictor [AUC, 0.68 (p = 0.028)]. In the metabolomics analysis, we identified four distinct profiles; the metabolite that maintained statistical significance in predicting mortality was p-cresol glucuronide. Conclusions: This study strongly suggests that uSerpinA3 and uKIM-1 can predict CRR and mortality in patients with critical COVID-19 and AKI requiring KRT. Metabolic analysis appears promising for identifying affected pathways and their clinical impact in this population. Full article

Background/Objective: Pharmacological interventions often exert systemic effects beyond their primary targets, underscoring the need for a comprehensive evaluation of their metabolic impact. Etodolac is a nonsteroidal anti-inflammatory drug (NSAID) that alleviates pain, fever, and inflammation by inhibiting cyclooxygenase-2 (COX-2), thereby reducing prostaglandin synthesis. While its pharmacological effects are well known, the broader metabolic impact and potential mechanisms underlying improved clinical outcomes remain underexplored. Untargeted metabolomics, which profiles the metabolome without prior selection, is an emerging tool in clinical pharmacology for elucidating drug-induced metabolic changes. In this study, untargeted metabolomics was applied to investigate metabolic changes following a single oral dose of etodolac in healthy male volunteers. By analyzing serial blood samples over time, we identified endogenous metabolites whose concentrations were positively or inversely associated with the drug’s plasma levels. This approach provides a window into both therapeutic pathways and potential off-target effects, offering a promising strategy for early-stage drug evaluation and multi-target discovery using minimal human exposure. Methods: Thirty healthy participants received a 400 mg dose of Etodolac. Plasma samples were collected at five time points: pre-dose, before Cmax, at Cmax, after Cmax, and 36 h post-dose (n = 150). Samples underwent LC/MS-based untargeted metabolomics profiling and pharmacokinetic analysis. A total of 997 metabolites were significantly dysregulated between the pre-dose and Cmax time points, with 875 upregulated and 122 downregulated. Among these, 80 human endogenous metabolites were identified as being influenced by Etodolac. Results: A total of 17 metabolites exhibited time-dependent changes closely aligned with Etodolac’s pharmacokinetic profile, while 27 displayed inverse trends. Conclusions: Etodolac influences various metabolic pathways, including arachidonic acid metabolism, sphingolipid metabolism, and the biosynthesis of unsaturated fatty acids. These selective metabolic alterations complement its COX-2 inhibition and may contribute to its anti-inflammatory effects. This study provides new insights into Etodolac’s metabolic impact under healthy conditions and may inform future therapeutic strategies targeting inflammation. Full article

(1) Background: Epidemiological studies link ozone (O₃) exposure to diabetes risk, but mechanisms and early biomarkers remain unclear. (2) Methods: Female mice exposed to 0.5/1.0 ppm O₃ were assessed for glucose tolerance and HOMA (homeostasis model assessment) index. Genes related to impaired glucose tolerance and insulin resistance were screened through the Comparative Toxicogenomics Database (CTD), and verified using quantitative real-time PCR. In addition, liver histopathological observations and the determination of basic biochemical indicators were conducted, and targeted metabolomics analysis was performed on the liver to verify glycogen levels and gene expression. In vitro validation was conducted with HepG2 and Min6 cell lines. (3) Results: Fasting blood glucose and insulin resistance were elevated following O₃ exposure. Given that the liver plays a critical role in glucose metabolism, we further investigated hepatocyte apoptosis and alterations in glycogen metabolism, including reduced glycogen levels and genetic dysregulation. Metabolomics analysis revealed abnormalities in fructose metabolism and glycogen synthesis in the livers of the O₃-exposed group. In vitro studies demonstrated that oxidative stress enhances both liver cell apoptosis and insulin resistance in pancreatic islet β cells. (4) Conclusions: O₃ triggers prediabetes symptoms via hepatic metabolic dysfunction and hepatocyte apoptosis. The identified metabolites and genes offer potential as early biomarkers and therapeutic targets. Full article

Precision neurosurgery is rapidly evolving as a medical specialty by merging genomic medicine, multi-omics technologies, and artificial intelligence (AI) technology, while at the same time, society is shifting away from the traditional, anatomic model of care to consider a more precise, molecular model of care. The general purpose of this review is to contemporaneously reflect on how these advances will impact neurosurgical care by providing us with more precise diagnostic and treatment pathways. We hope to provide a relevant review of the recent advances in genomics and multi-omics in the context of clinical practice and highlight their transformational opportunities in the existing models of care, where improved molecular insights can support improvements in clinical care. More specifically, we will highlight how genomic profiling, CRISPR-Cas9, and multi-omics platforms (genomics, transcriptomics, proteomics, and metabolomics) are increasing our understanding of central nervous system (CNS) disorders. Achievements obtained with transformational technologies such as single-cell RNA sequencing and intraoperative mass spectrometry are exemplary of the molecular diagnostic possibilities in real-time molecular diagnostics to enable a more directed approach in surgical options. We will also explore how identifying specific biomarkers (e.g., IDH mutations and MGMT promoter methylation) became a tipping point in the care of glioblastoma and allowed for the establishment of a new taxonomy of tumors that became applicable for surgeons, where a change in practice enjoined a different surgical resection approach and subsequently stratified the adjuvant therapies undertaken after surgery. Furthermore, we reflect on how the novel genomic characterization of mutations like DEPDC5 and SCN1A transformed the pre-surgery selection of surgical candidates for refractory epilepsy when conventional imaging did not define an epileptogenic zone, thus reducing resective surgery occurring in clinical practice. While we are atop the crest of an exciting wave of advances, we recognize that we also must be diligent about the challenges we must navigate to implement genomic medicine in neurosurgery—including ethical and technical challenges that could arise when genomic mutation-based therapies require the concurrent application of multi-omics data collection to be realized in practice for the benefit of patients, as well as the constraints from the blood–brain barrier. The primary challenges also relate to the possible gene privacy implications around genomic medicine and equitable access to technology-based alternative practice disrupting interventions. We hope the contribution from this review will not just be situational consolidation and integration of knowledge but also a stimulus for new lines of research and clinical practice. We also hope to stimulate mindful discussions about future possibilities for conscientious and sustainable progress in our evolution toward a genomic model of precision neurosurgery. In the spirit of providing a critical perspective, we hope that we are also adding to the larger opportunity to embed molecular precision into neuroscience care, striving to promote better practice and better outcomes for patients in a global sense. Full article

Alzheimer’s dementia (AD) is a disease of the ageing brain. It begins in the hippocampal region with the epicentre in the entorhinal cortex, then gradually extends into adjacent brain areas involved in memory and cognition. The events which initiate the damage are unknown and under intense investigation. Localization to the hippocampus can now be explained by anatomical features of the blood vessels supplying this region. Blood supply and hence oxygen delivery to the area are jeopardized by poor flow through narrowed arteries. In genomic and metabolomic studies, the respiratory chain and mitochondrial pathways which generate ATP were leading pathways associated with AD. This review explores the notion that ATP depletion resulting from hippocampal hypoperfusion has a prime role in initiating damage. Sections cover sensing of ATP depletion and protective responses, vulnerable processes with very heavy ATP consumption (the malate shuttle, the glutamate/glutamine/GABA (γ-aminobutyric acid) cycle, and axonal transport), phospholipid disturbances and peroxidation by reactive oxygen species, hippocampal perfusion and the effects of hypertension, chronic hypoxia, and arterial vasospasm, and an overview of recent relevant genomic studies. The findings demonstrate strong scientific arguments for the proposal with increasing supportive evidence. These lines of enquiry should be pursued. Full article

Phenylketonuria (PKU) is a monogenic disorder caused by pathogenic variants in the gene encoding phenylalanine hydroxylase (PAH), an enzyme essential for phenylalanine (Phe) metabolism. It is characterized by elevated Phe levels, leading to a wide spectrum of clinical phenotypes. These phenotypes are characterized by varying Phe accumulation, dietary tolerance, and heterogeneous cognitive and neurological outcomes, but current monitoring methods, focused primarily on blood Phe levels, are limited in capturing this variability. In this study, we applied mass spectrometry-based advanced quantitative amino acid analyses, untargeted metabolomics, and lipidomics analyses. We examined the plasma metabolite and lipid profiles in a total of 73 individuals with various PKU phenotypes against healthy controls to see how the metabolome and lipidome of the patients change in different phenotypes. We investigated whether novel markers could be associated with metabolic control status. By elucidating the metabolic and lipid fingerprints of PKU’s phenotypic variability, our findings may provide novel insights that could inform the refinement of dietary and pharmacological interventions, thereby supporting the development of more personalized treatment strategies. Full article

Metabolome studies have already been carried out in patients with systemic sclerosis (SSc). However, polyneuropathy (PNP) as a complication of SSc has been overlooked in these studies. To the best of our knowledge, this is the first study to examine metabolic changes in SSc patients with PNP. Patients with SSc (n = 62) and a healthy control group (HC) (n = 72) were recruited from two Latvian hospitals. Blood plasma samples were collected and analyzed using an LC-MS-based targeted metabolomics workflow. Our plasma sample cohort consisted of 62 patients with SSc, 42% of whom had PNP. Differences between SSc patients and the HC group with fold changes > 2 were observed for aspartic acid, glutamic acid, valine, and citrulline, all of which were reduced. In contrast to the SSc to HC discrimination, no metabolites had a high fold change; only minor changes were observed using FC > 1.3. We identified elevated concentrations of kynurenine, asparagine, and alanine. Changes in metabolite regulation in patients with SSc, compared to controls, are not identical to those observed in SSc patients with PNP, with elevated concentrations of kynurenine and alanine specific to the SSc subgroup. SSc patients with PNP should probably be considered a distinct population with important metabolomic features. Full article

In this study, we investigated the effects of dietary supplementation with thermally modified attapulgite on the daily weight gain, serum biochemical indices, and serum metabolites of Simmental fattening cattle. A total of 30 healthy Simmental fattening beef calves of similar age (8 to 9 months old) and body weight (370 ± 10 kg) were randomly divided into two groups, each containing 15 animals. A control group was fed the basal diet, and a treatment group was fed the same basal diet with the addition of 4 g/kg of thermally modified attapulgite. After 75 days of formal experiment, the calves in the two groups were weighed, and blood samples were collected by tail vein blood sampling for determinations of the serum biochemical indices and serum metabolites using liquid chromatography–mass spectrometry (LC-MS) analysis. The results indicated that the addition of thermally modified attapulgite to the diet had no significant effects on the daily weight gain of fattening beef cattle. After feeding with modified attapulgite, the glutathione peroxidase and superoxide dismutase activities in the serum of the experimental group were 55.02% (257.26 U·mL⁻¹ to 165.95 U·mL⁻¹, p < 0.05) and 13.11% (18.98 U·mL⁻¹ to 16.78 U·mL⁻¹, p < 0.05) higher than that in the control group. Compared with the control group, the tumor necrosis factor-alpha content was reduced by 14.50% (31.27 pg·mL⁻¹ to 36.57 pg·mL⁻¹, p < 0.01), and the concentration of interleukin-6 and lipopolysaccharide decreased by 17.00% (34.33 pg·mL⁻¹ to 41.36 pg·mL⁻¹, p < 0.001) and 23.05% (51.34 EU·L⁻¹ to 66.72 EU·L⁻¹, p < 0.001) in the serum of the experimental group. Contrastingly, the thermally modified attapulgite had no significant effects on the levels of serum total protein, albumin, or globulin in Simmental fattening cattle (p > 0.05). Furthermore, the results of serum metabolomic analyses revealed that there were a total of 98 differential metabolites, which were mainly enriched with respect to glycerophospholipid metabolism, Th1 and Th2 cell differentiation, autophagy-other, retrograde endogenous cannabinoid signaling, and the NF-κB signaling pathway. Overall, thermally modified attapulgite was found to effectively increase the activity of antioxidant enzymes, reduce serum inflammatory mediators, may suppress oxidative damage, enhance immunity, and have a positive influence on the health of Simmental fattening beef calves. Full article

This study employs a machine learning approach to identify a small-molecule-based signature capable of predicting Alzheimer’s disease (AD). Utilizing metabolomics data from the plasma of a well-characterized cohort of 94 AD patients and 62 healthy controls; metabolite levels were assessed using the Biocrates MxP^® Quant 500 platform. Data preprocessing involved removing low-quality samples, selecting relevant biochemical groups, and normalizing metabolite data based on demographic variables such as age, sex, and fasting time. Linear regression models were used to identify concomitant parameters that consisted of the data for a given metabolite within each of the biochemical families that were considered. Detection of these “concomitant” metabolites facilitates normalization and allows sample comparison. Residual analysis revealed distinct metabolite profiles between AD patients and controls across groups, such as amino acid-related compounds, bile acids, biogenic amines, indoles, carboxylic acids, and fatty acids. Correlation heatmaps illustrated significant interdependencies, highlighting specific molecules like carnosine, 5-aminovaleric acid (5-AVA), cholic acid (CA), and indoxyl sulfate (Ind-SO₄) as promising indicators. Linear Discriminant Analysis (LDA), validated using Leave-One-Out Cross-Validation, demonstrated that combinations of four or five molecules could classify AD with accuracy exceeding 75%, sensitivity up to 80%, and specificity around 79%. Notably, optimal combinations integrated metabolites with both a tendency to increase and a tendency to decrease in AD. A multivariate strategy consistently identified included 5-AVA, carnosine, CA, and hypoxanthine as having predictive potential. Overall, this study supports the utility of combining data of plasma small molecules as predictors for AD, offering a novel diagnostic tool and paving the way for advancements in personalized medicine. Full article

Background/Objectives: Chronic manganese (Mn) exposure is a recognized environmental contributor to Parkinsonian syndromes, including Mn-induced Parkinsonism (MnIP). This study aimed to evaluate whole-blood Mn levels and investigate disease/exposure-status-related alterations in metabolomic and lipidomic profiles. Methods: A case–control study (N = 97) was conducted in Brescia, Italy, stratifying participants by Parkinsonism diagnosis and residential Mn exposure. Whole-blood Mn was quantified using ICP-MS. Untargeted metabolomic and lipidomic profiling was conducted using LC-MS. Statistical analyses included Mann–Whitney U tests, conditional logistic regression, ANCOVA, and pathway analysis. Results: Whole-blood Mn levels were significantly elevated in Parkinsonism cases vs. controls (median: 1.55 µg/dL [IQR: 0.75] vs. 1.02 µg/dL [IQR: 0.37]; p = 0.001), with Mn associated with increased odds of Parkinsonism (OR = 2.42, 95% CI: 1.13–5.17; p = 0.022). The disease effect metabolites included 3-sulfoxy-L-tyrosine (β = 1.12), formiminoglutamic acid (β = 0.99), and glyoxylic acid (β = 0.83); all FDR p < 0.001. The exposure effect was associated with elevated glycocholic acid (β = 0.51; FDR p = 0.006) and disrupted butanoate (Impact = 0.03; p = 0.004) and glutamate metabolism (p = 0.03). Additionally, SLC-mediated transmembrane transport was enriched (p = 0.003). The interaction effect identified palmitelaidic acid (β = 0.30; FDR p < 0.001), vitamin B6 metabolism (Impact = 0.08; p = 0.03), and glucose homeostasis pathways. In lipidomics, triacylglycerols and phosphatidylethanolamines were associated with the disease effect (e.g., TG(16:0_10:0_18:1), β = 0.79; FDR p < 0.01). Ferroptosis and endocannabinoid signaling were enriched in both disease and interaction effects, while sphingolipid metabolism was specific to the interaction effect. Conclusions: Mn exposure and Parkinsonism are associated with distinct metabolic and lipidomic perturbations. These findings support the utility of omics in identifying environmentally linked Parkinsonism biomarkers and mechanisms. Full article

Due to advances in molecular technologies and the expanding knowledge of biomarkers, their use in patient screening, diagnosis, prognosis, and targeted therapy is continuously increasing. Biomarker characteristics play a crucial role across all areas of medical research/practice. Biomarkers often reflect changes in the biochemical composition of biofluids, which can be qualitatively and quantitatively analyzed using methods such as high-performance liquid chromatography (HPLC) at various stages of clinical intervention. This study focuses on establishing physiological reference ranges for selected biochemical and metabolomic indicators by analyzing blood serum samples from domestic sheep. A total of sixty samples are examined using standard biochemical assays and HPLC, resulting in the determination of experimental reference values for twenty-one biochemical and eight metabolomic parameters. Reliable and reproducible preclinical testing is essential before any diagnostic method can be introduced into clinical use. A thorough understanding of the safety and efficacy of such methods in animal models is a prerequisite for initiating human trials. Species selection and the definition of physiological biomarker ranges are therefore critical components in the development of effective preclinical protocols. This work contributes to the foundation needed for further clinical testing by establishing reference values for relevant biomarkers in a commonly used animal model. Full article

Background and Objectives: Regional and systemic analgesic techniques, such as erector spinae plane (ESP) block and opioid administration, implemented during cancer surgery, have been shown to influence immune responses and potentially affect cancer outcomes. Surgical stress and analgesic techniques used in cancer surgery—such as regional nerve blocks or systemic opioids—not only affect pain control but also influence immune and inflammatory pathways that may impact cancer progression. To understand the biological consequences of these interventions, metabolomic profiling has emerged as a powerful approach for capturing systemic metabolic and immunological changes, which are particularly relevant in the oncologic perioperative setting. In this study, we examined the impact of the ESP on the metabolomic profile, as well as levels of VEGF, cortisol, and CRP, in addition to its analgesic effects in breast cancer surgery. Materials and Methods: Ninety patients were placed into three different analgesia groups (morphine, ESP, and control groups). Demographic data, ASA classification, comorbidities, surgery types, and pain scores were documented. Blood samples were taken at preoperative hour 0, postoperative hour 1, and postoperative hour 24 (T0, T1, and T24). VEGF, cortisol, and CRP levels were measured, and metabolomic analysis was performed. Results: Study groups were comparable regarding demographic findings, comorbidities, and surgery types (p > 0.05). NRS scores of group ESP were lowest in the first 12 h period (p < 0.01) and ESP block reduced opioid consumption (p < 0.01). VEGF and cortisol levels of group morphine were similar to ESP at T24 (p > 0.05). Group ESP had lower VEGF and cortisol levels than the control at T24 (p = 0.025, p = 0.041, respectively.). The CRP level of group morphine was higher than both ESP and control at T24 (p = 0.022). Metabolites involved in primary bile acid, steroid hormone biosynthesis, amino acid, and glutathione metabolism were changed in group ESP. Conclusions: Metabolites in bile acid biosynthesis and steroid hormone pathways, which play a key role in immune responses, were notably lower in the ESP group. Accordingly, VEGF and cortisol peaks were more moderate in group ESP. In conclusion, we think that ESP block, which provides adequate analgesia, is an acceptable approach in terms of modulating immune responses in breast cancer surgery. Full article

Dapagliflozin, a sodium-glucose cotransporter 2 inhibitor, treats type 2 diabetes by blocking renal glucose reabsorption and promoting urinary glucose excretion. This mechanism lowers blood glucose concentrations independently of insulin. The resulting caloric loss also contributes to weight reduction. Although these effects are well documented in patients with diabetes, their magnitude and underlying mechanisms in healthy individuals remain poorly understood. Background/Objectives: We investigated metabolic alterations after a single 10 mg dose of dapagliflozin in healthy adults with normal body-mass indices (BMIs) using untargeted metabolomics. Methods: Thirteen healthy volunteers completed this study. Plasma was collected before and 24 h after dosing. Untargeted metabolic profiling was performed with ultra-high-performance liquid chromatography–quadrupole time-of-flight/mass spectrometry. Results: Twenty-five endogenous metabolites were annotated; ten were putatively identified. Eight metabolites increased significantly, whereas two decreased. Up-regulated metabolites included phosphatidylcholine (PC) species (PC O-36:5, PC 36:3), phosphatidylserine (PS) species (PS 40:2, PS 40:3, PS 36:1, PS 40:4), lysophosphatidylserine 22:1, and uridine. Dehydroepiandrosterone sulfate and bilirubin were down-regulated. According to the Human Metabolome Database, these metabolites participate in glycerophospholipid, branched-chain amino acid, pyrimidine, and steroid-hormone metabolism. Conclusions: Dapagliflozin may affect pathways related to energy metabolism and homeostasis beyond glucose regulation. These data provide a reference for future investigations into energy balance and metabolic flexibility in metabolic disorders. Full article

Odors from pet cats can negatively affect the quality of life of cat owners. The diverse bioactive compounds in plant extracts make them a promising candidate for effective odor reduction. This study evaluated twelve plant extracts for deodorizing efficacy via in vitro fermentation tests. Rosemary extract and licorice extract exhibited better deodorizing effects, with fractions of rosemary extract below 100 Da demonstrating the most effective deodorizing performance. Based on these findings, subsequent feeding trials were conducted using rosemary extract and its fractions below 100 Da. In the feeding trial, adult British Shorthair cats were divided into three groups (Control Check, RE, and RE100) and housed in a controlled-environment respiration chamber for 30 days. Measurements included odor emissions, fecal and blood physicochemical parameters, immune parameters, microbiota composition based on 16S rRNA sequencing, and metabolome analysis. The results of the feeding trial indicated that rosemary extract significantly reduced ammonia and hydrogen sulfide emissions (46.84%, 41.64%), while fractions below 100 Da of rosemary extract achieved even greater reductions (55.62%, 53.87%). Rosemary extract regulated the intestinal microbial community, significantly increasing the relative abundance of the intestinal probiotic Bifidobacterium (p < 0.05) and reducing the population of sulfate-reducing bacteria (p < 0.05). It also significantly reduced urease and uricase activities (p < 0.05) to reduce ammonia production and inhibited the degradation of sulfur-containing proteins and sulfate reduction to reduce hydrogen sulfide emissions. Furthermore, rosemary extract significantly enhanced the immune function of British Shorthair cats (p < 0.05). This study suggests that rosemary extract, particularly its fractions below 100 Da, is a highly promising pet deodorizer. Full article