KIF18A inactivates hepatic stellate cells and alleviates liver fibrosis through the TTC3/Akt/mTOR pathway.

Activation of hepatic stellate cells (HSCs) has been demonstrated to play a pivotal role in the process of liver fibrogenesis. In this study, we observed a decrease in the expression of KIF18A in fibrotic liver tissues compared to healthy liver tissues, which exhibited a negative correlation with the activation of HSCs. To elucidate the molecular mechanisms underlying the involvement of KIF18A, we performed in vitro proliferation experiments and established a CCl4-induced liver fibrosis model. Our results revealed that KIF18A knockdown enhanced HSCs proliferation and reduced HSCs apoptosis in vitro. Mouse liver fibrosis grade was evaluated with Masson's trichrome and alpha-smooth muscle actin (α-SMA) staining. In addition, the expression of fibrosis markers Col1A1, Stat1, and Timp1 were detected. Animal experiments demonstrated that knockdown of KIF18A could promote liver fibrosis, whereas overexpression of KIF18A alleviated liver fibrosis in a CCl4-induced mouse model. Mechanistically, we found that KIF18A suppressed the AKT/mTOR pathway and exhibited direct binding to TTC3. Moreover, TTC3 was found to interact with p-AKT and could promote its ubiquitination and degradation. Our findings provide compelling evidence that KIF18A enhances the protein binding between TTC3 and p-AKT, promoting TTC3-mediated ubiquitination and degradation of p-AKT. These results refine the current understanding of the mechanisms underlying the pathogenesis of liver fibrosis and may offer new targets for treating this patient population.

Promising role of protein arginine methyltransferases in overcoming anti-cancer drug resistance.

Drug resistance remains a major challenge in cancer treatment, necessitating the development of novel strategies to overcome it. Protein arginine methyltransferases (PRMTs) are enzymes responsible for epigenetic arginine methylation, which regulates various biological and pathological processes, as a result, they are attractive therapeutic targets for overcoming anti-cancer drug resistance. The ongoing development of small molecules targeting PRMTs has resulted in the generation of chemical probes for modulating most PRMTs and facilitated clinical treatment for the most advanced oncology targets, including PRMT1 and PRMT5. In this review, we summarize various mechanisms underlying protein arginine methylation and the roles of specific PRMTs in driving cancer drug resistance. Furthermore, we highlight the potential clinical implications of PRMT inhibitors in decreasing cancer drug resistance. PRMTs promote the formation and maintenance of drug-tolerant cells via several mechanisms, including altered drug efflux transporters, autophagy, DNA damage repair, cancer stem cell-related function, epithelial-mesenchymal transition, and disordered tumor microenvironment. Multiple preclinical and ongoing clinical trials have demonstrated that PRMT inhibitors, particularly PRMT5 inhibitors, can sensitize cancer cells to various anti-cancer drugs, including chemotherapeutic, targeted therapeutic, and immunotherapeutic agents. Combining PRMT inhibitors with existing anti-cancer strategies will be a promising approach for overcoming anti-cancer drug resistance. Furthermore, enhanced knowledge of the complex functions of arginine methylation and PRMTs in drug resistance will guide the future development of PRMT inhibitors and may help identify new clinical indications.

DIA-based quantitative proteomics analysis of plasma exosomes in rat model of allergic rhinitis.

Allergic rhinitis (AR) is a common chronic inflammatory disease characterized by symptoms such as itching, rhinorrhea, sneezing, and nasal obstruction. Despite being classified as an IgE-mediated typeⅠ allergy for many years, the complex pathophysiological mechanism of AR continues to present a challenge in clinical management. The objective of this study was to quantify the proteomics of plasma exosomes using data independent acquisition (DIA) in combination with liquid chromatography-mass spectrometry (LC-MS/MS) to identify the key proteins involved in the development and progression of AR. In the AR rat model, a total of 41 proteins demonstrated significant up-regulation, while 51 proteins were found to be significantly down-regulated. Gene ontology (GO) analysis results indicated that the altered proteins were highly enriched in cellular regulatory processes and enzymatic activity in AR rats. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and protein-protein interaction (PPI) network results revealed that the pivotal proteins C4b, C1qa, C1qc, and Mbl1 might be involved in the metabolic pathways of the immune system in AR through the activation of the complement and coagulation cascades pathway. These proteins could serve as diagnostic markers and therapeutic targets for AR, which is of great significance in understanding the role of exosome proteins in AR.

Hybrid biodegradable materials from starch and hydrocolloid: fabrication, properties and applications of starch-hydrocolloid film, gel and bead.

The potential for utilizing starch and hydrocolloids as sustainable biomaterials has garnered significant attention among researchers. The biodegradability and functional properties of composite films, gels, and beads, as well as their environmental friendliness, make them attractive options for a variety of applications. However, the hydrophilicity, brittleness, and regeneration limitations of starch materials can be addressed through the incorporation of non-starch hydrocolloids. This article summarizes the formation mechanisms and interactions of starch-hydrocolloid films, gels, and gel beads, evaluates the factors that affect their structural and functional properties, and presents an overview of the progress made in their physicochemical and functional applications. The structure of starch-hydrocolloid composites is primarily formed through hydrogen bond interactions, and the source, proportion, and preparation conditions of the components are critical factors that affect the properties of the biomaterials. Starch-hydrocolloid films are primarily used for extending the shelf life of food products and detecting food freshness. Starch-hydrocolloid gels are utilized as adsorption materials, wound dressings, and flexible sensors, and starch-hydrocolloid beads are primarily employed for the controlled release of bioactive substances. It is clear that starch-hydrocolloid composites have the potential to develop novel advanced materials for various applications in the food, biological, and materials industries.

Cost-related medication nonadherence in US adults with asthma: The National Health Interview Survey, 2013-2020.

BACKGROUND: Asthma is a chronic disease that needs long-term control for secondary prevention. Health-related expenditures resulting from asthma are rising in the United States, and medication nonadherence is associated with adverse health outcomes in patients with asthma. OBJECTIVE: To estimate the prevalence and risk factors of cost-related medication nonadherence (CRN) in individuals with asthma in the United States. METHODS: We identified patients aged above or equal to 18 years with a history of asthma in nationally representative cross-sectional data, the National Health Interview Survey 2013 to 2020. Participants were considered to have experienced CRN if at any time in the 12 months they reported skipping doses, taking less medication, or delaying filling a prescription to save money. The weighted prevalence of CRN was estimated overall and by subgroups. Logistic regression was used to identify CRN-related characteristics. RESULTS: Of the 26,539 National Health Interview Survey participants with a history of asthma, 4360 (15.77%; representing 3.92 million of the US population) reported CRN, with 10.12% (weighted 2.51 million) of patients skipping doses to save money, 10.82% (weighted 2.69 million) taking less medication to save money, and 13.35% (weighted 3.31 million) delaying filling a prescription to save money. The subgroups young, women, low income, no health insurance, currently smoking, and with comorbidities had a higher prevalence of CRN. The results of this sensitivity analysis did not differ from the overall results. CONCLUSION: In the United States, 1 in 6 adults with a history of asthma is nonadherence with medications due to costs. Removing financial barriers to accessing medication can improve medication adherence in patients with asthma.

Design, synthesis and biological evaluation of novel 9-methyl-9H-purine and thieno[3, 2-d]pyrimidine derivatives as potent mTOR inhibitors.

The mammalian target of rapamycin (mTOR) has been proved to be an effective target for cancer therapy. Two kinds of mTOR inhibitors, the rapalogs and mTOR kinase inhibitors (TORKi), have been developed and clinically validated in several types of malignancies. Compared with rapalogs, TORKi can exert better antitumor activity by inhibiting both mTORC1 and mTORC2, but the clinical development of current TORKi candidates has been relative slow, more TORKi with novel scaffold need to be developed to expand the current pipelines. In this study, a series of 9-methyl-9H-purine and thieno[3, 2-d]pyrimidine derivatives were designed, synthesized and biological evaluation. Most of these compounds exhibited good mTOR kinase inhibitory activity and selectivity over PI3Kα. Subsequent antiproliferative assay allowed us to identify the lead compound 15i, which display nanomolar to low micromolar IC50s against six human cancer cell lines. 15i could induce cell cycle arrest of MCF-7, PC-3 and A549 cells at the G0/G1 phase and suppress the migration and invasion of these cancer cells by suppressing the phosphorylation of AKT and P70S6 kinase. It could also regulate autophagy-related proteins to induce autophagy. Therefore, 15i would be a starting point for the development of new TORKi as anticancer drug.

Evaluating Listening Performance for COVID-19 Detection by Clinicians and Machine Learning: Comparative Study.

BACKGROUND: To date, performance comparisons between men and machines have been carried out in many health domains. Yet machine learning (ML) models and human performance comparisons in audio-based respiratory diagnosis remain largely unexplored. OBJECTIVE: The primary objective of this study was to compare human clinicians and an ML model in predicting COVID-19 from respiratory sound recordings. METHODS: In this study, we compared human clinicians and an ML model in predicting COVID-19 from respiratory sound recordings. Prediction performance on 24 audio samples (12 tested positive) made by 36 clinicians with experience in treating COVID-19 or other respiratory illnesses was compared with predictions made by an ML model trained on 1162 samples. Each sample consisted of voice, cough, and breathing sound recordings from 1 subject, and the length of each sample was around 20 seconds. We also investigated whether combining the predictions of the model and human experts could further enhance the performance in terms of both accuracy and confidence. RESULTS: The ML model outperformed the clinicians, yielding a sensitivity of 0.75 and a specificity of 0.83, whereas the best performance achieved by the clinicians was 0.67 in terms of sensitivity and 0.75 in terms of specificity. Integrating the clinicians' and the model's predictions, however, could enhance performance further, achieving a sensitivity of 0.83 and a specificity of 0.92. CONCLUSIONS: Our findings suggest that the clinicians and the ML model could make better clinical decisions via a cooperative approach and achieve higher confidence in audio-based respiratory diagnosis.

Parametric analysis and a predictive model for color difference during laser-induced coloration on titanium.

Laser-induced coloration on a metallic surface has been of interest to many application arweas. However laser machining of metals involves many complex problems including nonlinear unstable coupled with multiple factors. Therefore there are still some significant challenges in the precise control of color creation. Here we explored the process of the laser-induced coloration and find the connection between surface colors and processing parameters. The Response Surface Methodology (RSM) based experimental design was adopted to explore the influence of the single processing parameter and the interaction between parameters on color changes of titanium. The results showed that the scanning speed laser power repetition rate and hatch distance had significant effects on color changes of titanium. Then we demonstrated that using artificial neural network (ANN) is an effective solution of nonlinear problems in laser-induced coloration which can match the processing parameters and the L*a*b* color values on titanium surface precisely with limited experiments. Finally we successfully used the processing parameters estimated by ANN model to create unique art painting on titanium with nanosecond pulsed laser. This work can provide a potential method to solve the problem in the color consistency and open a new perspective in industrial application of laser-induced coloration technology.

Dihydroartemisinin induces cell apoptosis through repression of UHRF1 in prostate cancer cells.

Prostate cancer (PCa) seriously jeopardizes men's health worldwide. Dihydroartemisinin, which is an effective antimalarial agent, has shown potential anticancer effects in various human cancer cell lines, including PCa cells. However, the mechanisms underlying the anticancer activity of dihydroartemisinin are not fully understood. Ubiquitin-like with plant homeodomain and ring finger domain 1 (UHRF1) is highly expressed in a variety of tumors and is negatively correlated with the prognosis of various tumors. We reported previously that UHRF1 is downregulated during apoptosis induced by dihydroartemisinin in PC-3 PCa cells. In this study, we transfected PC-3 cells with lentiviruses containing UHRF1 or shRNA-UHRF1. Then, the cells were treated with dihydroartemisinin at different concentrations. Our data showed that overexpression of UHRF1 promoted cell proliferation and migration in PC-3 cells, inhibited cell apoptosis, increased cell proportion in G2 phase, increased DNA methyltransferase 1 and decreased p16INK4A expression at mRNA and protein levels. Downregulation of UHRF1 produces the opposite results. Moreover, the phenomena caused by overexpression of UHRF1 were inhibited after dihydroartemisinin treatment. Compared with control cells, cells overexpressing UHRF1 can resist the proapoptotic and antiproliferative effects of dihydroartemisinin to a certain extent. The effects of UHRF1 knockdown were further aggravated by dihydroartemisinin treatment, but no statistically significant effect was observed with increasing drug concentration. Our results suggested that dihydroartemisinin decreases proliferation and migration but enhances apoptosis of PCa cells, likely by downregulating UHRF1 and upregulating p16INK4A.

Laparoscopic versus open hepatectomy for intrahepatic cholangiocarcinoma in patients aged 60 and older: a retrospective cohort study.

Objective laparoscopic surgical excision is the recommended treatment for liver cancers, yet its benefits in patients aged 60 and older remain poorly understood. Thus, this study evaluated the feasibility, safety, and clinical outcomes of laparoscopic hepatectomy for patients aged 60 and older with intrahepatic cholangiocarcinoma (ICC).MethodsAfter screening, 107 patients who underwent hepatectomy for ICC were enrolled and grouped into either laparoscopic (LH) or open hepatectomy (OH) groups. Baseline characteristics, pathological findings, and long-term outcomes were compared between the two groups. Independent prognostic factors for overall survival (OS) and disease-free survival (DFS) were identified using univariate and multivariate analyses.ResultsAmong baseline characteristics and pathological findings, only pre-operative albumin was higher in the LH group. The LH group had more favorable short-term outcomes such as incision length, level of postoperative total bilirubin, and length of postoperative stays than the OH group. The postoperative complication, lymph node dissection and R0 resection rate, and long-term outcomes including OS and DFS were not significantly different between the two groups. Cancer Antigen-19-9(CA-19-9) and pathological differentiation were independent prognostic factors for OS, whereas CA-19-9 and neutrophil count were independent prognostic factors for DFS.ConclusionLH is safe, reliable, and feasible for treatment of ICC patients aged 60 and older as it had better short-term clinical outcomes than OH and achieved long-term prognoses that were comparable to those of OH.

Exploring Longitudinal Cough, Breath, and Voice Data for COVID-19 Progression Prediction via Sequential Deep Learning: Model Development and Validation.

BACKGROUND: Recent work has shown the potential of using audio data (eg, cough, breathing, and voice) in the screening for COVID-19. However, these approaches only focus on one-off detection and detect the infection, given the current audio sample, but do not monitor disease progression in COVID-19. Limited exploration has been put forward to continuously monitor COVID-19 progression, especially recovery, through longitudinal audio data. Tracking disease progression characteristics and patterns of recovery could bring insights and lead to more timely treatment or treatment adjustment, as well as better resource management in health care systems. OBJECTIVE: The primary objective of this study is to explore the potential of longitudinal audio samples over time for COVID-19 progression prediction and, especially, recovery trend prediction using sequential deep learning techniques. METHODS: Crowdsourced respiratory audio data, including breathing, cough, and voice samples, from 212 individuals over 5-385 days were analyzed, alongside their self-reported COVID-19 test results. We developed and validated a deep learning-enabled tracking tool using gated recurrent units (GRUs) to detect COVID-19 progression by exploring the audio dynamics of the individuals' historical audio biomarkers. The investigation comprised 2 parts: (1) COVID-19 detection in terms of positive and negative (healthy) tests using sequential audio signals, which was primarily assessed in terms of the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity, with 95% CIs, and (2) longitudinal disease progression prediction over time in terms of probability of positive tests, which was evaluated using the correlation between the predicted probability trajectory and self-reported labels. RESULTS: We first explored the benefits of capturing longitudinal dynamics of audio biomarkers for COVID-19 detection. The strong performance, yielding an AUROC of 0.79, a sensitivity of 0.75, and a specificity of 0.71 supported the effectiveness of the approach compared to methods that do not leverage longitudinal dynamics. We further examined the predicted disease progression trajectory, which displayed high consistency with longitudinal test results with a correlation of 0.75 in the test cohort and 0.86 in a subset of the test cohort with 12 (57.1%) of 21 COVID-19-positive participants who reported disease recovery. Our findings suggest that monitoring COVID-19 evolution via longitudinal audio data has potential in the tracking of individuals' disease progression and recovery. CONCLUSIONS: An audio-based COVID-19 progression monitoring system was developed using deep learning techniques, with strong performance showing high consistency between the predicted trajectory and the test results over time, especially for recovery trend predictions. This has good potential in the postpeak and postpandemic era that can help guide medical treatment and optimize hospital resource allocations. The changes in longitudinal audio samples, referred to as audio dynamics, are associated with COVID-19 progression; thus, modeling the audio dynamics can potentially capture the underlying disease progression process and further aid COVID-19 progression prediction. This framework provides a flexible, affordable, and timely tool for COVID-19 tracking, and more importantly, it also provides a proof of concept of how telemonitoring could be applicable to respiratory diseases monitoring, in general.

Sunlight-Driven Highly Selective Catalytic Oxidation of 5-Hydroxymethylfurfural Towards Tunable Products.

Owing to the easy over-oxidation, it is a promising yet challenging task to explore renewable carbon resources to control the sunlight-driven selective catalytic oxidation of biomass-derived 5-hydroxymethylfurfural (HMF), producing important chemical feedstocks, namely, less-oxidized 2,5-diformylfuran (DFF) and 5-hydroxymethyl-2-furancarboxylic acid (HMFCA). Herein, we have developed a photocatalyst by anchoring a Ru complex on CdS quantum dots, which achieves selective oxidation of HMF toward DFF or HMFCA with high conversion (>81 %) and selectivity (>90 %), based on the controllable generation of two oxygen radicals under different atmospheres. Such selective conversion can also work well outside the laboratory by using natural sunlight. In particular, the selective production of HMFCA through photocatalytic HMF oxidation is achieved for the first time. More importantly, our photocatalyst is applicable for the selective oxidation of other compounds with hydroxyl and aldehyde groups.

Real-Time Monitoring of Heavy Metals in Healthcare via Twistable and Washable Smartsensors.

The wearable and integrated sensing platform is a promising choice for developing real-time analytic electronics with clear advantages but still poses challenges, such as the realization of high precision, low limit of detection (LOD), moderate mechanical capacity, integration, and miniaturization. In this work, a simple printed wearable smartsensor has been fabricated with the aid of electrochemical plating methods with bismuth (Bi) films. The excellent sensing behaviors, including linear relationship, selectivity, stability, repeatability, and the LOD at ppb levels, have been obtained by this smartsensor. Additionally, the highly flexible textile-based sensor exhibits potential application on the substrates of daily cloth, sports T-shirt, and sports wristbands, and it maintains good stability under repeated deformations of washing and twisting. Importantly, integrated with printed circuit board, single chip micyoco, and Bluetooth modules, a smartsensing platform is successfully acquired for real-time detection of heavy metals (e.g., Zn, Cd, Pb, etc.). Finally, actual samples of human sweat, seawater, cosmetics, and drinking water have been remotely successfully demonstrated for detection by this smartsensor, enabling a great promise for fast on-site screening of samples in practical application.

Adherence to DASH dietary pattern is inversely associated with osteoarthritis in Americans.

Osteoarthritis (OA) is one of the most common diseases that cause disability among older adults. The objective of this study was to assess the association between adherence to the Dietary Approaches to Stop Hypertension (DASH) and OA in American adults. This study included adults (≥ aged 20 years) who participated in the National Health and Examination Survey (NHANES) 2007-2016 in the United States. Adherence to the DASH score was calculated from 8 food groups. Higher scores indicate better adherence to the DASH dietary pattern. Among the 21,901 participants included in this study, 10.26% reported having OA. Results of our multivariable logistic regression indicated a statistically significant inverse association between DASH score tertiles and OA. The adjusted ORs (95% CI) were 1.00 (ref), 0.89 (0.72; 1.10), and 0.78 (0.60; 1.00) across increasing DASH score tertiles (P for trend = 0.045). In this representative sample of American adults, greater adherence to the DASH dietary pattern was associated with lower likelihood of having OA.

Enabling Visible-Light-Driven Selective CO2 Reduction by Doping Quantum Dots: Trapping Electrons and Suppressing H2 Evolution.

Quantum dots (QDs), a class of promising candidates for harvesting visible light, generally exhibit low activity and selectivity towards photocatalytic CO2 reduction. Functionalizing QDs with metal complexes (or metal cations through ligands) is a widely used strategy for improving their catalytic activity; however, the resulting systems still suffer from low selectivity and stability in CO2 reduction. Herein, we report that doping CdS QDs with transition-metal sites can overcome these limitations and provide a system that enables highly selective photocatalytic reactions of CO2 with H2 O (100 % selectivity to CO and CH4 ), with excellent durability over 60 h. Doping Ni sites into the CdS lattice leads to effective trapping of photoexcited electrons at surface catalytic sites and substantial suppression of H2 evolution. The method reported here can be extended to various transition-metal sites, and offers new opportunities for exploring QD-based earth-abundant photocatalysts.

Association of Occupational and Leisure-Time Physical Activity With Allostatic Load.

INTRODUCTION: Leisure-time physical activity decreases allostatic load, a measure of burden of chronic stress. However, the role of occupational physical activity is unknown. This study examined associations of occupational physical activity and leisure-time physical activity with allostatic load among workers in the U.S. METHODS: This cross-sectional study included 6,944 U.S. workers aged 20-64 years from the National Health and Nutrition Examination Survey (2007-2018). Physical activity was assessed using the Global Physical Activity Questionnaire. Allostatic load was calculated using biomarkers of cardiovascular, metabolic, and immune systems. Associations of occupational physical activity and leisure-time physical activity with allostatic load were examined using negative binomial regressions. Analyses were conducted between August 2022 and March 2023. RESULTS: Vigorous leisure-time physical activity inversely associated with allostatic load among all workers (count ratio=0.68, 95% CI=0.62, 0.76) and in each sex- and age-stratified group as well as in each race/ethnicity-stratified group. Vigorous occupational physical activity positively associated with allostatic load only among females aged 20-44 years (1.38, 95% CI=1.10, 1.73). Inverse associations of vigorous leisure-time physical activity with allostatic load were similar in young females with high or low vigorous occupational physical activity. CONCLUSIONS: Increasing vigorous leisure-time physical activity associates with a lower allostatic load for all workers, whereas increasing vigorous occupational physical activity associates with a higher allostatic load only in young females. Promoting vigorous leisure-time physical activity reduces allostatic load among young females with either low or high vigorous occupational physical activity.

Uncertainty-aware Health Diagnostics via Class-balanced Evidential Deep Learning.

Uncertainty quantification is critical for ensuring the safety of deep learning-enabled health diagnostics, as it helps the model account for unknown factors and reduces the risk of misdiagnosis. However, existing uncertainty quantification studies often overlook the significant issue of class imbalance, which is common in medical data. In this paper, we propose a class-balanced evidential deep learning framework to achieve fair and reliable uncertainty estimates for health diagnostic models. This framework advances the state-of-the-art uncertainty quantification method of evidential deep learning with two novel mechanisms to address the challenges posed by class imbalance. Specifically, we introduce a pooling loss that enables the model to learn less biased evidence among classes and a learnable prior to regularize the posterior distribution that accounts for the quality of uncertainty estimates. Extensive experiments using benchmark data with varying degrees of imbalance and various naturally imbalanced health data demonstrate the effectiveness and superiority of our method. Our work pushes the envelope of uncertainty quantification from theoretical studies to realistic healthcare application scenarios. By enhancing uncertainty estimation for class-imbalanced data, we contribute to the development of more reliable and practical deep learning-enabled health diagnostic systems1.

Research on terahertz image analysis of thin-shell seeds based on semantic segmentation.

Assessing crop seed phenotypic traits is essential for breeding innovations and germplasm enhancement. However, the tough outer layers of thin-shelled seeds present significant challenges for traditional methods aimed at the rapid assessment of their internal structures and quality attributes. This study explores the potential of combining terahertz (THz) time-domain spectroscopy and imaging with semantic segmentation models for the rapid and non-destructive examination of these traits. A total of 120 watermelon seed samples from three distinct varieties, were curated in this study, facilitating a comprehensive analysis of both their outer layers and inner kernels. Utilizing a transmission imaging modality, THz spectral images were acquired and subsequently reconstructed employing a correlation coefficient method. Deep learning-based SegNet and DeepLab V3+ models were employed for automatic tissue segmentation. Our research revealed that DeepLab V3+ significantly surpassed SegNet in both speed and accuracy. Specifically, DeepLab V3+ achieved a pixel accuracy of 96.69 % and an intersection over the union of 91.3 % for the outer layer, with the inner kernel results closely following. These results underscore the proficiency of DeepLab V3+ in distinguishing between the seed coat and kernel, thereby furnishing precise phenotypic trait analyses for seeds with thin shells. Moreover, this study accentuates the instrumental role of deep learning technologies in advancing agricultural research and practices.

Comprehensive evaluation of treating drinking water for laying hens using slightly acidic electrolyzed water.

Slightly acidic electrolyzed water (SAEW) is well-known for its highly potent antibacterial properties and safe residue-free nature. In this study, a comprehensive evaluation was conducted on 2 disinfection methods for waterline cleaning in poultry houses: (1) continuously add SAEW into the waterline and (2) the conventional waterline disinfection method, which includes regular use of high-concentration chemical disinfectant for soaking the waterline and flushing with water. The evaluation focused on the effects of these methods on bacteria levels in laying hens' drinking water, the fecal normal rate of laying hens, egg quality, as well as the economic costs and water footprint associated with each method. The results show that the inhibition rate of the control group was 52.45% to 80.36%, which used 1500 mg/L sodium dichloroisocyanurate (DCCNa) for soaking and then flushing with water. The bacterial levels in the waterline returned to pre-treatment levels 26 h after cleaning. However, the experimental group with an available chlorine concentration (ACC) of 0.3 mg/L SAEW showed a higher inhibition rate (99.90%) than the control group (P < 0.05) and exhibited a sustained antimicrobial effect. Regarding eggshell thickness, eggshell strength, and Haugh units of the egg, there were no significant differences between the experimental and control groups. However, the experimental group had higher egg weight and darker yolk color (P < 0.05) than those of the control group. Besides, the experimental group exhibited a higher fecal normal rate and a lower water footprint than those of the control group. Hence, SAEW represents a favorable choice for disinfecting drinking water in poultry houses due to its ease of preparation, lack of residue, energy efficiency, and efficient antibacterial properties. To ensure adequate sanitation, it is recommended to incorporate SAEW with an ACC of 0.3 mg/L into the daily management of the drinking water system for laying hens.