Zirconium ion mediated collagen nanofibrous hydrogels with high mechanical strength.

Low mechanical strength is still the key question for collagen hydrogel consisting of nanofibrils as hard tissue repair scaffolds with no loss of biological function. In this work, novel collagen nanofibrous hydrogels with high mechanical strength were fabricated based on the pre-protection of trisodium citrate masked Zr(SO4)2 solution for collagen self-assembling nanofibrils and then further coordination with Zr(SO4)2 solution. The mature collagen nanofibrils with d-period were observed in Zr(IV) mediated collagen hydrogels by AFM when the Zr(IV) concentration was ≥ 10 mmol/L, and the distribution of zirconium element was uniform. Due to the coordination of Zr(IV) with ─COOH, ─NH2 and ─OH within collagen and the tighter entanglement of collagen nanofibrils, the elastic modulus and compressive strength of Zr(IV) mediated collagen nanofibrous hydrogel were 208.3 and 1103.0 kPa, which were approximate 77 and 12 times larger than those of pure collagen hydrogel, respectively. Moreover, the environmental stability such as thermostability, swelling ability and biodegradability got outstanding improvements and could be regulated by Zr(IV) concentration. Most importantly, the resultant hydrogel showed excellent biocompatibility and even accelerated cell proliferation.

Exposure to a mixture of metal(loid)s and sleep quality in pregnant women during early pregnancy: A cross-sectional study.

Biological characteristics of pregnant women during early pregnancy make them susceptible to both poor sleep quality and metal/metalloid exposure. However, the effects of metal(loid) exposure on sleep quality in pregnant women remain unknown and unexplored. We aimed to examine the relationship between exposure to a mixture of metal(loid)s and pregnant women's sleep quality during early pregnancy. We recruited 493 pregnant women in the first trimester from prenatal clinics in Jinan, Shandong Province, China, and collected their spot urine samples. All urine specimens were assessed for eight metal(loid)s: arsenic (As), cadmium (Cd), iron (Fe), zinc (Zn), molybdenum (Mo), lead (Pb), selenium (Se), and mercury (Hg). We used the Pittsburgh Sleep Quality Index (PSQI) to assess sleep quality. Linear regression, logistic regression, generalized additive models (GAMs), quantile g-computation, and Bayesian kernel machine regression (BKMR) were applied to investigate the relationships between metal(loid) exposure and sleep quality. The results from single metal(loid) models, quantile g-computation models, and BKMR models consistently suggested that Fe was positively related to women's sleep quality. Moreover, in the quantile g-computation models, As was the most critical contributor to the negative effects of the metal(loid) mixture on sleep quality. In addition, we found significant As by Fe interaction for scores of PSQI and habitual sleep efficiency, Pb by Fe interaction for PSQI and sleep latency, and Hg by Fe interaction for PSQI, suggesting the interactive effects of As and Fe, Pb and Fe, Hg and Fe on sleep quality and specific sleep components. Our study provided the first-hand evidence of the effects of metal(loid) exposure on pregnant women's sleep quality. The underlying mechanisms need to be explored in the future.

Quantifying the pollution changes and meteorological dependence of airborne trace elements coupling source apportionment and machine learning.

Airborne trace elements (TEs) present in atmospheric fine particulate matter (PM2.5) exert notable threats to human health and ecosystems. To explore the impact of meteorological conditions on shaping the pollution characteristics of TEs and the associated health risks, we quantified the variations in pollution characteristics and health risks of TEs due to meteorological impacts using weather normalization and health risk assessment models, and analyzed the source-specific contributions and potential sources of primary TEs affecting health risks using source apportionment approaches at four sites in Shandong Province from September to December 2021. Our results indicated that TEs experience dual effects from meteorological conditions, with a tendency towards higher TE concentrations and related health risks during polluted period, while the opposite occurred during clean period. The total non-carcinogenic and carcinogenic risks of TEs during polluted period increased approximately by factors of 0.53-1.74 and 0.44-1.92, respectively. Selenium (Se), manganese (Mn), and lead (Pb) were found to be the most meteorologically influenced TEs, while chromium (Cr) and manganese (Mn) were identified as the dominant TEs posing health risks. Enhanced emissions of multiple sources for Cr and Mn were found during polluted period. Depending on specific wind speeds, industrialized and urbanized centers, as well as nearby road dusts, could be key sources for TEs. This study suggested that attentions should be paid to not only the TEs from primary emissions but also the meteorology impact on TEs especially during pollution episodes to reduce health risks in the future.

The Impact of a Multidisciplinary Experiential Training Model on Knowledge, Attitude and Practice of Healthcare Workers in Maternity Health Management: A Preliminary Study.

Background: Maternity health management has always been the area of concern and considering, and considering its complexity and multidisciplinary, it is necessary to provide effective training for healthcare workers. Purpose: To evaluate the impact of a multidisciplinary experiential training model on the knowledge, attitude, and practice of healthcare workers in maternity health management. Patients and Methods: We conducted a novel educational model, Multidisciplinary Maternity Health Experiential Training based on Knowledge, Attitude and Practice (MMHET), which combined theoretical knowledge, practical skills, and human-centred humanistic care, offering a comprehensive offline education program supported by online teaching materials structured around knowledge graphs. Pre- and post-test surveys were used to assess the changes in participants' knowledge, attitudes, and practices. Results: From May to July 2023, a total of 322 participants attended the course, and only a small percentage had participated in experiential training. For all topics, the vast majority of participants endorsed the course, and the attitude content had the highest percentage of participants who said they agreed. Among the groups with different years of working life, the highest percentage of participants in the >20 years group strongly endorsed the course. Conclusion: The preliminary findings indicate that the MMHET model is well-received and feasible, demonstrating its potential to enhance maternity health management education.

Total Tumor ADC Histogram Analysis: A New Tool for Predicting High-Risk Cytogenetic Abnormalities in Multiple Myeloma Patients.

RATIONALE AND OBJECTIVES: We explored the feasibility of using total tumor apparent diffusion coefficient (ttADC) histogram parameters to predict high-risk cytogenetic abnormalities (HRCA) in patients with multiple myeloma (MM) and compared the performance of an image prediction model based on these parameters with that of a combined prediction model based on these parameters and clinical indicators. METHODS: We retrospectively analyzed the parameters of the ttADC histogram based on whole-body diffusion-weighted images(WB-DWI) and clinical indicators in 92 patients with MM. The patients were divided into HRCA and non-HRCA groups according to the results of the fluorescence in situ hybridization. Logistic regression analysis was used to construct the image prediction and combined prediction models. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to evaluate the performance of the models to identify HRCA. The DeLong test was used to compare the AUC differences of each prediction model. RESULTS: Logistic regression analysis results revealed that the ttADC histogram parameter, ttADC entropy < 7.959 (OR: 39.167; 95% confidence interval [CI]: 3.891-394.208; P < 0.05), was an independent risk factor for HRCA. The image prediction model consisted of ttADC entropy and ttADC SD. The combined prediction model included ttADC entropy along with patient clinical indicators such as biological sex and M protein percentage. The AUCs of the image prediction and combined prediction models were 0.739 and 0.811, respectively (P < .05). The image prediction model showed a sensitivity of 73.9% and a specificity of 68.1%. The combined prediction model showed 82.6% sensitivity and 72.5% specificity. CONCLUSIONS: Using ttADC histogram parameters based on WB-DWI images to predict HRCA in patients with MM is feasible, and combining ttADC parameters with clinical indicators can achieve better predictive performance.

Nicotinamide mitigates visceral leishmaniasis by regulating inflammatory response and enhancing lipid metabolism.

BACKGROUND: Currently, treatment regimens for visceral leishmaniasis (VL) are limited because of the presence of numerous adverse effects. Nicotinamide, a readily available and cost-effective vitamin, has been widely acknowledged for its safety profile. Several studies have demonstrated the anti-leishmanial effects of nicotinamide in vitro. However, the potential role of nicotinamide in Leishmania infection in vivo remains elusive. METHODS: In this study, we assessed the efficacy of nicotinamide as a therapeutic intervention for VL caused by Leishmania infantum in an experimental mouse model and investigated its underlying molecular mechanisms. The potential molecular mechanism was explored through cytokine analysis, examination of spleen lymphocyte subsets, liver RNA-seq analysis, and pathway validation. RESULTS: Compared to the infection group, the group treated with nicotinamide demonstrated significant amelioration of hepatosplenomegaly and recovery from liver pathological damage. The NAM group exhibited parasite reduction rates of 79.7% in the liver and 86.7% in the spleen, respectively. Nicotinamide treatment significantly reduced the activation of excessive immune response in infected mice, thereby mitigating hepatosplenomegaly and injury. Furthermore, nicotinamide treatment enhanced fatty acid ß-oxidation by upregulating key enzymes to maintain lipid homeostasis. CONCLUSIONS: Our findings provide initial evidence supporting the safety and therapeutic efficacy of nicotinamide in the treatment of Leishmania infection in BALB/c mice, suggesting its potential as a viable drug for VL.

Development of a quantitative index system for evaluating the quality of electronic medical records in disease risk intelligent prediction.

OBJECTIVE: This study aimed to develop and validate a quantitative index system for evaluating the data quality of Electronic Medical Records (EMR) in disease risk prediction using Machine Learning (ML). MATERIALS AND METHODS: The index system was developed in four steps: (1) a preliminary index system was outlined based on literature review; (2) we utilized the Delphi method to structure the indicators at all levels; (3) the weights of these indicators were determined using the Analytic Hierarchy Process (AHP) method; and (4) the developed index system was empirically validated using real-world EMR data in a ML-based disease risk prediction task. RESULTS: The synthesis of review findings and the expert consultations led to the formulation of a three-level index system with four first-level, 11 second-level, and 33 third-level indicators. The weights of these indicators were obtained through the AHP method. Results from the empirical analysis illustrated a positive relationship between the scores assigned by the proposed index system and the predictive performances of the datasets. DISCUSSION: The proposed index system for evaluating EMR data quality is grounded in extensive literature analysis and expert consultation. Moreover, the system's high reliability and suitability has been affirmed through empirical validation. CONCLUSION: The novel index system offers a robust framework for assessing the quality and suitability of EMR data in ML-based disease risk predictions. It can serve as a guide in building EMR databases, improving EMR data quality control, and generating reliable real-world evidence.

Enzymatic Synthesis of Novel Terpenoid Glycoside Derivatives Decorated with N-Acetylglucosamine Catalyzed by UGT74AC1.

The addition of the O-linked N-acetylglucosamine (O-GlcNAc) is a significant modification for active molecules, such as proteins, carbohydrates, and natural products. However, the synthesis of terpenoid glycoside derivatives decorated with GlcNAc remains a challenging task due to the absence of glycosyltransferases, key enzymes for catalyzing the transfer of GlcNAc to terpenoids. In this study, we demonstrated that the enzyme mutant UGT74AC1T79Y/L48M/R28H/L109I/S15A/M76L/H47R efficiently transferred GlcNAc from uridine diphosphate (UDP)-GlcNAc to a variety of terpenoids. This powerful enzyme was employed to synthesize GlcNAc-decorated derivatives of terpenoids, including mogrol, steviol, andrographolide, protopanaxadiol, glycyrrhetinic acid, ursolic acid, and betulinic acid for the first time. To unravel the mechanism of UDP-GlcNAc recognition, we determined the X-ray crystal structure of the inactivated mutant UGT74AC1His18A/Asp111A in complex with UDP-GlcNAc at a resolution of 1.66 Å. Through molecular dynamic simulation and activity analysis, we revealed the molecular mechanism and catalytically important amino acids directly involved in the recognition of UDP-GlcNAc. Overall, this study not only provided a potent biocatalyst capable of glycodiversifying natural products but also elucidated the structural basis for UDP-GlcNAc recognition by glycosyltransferases.

[Construction and culture condition optimization of a Saccharomyces cerevisiae strain for production of galactitol].

Galactitol, a rare sugar alcohol, has promising potential in the food industry and pharmaceutical field. The available industrial production methods rely on harsh hydrogenation processes, which incur high costs and environmental concerns. It is urgent to develop environmentally friendly and efficient biosynthesis technologies. In this study, a xylose reductase named AnXR derived from Aspergillus niger CBS 513.88 was identified and characterized for the enzymatic properties. AnXR exhibited the highest activity at 25 ℃ and pH 8.0, and it belonged to the NADPH-dependent aldose reductase family. To engineer a strain for galactitol production, we deleted the galactokinase (GAL1) gene in Saccharomyes cerevisiae by using the recombinant gene technology, which significantly reduced the metabolic utilization of D-galactose by host cells. Subsequently, we introduced the gene encoding AnXR into this modified strain, creating an engineered strain capable of catalyzing the conversion of D-galactose into galactitol. Furthermore, we optimized the whole-cell catalysis conditions for the engineered strain, which achieved a maximum galactitol yield of 12.10 g/L. Finally, we tested the reduction ability of the strain for other monosaccharides and discovered that it could produce functional sugar alcohols such as xylitol and arabinitol. The engineered strain demonstrates efficient biotransformation capabilities for galactitol and other functional sugar alcohols, representing a significant advancement in environmentally sustainable production practices.

Characterization of VOCs emissions and associated health risks inherent to the packaging and printing industries in Shandong Province, China.

Comprehensive volatile organic compounds (VOCs) emission control is imperative to decreasing occupational health risks and environmental impact of the packaging and printing industries. In this work, we investigated the VOCs emission characteristics and concentrations of individual contaminants generated by the packaging and printing industries, with regard to various categories, processes, and geographic regions. VOCs emissions, ozone formation potential (OFP), and associated health risks were assessed at 10 representative packaging and printing firms across several cities in Shandong Province, China. Plastic packaging enterprises had the greatest levels of unorganized VOCs emissions, consisting predominantly of oxygenated volatile organic compounds (OVOCs), followed by alkanes and halocarbons. From metal and paper packaging enterprises, OVOCs, alkanes, and aromatics were significant components of unorganized VOCs emissions. Aromatics, halocarbons, and OVOCs contributed significantly to OFP in workshops. The potential carcinogenic risk associated with VOCs in the packaging and printing industries was not significant. However, according to the findings in this study, the workshop environment may provide a comparatively elevated non-carcinogenic risk attributable to ethyl acetate, isopropanol, acrolein, 1,1,2-Trichloroethane, 1,2-Dichloropropane, and naphthalene exposure. In particular, the endocrine-disrupting and genetic toxic effects caused by benzene, toluene, styrene, and naphthalene should not be overlooked. Thus, it is essential to provide precedence to the working environment conditions of workshop laborers, while also undertaking scientific and systematic measures to mitigate the detrimental impacts of VOCs on the environment and human welfare.

Alginate nanogel-embedded liposomal drug carriers facilitate drug delivery efficiency in arthritis treatment.

Despite numerous advantages of liposomes in treating rheumatoid arthritis (RA), the in vivo stability remains a critical issue. Current strategies for improving liposomal stability often compromise their original properties. Herein, we designed an alginate nanogel-embedded liposome aiming at retaining those inherent advantages while enhancing their in vivo stability. The introduction of alginate network within the liposome core can provide mechanical support and controlled drug release without affecting the surface properties. Results showed the cross-linking of alginate network within the inner core of liposomes elevated the particle rigidity to 3 times, allowing for improved stability and decreased drug leakage. Moreover, this nanogel-embedded liposome with optimized elasticity obviously facilitated cellular uptake in inflammatory macrophages. When entering blood circulation, increased rigidity altered the composition of protein corona on the particle surface, resulting in 2-fold increase in circulation time and improved drug accumulation in arthritic joints. When anti-inflammatory chlorogenic acid (CA) was encapsulated into the nanogel network, this CA-loaded nanogel-embedded liposome significantly inhibited ROS production and inflammatory response, ultimately achieved superior therapeutic outcome in arthritic rats. Results demonstrated that this nanogel-embedded liposomes can essentially retain the inherent advantages and overcome the drawbacks of liposomes, thereby improving the drug delivery efficiency.

Grape seed extract prevents oestrogen deficiency-induced bone loss by modulating the gut microbiota and metabolites.

Proanthocyanidin-rich grape seed extract (GSE) has been shown to have the potential to protect bones, although the underlying mechanism remains unknown. The current study aims to explore GSE's preventive and therapeutic impact on bone loss induced by oestrogen deficiency and the underlying mechanism through the gut microbiota (GM) and metabolomic responses. In oestrogen-deficient ovariectomized (OVX) mice, GSE ameliorated bone loss by inhibiting the expansion of bone marrow adipose tissue (BMAT), restoring BMAT lipolysis and promoting bone formation. GSE regulated OVX-induced GM dysbiosis by reducing the abundance of opportunistic pathogenic bacteria, such as Alistipes, Turicibacter and Romboutsia, while elevating the abundance of beneficial bacteria, such as Bifidobacterium. The modified GM primarily impacted lipid and amino acid metabolism. Furthermore, the serum metabolites of GSE exhibited a significant enrichment in lipid metabolism. In summary, GSE shows potential as a functional food for preventing oestrogen deficiency-induced bone loss by modulating GM and metabolite-mediated lipid metabolism.

Potential anti-liver cancer targets and mechanisms of kaempferitrin based on network pharmacology, molecular docking and experimental verification.

AIM: Kaempferitrin is an active component in Chenopodium ambrosioides, showing medicinal functions against liver cancer. This study aimed to identify the potential targets and pathways of kaempferitrin against liver cancer using network pharmacology and molecular docking, and verify the essential hub targets and pathway in mice model of SMMC-7721 cells xenografted tumors and SMMC-7721 cells. METHODS: Kaempferitrin therapeutical targets were obtained by searching SwissTargetPrediction, PharmMapper, STITCH, DrugBank, and TTD databases. Liver cancer specific genes were obtained by searching GeneCards, DrugBank, TTD, OMIM, and DisGeNET databases. PPI network of "kaempferitrin-targets-liver cancer" was constructed to screen the hub targets. GO, KEGG pathway and MCODE clustering analyses were performed to identify possible enrichment of genes with specific biological subjects. Molecular docking and molecular dynamics simulation were employed to determine the docking pose, potential and stability of kaempferitrin with hub targets. The potential anti-liver cancer mechanisms of kaempferitrin, as predicted by network pharmacology analyses, were verified by in vitro and in vivo experiments. RESULTS: 228 kaempferitrin targets and 2186 liver cancer specific targets were identified, of which 50 targets were overlapped. 8 hub targets were identified through network topology analysis, and only SIRT1 and TP53 had a potent binding activity with kaempferitrin as indicated by molecular docking and molecular dynamics simulation. MCODE clustering analysis revealed the most significant functional module of PPI network including SIRT1 and TP53 was mainly related to cell apoptosis. GO and KEGG enrichment analyses suggested that kaempferitrin exerted therapeutic effects on liver cancer possibly by promoting apoptosis via p21/Bcl-2/Caspase 3 signaling pathway, which were confirmed by in vivo and in vitro experiments, such as HE staining of tumor tissues, CCK-8, qRT-PCR and Western blot. CONCLUSION: This study provided not only insight into how kaempferitrin could act against liver cancer by identifying hub targets and their associated signaling pathways, but also experimental evidence for the clinical use of kaempferitrin in liver cancer treatment.

A novel higher performance nomogram based on explainable machine learning for predicting mortality risk in stroke patients within 30 days based on clinical features on the first day ICU admission.

BACKGROUND: This study aimed to develop a higher performance nomogram based on explainable machine learning methods, and to predict the risk of death of stroke patients within 30 days based on clinical characteristics on the first day of intensive care units (ICU) admission. METHODS: Data relating to stroke patients were extracted from the Medical Information Marketplace of the Intensive Care (MIMIC) IV and III database. The LightGBM machine learning approach together with Shapely additive explanations (termed as explain machine learning, EML) was used to select clinical features and define cut-off points for the selected features. These selected features and cut-off points were then evaluated using the Cox proportional hazards regression model and Kaplan-Meier survival curves. Finally, logistic regression-based nomograms for predicting 30-day mortality of stroke patients were constructed using original variables and variables dichotomized by cut-off points, respectively. The performance of two nomograms were evaluated in overall and individual dimension. RESULTS: A total of 2982 stroke patients and 64 clinical features were included, and the 30-day mortality rate was 23.6% in the MIMIC-IV datasets. 10 variables ("sofa (sepsis-related organ failure assessment)", "minimum glucose", "maximum sodium", "age", "mean spo2 (blood oxygen saturation)", "maximum temperature", "maximum heart rate", "minimum bun (blood urea nitrogen)", "minimum wbc (white blood cells)" and "charlson comorbidity index") and respective cut-off points were defined from the EML. In the Cox proportional hazards regression model (Cox regression) and Kaplan-Meier survival curves, after grouping stroke patients according to the cut-off point of each variable, patients belonging to the high-risk subgroup were associated with higher 30-day mortality than those in the low-risk subgroup. The evaluation of nomograms found that the EML-based nomogram not only outperformed the conventional nomogram in NIR (net reclassification index), brier score and clinical net benefits in overall dimension, but also significant improved in individual dimension especially for low "maximum temperature" patients. CONCLUSIONS: The 10 selected first-day ICU admission clinical features require greater attention for stroke patients. And the nomogram based on explainable machine learning will have greater clinical application.

Morphometric brain organization across the human lifespan reveals increased dispersion linked to cognitive performance.

The human brain is organized as segregation and integration units and follows complex developmental trajectories throughout life. The cortical manifold provides a new means of studying the brain's organization in a multidimensional connectivity gradient space. However, how the brain's morphometric organization changes across the human lifespan remains unclear. Here, leveraging structural magnetic resonance imaging scans from 1,790 healthy individuals aged 8 to 89 years, we investigated age-related global, within- and between-network dispersions to reveal the segregation and integration of brain networks from 3D manifolds based on morphometric similarity network (MSN), combining multiple features conceptualized as a "fingerprint" of an individual's brain. Developmental trajectories of global dispersion unfolded along patterns of molecular brain organization, such as acetylcholine receptor. Communities were increasingly dispersed with age, reflecting more disassortative morphometric similarity profiles within a community. Increasing within-network dispersion of primary motor and association cortices mediated the influence of age on the cognitive flexibility of executive functions. We also found that the secondary sensory cortices were decreasingly dispersed with the rest of the cortices during aging, possibly indicating a shift of secondary sensory cortices across the human lifespan from an extreme to a more central position in 3D manifolds. Together, our results reveal the age-related segregation and integration of MSN from the perspective of a multidimensional gradient space, providing new insights into lifespan changes in multiple morphometric features of the brain, as well as the influence of such changes on cognitive performance.

Molybdenum Disulfide-Supported Cuprous Oxide Nanocomposite for Near-Infrared-I Light-Responsive Synergistic Antibacterial Therapy.

Drug-resistant bacterial infections pose a serious threat to human health; thus, there is an increasingly growing demand for nonantibiotic strategies to overcome drug resistance in bacterial infections. Mild photothermal therapy (PTT), as an attractive antibacterial strategy, shows great potential application due to its good biocompatibility and ability to circumvent drug resistance. However, its efficiency is limited by the heat resistance of bacteria. Herein, Cu2O@MoS2, a nanocomposite, was constructed by the in situ growth of Cu2O nanoparticles (NPs) on the surface of MoS2 nanosheets, which provided a controllable photothermal therapeutic effect of MoS2 and the intrinsic catalytic properties of Cu2O NPs, achieving a synergistic effect to eradicate multidrug-resistant bacteria. Transcriptome sequencing (RNA-seq) results revealed that the antibacterial process was related to disrupting the membrane transport system, phosphorelay signal transduction system, oxidative stress response system, as well as the heat response system. Animal experiments indicated that Cu2O@MoS2 could effectively treat wounds infected with methicillin-resistant Staphylococcus aureus. In addition, satisfactory biocompatibility made Cu2O@MoS2 a promising antibacterial agent. Overall, our results highlight the Cu2O@MoS2 nanocomposite as a promising solution to combating resistant bacteria without inducing the evolution of antimicrobial resistance.

Natural product nanozymes of herbal extract galangin in managing hepatocellular carcinoma.

Numerous local herbal extract species have been investigated as potential medicinal ingredients due to their promising anti-cancer properties. However, the primary constraint of the class of plant flavonoids lies in their low solubility and limited membrane permeability, leading to chemical instability and restricted bioavailability that impede biomedical applications. In this study, we have developed an ideal nanozyme-Galazyme, comprising galangin-loaded copper Nanozyme coated by DSPE-PEG, which amplifies oxidative stress to induce apoptosis via the regulation of reactive oxygen species (ROS) generation and mitogen-activated protein kinase (MAPK) activation. Galazyme exhibited significant peroxidase mimetic activity, demonstrating its potential to generate ROS and elevate oxidative stress. Upon uptake by HepG-2 cells, Galazyme efficiently converts excess hydrogen peroxide (H2O2) into highly reactive •OH radicals and upregulates MAPK expression, leading to the activation of Bax and Caspase 3, thereby promoting irreversible tumor cell apoptosis. Both in vitro and in vivo results demonstrate that Galazyme inhibits tumor cell growth and induces apoptosis by generating ample ROS and activating the MAPK pathway. Our study offers novel evidence supporting the enhancement of Galazyme-induced apoptosis through the upregulation of Bax and Caspase 3, along with the elucidation of the interaction between MAPK and apoptosis.

Prevalence and Burden of Multiple Sclerosis in China, 1990-2019: Findings From the Global Burden of Disease Study 2019.

BACKGROUND AND OBJECTIVES: Multiple sclerosis (MS) is the leading cause of neurologic disability in young adults, but the burden caused by MS in China is lacking. We aimed to comprehensively describe the prevalence and health loss due to MS by demographic and geographical variables from 1990 to 2019 across China. METHODS: Data were obtained from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019 (GBD 2019). We used GBD methodology to systematically analyze the prevalence, disability-adjusted life-years (DALYs), years of life lost (YLLs), and years lived with disability (YLDs) due to MS by age, sex, and location from 1990 to 2019 in mainland China and its provinces. We also compared the MS burden in China with the world and other Group of 20 (G20) countries. RESULTS: In 2019, 42,571 (95% uncertainty interval [UI] 33,001-53,329) individuals in China had MS, which doubled from 1990. The age-standardized prevalence rate of MS was 2.32 per 100,000 (95% UI 1.78-2.91), which increased by 23.31% (95% UI 20.50-25.89) from 1990, with most of the growth occurring after 2010. There was a positive latitudinal gradient with the increasing prevalence from south to north across China. The total DALYs caused by MS were 71,439 (95% UI 58,360-92,254) in 2019, ranking China third among G20 countries. Most of the MS burden in China derived from premature mortality, with the higher fraction of YLLs than that at the global level and most other G20 countries. From 1990 to 2019, the age-standardized DALY and YLL rate had nonsignificant changes; however, the age-standardized YLD rate substantially increased by 23.33% (95% UI 20.50-25.89). The geographic distribution of MS burden varied at the provincial level in China, with a slight downward trend in the age-standardized DALY rates along with increasing Socio-Demographic Index over the study period. DISCUSSION: Although China has a low risk of MS, the substantial and increasing prevalent cases should not be underestimated. The high burden due to premature death and geographic disparity of MS burden reveals insufficient management of MS in China, highlighting the needs for increased awareness and effective intervention.

Developing a Personalized Meal Recommendation System for Chinese Older Adults: Observational Cohort Study.

BACKGROUND: China's older population is facing serious health challenges, including malnutrition and multiple chronic conditions. There is a critical need for tailored food recommendation systems. Knowledge graph-based food recommendations offer considerable promise in delivering personalized nutritional support. However, the integration of disease-based nutritional principles and preference-related requirements needs to be optimized in current recommendation processes. OBJECTIVE: This study aims to develop a knowledge graph-based personalized meal recommendation system for community-dwelling older adults and to conduct preliminary effectiveness testing. METHODS: We developed ElCombo, a personalized meal recommendation system driven by user profiles and food knowledge graphs. User profiles were established from a survey of 96 community-dwelling older adults. Food knowledge graphs were supported by data from websites of Chinese cuisine recipes and eating history, consisting of 5 entity classes: dishes, ingredients, category of ingredients, nutrients, and diseases, along with their attributes and interrelations. A personalized meal recommendation algorithm was then developed to synthesize this information to generate packaged meals as outputs, considering disease-related nutritional constraints and personal dietary preferences. Furthermore, a validation study using a real-world data set collected from 96 community-dwelling older adults was conducted to assess ElCombo's effectiveness in modifying their dietary habits over a 1-month intervention, using simulated data for impact analysis. RESULTS: Our recommendation system, ElCombo, was evaluated by comparing the dietary diversity and diet quality of its recommended meals with those of the autonomous choices of 96 eligible community-dwelling older adults. Participants were grouped based on whether they had a recorded eating history, with 34 (35%) having and 62 (65%) lacking such data. Simulation experiments based on retrospective data over a 30-day evaluation revealed that ElCombo's meal recommendations consistently had significantly higher diet quality and dietary diversity compared to the older adults' own selections (P<.001). In addition, case studies of 2 older adults, 1 with and 1 without prior eating records, showcased ElCombo's ability to fulfill complex nutritional requirements associated with multiple morbidities, personalized to each individual's health profile and dietary requirements. CONCLUSIONS: ElCombo has shown enhanced potential for improving dietary quality and diversity among community-dwelling older adults in simulation tests. The evaluation metrics suggest that the food choices supported by the personalized meal recommendation system surpass autonomous selections. Future research will focus on validating and refining ElCombo's performance in real-world settings, emphasizing the robust management of complex health data. The system's scalability and adaptability pinpoint its potential for making a meaningful impact on the nutritional health of older adults.

Artificial intelligence for diagnosis and prognosis prediction of natural killer/T cell lymphoma using magnetic resonance imaging.

Accurate diagnosis and prognosis prediction are conducive to early intervention and improvement of medical care for natural killer/T cell lymphoma (NKTCL). Artificial intelligence (AI)-based systems are developed based on nasopharynx magnetic resonance imaging. The diagnostic systems achieve areas under the curve of 0.905-0.960 in detecting malignant nasopharyngeal lesions and distinguishing NKTCL from nasopharyngeal carcinoma in independent validation datasets. In comparison to human radiologists, the diagnostic systems show higher accuracies than resident radiologists and comparable ones to senior radiologists. The prognostic system shows promising performance in predicting survival outcomes of NKTCL and outperforms several clinical models. For patients with early-stage NKTCL, only the high-risk group benefits from early radiotherapy (hazard ratio = 0.414 vs. late radiotherapy; 95% confidence interval, 0.190-0.900, p = 0.022), while progression-free survival does not differ in the low-risk group. In conclusion, AI-based systems show potential in assisting accurate diagnosis and prognosis prediction and may contribute to therapeutic optimization for NKTCL.