Differential Associations of Erythrocyte Membrane Saturated Fatty Acids with Glycemic and Lipid Metabolic Markers in a Chinese Population: A Cross-Sectional Study.

Mounting evidence indicates a complex link between circulating saturated fatty acids (SFAs) and cardiovascular disease (CVD) risk factors, but research on erythrocyte membrane SFA associations with metabolic markers remains limited. Our study sought to investigate the correlations between erythrocyte membrane SFAs and key metabolic markers within glycemic and lipid metabolism in a Chinese population of 798 residents aged 41 to 71 from Guangzhou. Using gas chromatography-mass spectrometry, we assessed the erythrocyte membrane saturated fatty acid profile and performed multiple linear regression to evaluate the relationship between different SFA subtypes and metabolic markers. Our findings revealed that the odd-chain SFA group (C15:0 + C17:0) exhibited negative associations with fasting blood glucose (FBG), homeostatic model assessment for insulin resistance (HOMA-IR), and triglycerides (TG). Conversely, the very-long-chain SFA group (C20:0 + C22:0 + C23:0 + C24:0) exhibited positive associations with fasting insulins (FINS), HOMA-IR, total cholesterol (TC), and low-density lipoprotein cholesterol (LDL-C). Furthermore, there was no evidence supporting an association between the even-chain group (C14:0 + C16:0 + C18:0) and metabolic markers. Our findings suggest that different subtypes of SFAs have diverse effects on glycemic and lipid metabolic markers, with odd-chain SFAs associated with a lower metabolic risk. However, the results concerning the correlations between even-chain SFAs and very-long-chain SFAs with markers of glycemic and lipid metabolism pathways are confusing, highlighting the necessity for further exploration and investigation.

Prediction of influenza outbreaks in Fuzhou, China: comparative analysis of forecasting models.

BACKGROUND: Influenza is a highly contagious respiratory disease that presents a significant challenge to public health globally. Therefore, effective influenza prediction and prevention are crucial for the timely allocation of resources, the development of vaccine strategies, and the implementation of targeted public health interventions. METHOD: In this study, we utilized historical influenza case data from January 2013 to December 2021 in Fuzhou to develop four regression prediction models: SARIMA, Prophet, Holt-Winters, and XGBoost models. Their predicted performance was assessed by using influenza data from the period from January 2022 to December 2022 in Fuzhou. These models were used for fitting and prediction analysis. The evaluation metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), were employed to compare the performance of these models. RESULTS: The results indicate that the epidemic of influenza in Fuzhou exhibits a distinct seasonal and cyclical pattern. The influenza cases data displayed a noticeable upward trend and significant fluctuations. In our study, we employed SARIMA, Prophet, Holt-Winters, and XGBoost models to predict influenza outbreaks in Fuzhou. Among these models, the XGBoost model demonstrated the best performance on both the training and test sets, yielding the lowest values for MSE, RMSE, and MAE among the four models. CONCLUSION: The utilization of the XGBoost model significantly enhances the prediction accuracy of influenza in Fuzhou. This study makes a valuable contribution to the field of influenza prediction and provides substantial support for future influenza response efforts.

Red cell distribution width to total serum calcium ratio and in-hospital mortality risk in patients with acute ischemic stroke: A MIMIC-IV retrospective analysis.

We investigated the relationship among red cell distribution width (RDW), to total serum calcium (TSC) ratio (RCR), and in-hospital mortality in patients with acute ischemic stroke (AIS). This study was a retrospective analysis. The data of 2700 AIS patients was retrospectively analyzed from the Medical Information Mart for Intensive Care database (version IV). The main outcome of interest was in-hospital mortality. A Cox proportional hazards regression model was used to determine whether RCR was independently associated with in-hospital mortality. The Kaplan-Meier method was used to plot the survival curves for RCR. Subgroup analyses were performed to measure the mortality across various subgroups. The area under curve (AUC) of receiver operating characteristic curve (ROC) was calculated to ascertain the quality of RCR as a predictor of in-hospital mortality in patients with AIS. In the multivariate analysis, statistically significant differences were identified in age, ethnicity, length of ICU stay, mechanical ventilation, sequential organ failure assessment (SOFA) score, RDW, hemoglobin, RCR, whether taking anticoagulants, hyperlipidemia, and atrial fibrillation (P < .05). A threshold inflection point value of 1.83 was obtained through a two-piecewise regression model. There was a non-linear relationship between RCR and hospital mortality in patients with AIS. The hazard ratio (HR) and the 95% confidence intervals (CI) on the right and left of the inflection point were 0.93 (0.57-1.51; P = .7660) and 2.96 (1.37-6.42; P = .0060), respectively. The Kaplan-Meier curve indicated that survival rates were higher when RCR was ≤ 1.83 and lower when RDW was > 1.83 after adjustment for age, gender, BMI, ethnicity. The area under curve (AUC) of RCR was 0.715. A higher RCR was associated with an increased risk of in-hospital mortality in patients with AIS.

Visible-Light-Induced Regioselective Radical-Polar Crossover 1,4-Hydrophosphinylation of 1,3-Enynes: Access to Trisubstituted Allenes Bearing a Phosphine Oxide Group.

The radical 1,4-functionalizations of 1,3-enynes have emerged as a powerful strategy for the synthesis of multisubstituted allenes. However, the phosphorus-centered radical-initiated transformations remain largely elusive. Herein, visible-light photoredox catalytic regioselective radical hydrophosphinylation of 1,3-enynes with diaryl phosphine oxides as phosphinoyl radical precursors has been realized. This protocol features mild conditions, a wide substrate scope, and good functional group tolerance, producing a diverse range of phosphinoyl-substituted allenes in moderate to good yields with high atom economy. Detailed mechanistic experiments revealed a radical-polar crossover process in the reaction.

Association between PM2.5 constituents and cardiometabolic risk factors: Exploring individual and combined effects, and mediating inflammation.

BACKGROUND: The individual and combined effects of PM2.5 constituents on cardiometabolic risk factors are sparsely investigated. Besides, the key cardiometabolic risk factor that PM2.5 constituents targeted and the biological mechanisms remain unclear. METHOD: A multistage, stratified cluster sampling survey was conducted in two typically air-polluted Chinese cities. The PM2.5 and its constituents including sulfate, nitrate, ammonium, organic matter, and black carbon were predicted using a machine learning model. Twenty biomarkers in three category were simultaneously adopted as cardiometabolic risk factors. We explored the individual and mixture association of long-term PM2.5 constituents with these markers using generalized additive model and quantile-based g-computation, respectively. To minimize potential confounding effects, we accounted for covariates including demographic, lifestyle, meteorological, temporal trends, and disease-related information. We further used ROC curve and mediation analysis to identify the key subclinical indicators and explore whether inflammatory mediators mediate such association, respectively. RESULT: PM2.5 constituents was positively correlated with HOMA-B, TC, TG, LDL-C and LCI, and negatively correlated with PP and RC. Further, PM2.5 constituent mixture was positive associated with DBP, MAP, HbA1c, HOMA-B, AC, CRI-1 and CRI-2, and negative associated with PP and HDL-C. The ROC analysis further reveals that multiple cardiometabolic risk factors can collectively discriminate exposure to PM2.5 constituents (AUC>0.9), among which PP and CRI-2 as individual indicators exhibit better identifiable performance for nitrate and ammonium (AUC>0.75). We also found that multiple blood lipid indicators may be affected by PM2.5 and its constituents, possibly mediated through complement C3 or hsCRP. CONCLUSION: Our study suggested associations of individual and combined PM2.5 constituents exposure with cardiometabolic risk factors. PP and CRI-2 were the targeted markers of long-term exposure to nitrate and ammonium. Inflammation may serve as a mediating factor between PM2.5 constituents and dyslipidemia, which enhance current understanding of potential pathways for PM2.5-induced preclinical cardiovascular responses.

Ammonia stripping by in situ biogas self-circulation to upgrade continuous thermophilic and mesophilic digestion of hydrothermal high-solid sludge.

High-solid anaerobic digestion of hydrothermal sewage sludge has been developed. In order to upgrade the process by focusing on ammonia inhibition, a simply-equipped stripping system without additional alkali or heat supply was introduced by in situ biogas self-circulation. As the determined limit of total ammonia nitrogen at 1500 mg/L and 1000 mg/L for the mesophilic (MAD) and thermophilic anaerobic digestion (TAD) respectively and stripping rate at 5 L/min, continuous MAD and TAD was conducted in parallel. The stripping system successfully polished up the ammonia inhibition, and methanogenic capability of the TAD was promoted to approximately 90.0 % of the potential. Intermittent stripping mode proved usable. More frequent stripping was inevitable for the TAD as compared to the MAD. Hydraulic retention time below 20 d resulted in failure of the stripping mode due to rapid ammonia generation. Overall, this technology was practical in upgrading high-solid sludge digestion by effective ammonia control.

Comorbidities among adult patients with psoriasis in Tianjin: a cross-sectional analysis of the Health Database study.

OBJECTIVES: This study aims to examine the prevalence of comorbidities in adult patients with psoriasis and compare them with those in control subjects without psoriasis in Tianjin, China. DESIGN: The study is a cross-sectionalanalysis. PARTICIPANTS: The participants were established by identifying all patients (age ≥18 years) who visited hospitals and clinics in Tianjin between 1 January 2016 and 31 October 2019. SETTING: The study group consisted of 20 678 adult patients with psoriasis, and a comparison group was created after 1:1 propensity score matching. Logistic regression analyses were conducted to examine the risk of 22 comorbidities for these two groups. RESULTS: Patients with psoriasis had a significantly higher prevalence of 11 comorbidities and a lower prevalence of 2 comorbidities within 12 months of follow-up. Our results also showed that the proportion of psoriatic arthritis might account for approximately 2% of all patients with psoriasis. This psoriatic arthritis group had a higher average age and CCI (Charlson Comorbidity Index) index score (2.27 >1.62, p <0.001) than the non-arthritis group. CONCLUSIONS: This study showed that psoriasis in Tianjin is associated with various comorbidities. It also emphasises the importance of clinical treatment in improving therapeutic effects and reducing the burden of psoriasis in China.

IL-4 Licenses B Cell Activation Through Cholesterol Synthesis.

Lymphocyte activation involves a transition from quiescence and associated catabolic metabolism to a metabolic state with noted similarities to cancer cells such as heavy reliance on aerobic glycolysis for energy demands and increased nutrient requirements for biomass accumulation and cell division 1-3 . Following antigen receptor ligation, lymphocytes require spatiotemporally distinct "second signals". These include costimulatory receptor or cytokine signaling, which engage discrete programs that often involve remodeling of organelles and increased nutrient uptake or synthesis to meet changing biochemical demands 4-6 . One such signaling molecule, IL-4, is a highly pleiotropic cytokine that was first identified as a B cell co-mitogen over 30 years ago 7 . However, how IL-4 signaling mechanistically supports B cell proliferation is incompletely understood. Here, using single cell RNA sequencing we find that the cholesterol biosynthetic program is transcriptionally upregulated following IL-4 signaling during the early B cell response to influenza virus infection, and is required for B cell activation in vivo . By limiting lipid availability in vitro , we determine cholesterol to be essential for B cells to expand their endoplasmic reticulum, progress through cell cycle, and proliferate. In sum, we demonstrate that the well-known ability of IL-4 to act as a B cell growth factor is through a previously unknown rewiring of specific lipid anabolic programs, relieving sensitivity of cells to environmental nutrient availability.

Imrecoxib: Advances in Pharmacology and Therapeutics.

Imrecoxib, a cyclooxygenase-2 (COX-2) selective non-steroidal anti-inflammatory drug (NSAID), was discovered via the balanced inhibition strategy of COX-1/COX-2. It is indicated for the relief of painful symptoms of osteoarthritis. There have been some pharmacological and therapeutic advances since the approval of imrecoxib in 2011. However, an update review in this aspect is not yet available. Relevant literature until January 2024 was identified by search of PubMed, Web of science, Embase and CNKI. From the perspective of efficacy, imrecoxib provides relief of osteoarthritis symptoms, and potential off-label use for treatment of idiopathic pulmonary fibrosis, perioperative pain, hand-foot syndrome, axial spondyloarthritis, COVID-19, cartilage injury, and malignancies such as lung and colon cancer. From a safety point of view, imrecoxib showed adverse effects common to NSAIDs; however, it has lower incidence of new-onset hypertension than other types of selective COX-2 inhibitors, less gastrointestinal toxicities than non-selective NSAIDs, weaker risk of drug interaction than celecoxib, and more suitable for elderly patients due to balanced inhibition of COX-1/COX-2. From a pharmacoeconomic perspective, imrecoxib is more cost-effective than celecoxib and diclofenac for osteoarthritis patients. With the deepening of the disease pathophysiology study of osteoarthritis, new therapeutic schemes and pharmacological mechanisms are constantly discovered. In the field of osteoarthritis treatment, mechanisms other than the analgesic and anti-inflammatory effects of COX-2 inhibitors are also being explored. Taken together, imrecoxib is a moderate selective COX-2 inhibitor with some advantages, and there would be more clinical applications and research opportunities in the future.

Upregulation of collagen type X alpha 1 promotes the progress of triple-negative breast cancer via Wnt/ß-catenin signaling.

Triple-negative breast cancer (TNBC) is a malignant tumor with high degree of malignancy and lack of effective target treatment. The research aims to explore the role and mechanism of X collagen alpha-1 chain protein (COL10A1 gene) in TNBC. UALCAN and Kaplan-Meier were used to detect the expression of COL10A1 and its role in the prognosis of breast cancer patients. The cells with stably expressing high levels of COL10A1 were obtained by recombinant lentivirus infection. The expression of COL10A1 in cells was temporarily downregulated by siRNA interference fragments. Real-time quantitative polymerase chain reaction and western blot analysis were utilized to detect the changes of COL10A1 mRNA and protein expression. The biological functions of the cells were evaluated by colony formation, cell counting kit-8, cell invasion and wound healing experiments. In addition, the effect of COL10A1 on angiogenesis was investigated by tube formation assay. Xenograft tumor model was used to confirm the effect of COL10A1 on tumorigenicity in vivo and multiplex fluorescent immunohistochemistry to detect multiple proteins simultaneously. The possible molecular mechanism of the function of COL10A1 was speculated through the detection of proteins in functionally related pathways. COL10A1 is highly expressed and is significantly associated with worse overall survival (OS) and recurrence-free survival (RFS) in TNBC. Overexpression of COL10A1 increased the clone formation rate and cell migration capacity of TNBC cells. In the COL10A1 overexpression group, the clone formation rates of MD-MB-231 and BT-549 cells (21.5 ± 0.62, 27.83 ± 3.72)% were significantly higher than those in the control group(15.23 ± 2.79, 19.4 ± 1.47)%, and the relative migration ratio (47.40 ± 3.09, 41.26 ± 4.33)% were higher than those in the control group (34.48 ± 2.03, 21.80 ± 1.03)%. When the expression of COL10A1 was downregulated, the ability of clone formation and wound-healing migration capacity in TNBC cells was weakened. Upregulated COL10A1 in TNBC cells generated more junctions and longer total segments between vascular endothelial cells, and promoted angiogenesis of the cells, and thus enhanced the tumorigenesis. In TNBC, it was found that COL10A1 might affect epithelial-mesenchymal transition (EMT) of the cells through Wnt/ß-catenin signaling pathway by the detection of the related pathway proteins. COL10A1 is highly expressed in TNBC, and its high expression leads to poor OS and RFS. COL10A1 may enhance TNBC cell proliferation, migration and tumor-related angiogenesis, and promote tumorigenesis in vivo via Wnt/ß-catenin signaling.

Association between the blood urea nitrogen-to-creatinine ratio and 3-month outcomes in patients with acute ischemic stroke: a secondary analysis based on a prospective cohort study.

Introduction: This study aimed to assess the correlation between the blood urea nitrogen (BUN)-to-creatinine (BUN/Cr) ratio and adverse outcomes (AOs) at 3 months in patients with acute ischemic stroke (AIS) in the Korean population. Methods: This cohort study encompassed 1906 cases of AIS at a South Korean hospital from January 2010 to December 2016. To determine the linear correlation between the BUN/Cr ratio and AOs in AIS, a binary logistic regression model (BLRM) was employed. Additionally, generalized additive models and techniques for smooth curve fitting were utilized to reveal the nonlinear dynamics between the BUN/Cr ratio and AOs in patients with AIS. Results: The prevalence of AOs was 28.65%, with a median BUN/Cr ratio of 18.96. Following adjustments for covariates, the BLRM disclosed that the association between the BUN/Cr ratio and the risk of AOs in patients with AIS did not attain statistical significance. Nevertheless, a nonlinear relationship surfaced, pinpointing an inflection point at 21.591. To the left of this inflection point, a 31.42% reduction in the risk of AOs was noted for every 1-unit surge in the Z score of the BUN/Cr ratio [odds ratio (OR) = 0.686, 95% confidence interval (CI): 0.519, 0.906, p = 0.008]. On the right side of the inflection point, the effect size (OR = 1.405, 95% CI: 1.018, 1.902, p = 0.039) was determined. Conclusion: The findings of this study underscore the intricate nature of the relationship between the BUN/Cr ratio and 3-month outcomes in patients with AIS, establishing a robust groundwork for future investigations.

N-methyl Formamide Electrolyte Additive Enabling Highly Reversible Zn Anodes.

Parasitic side reactions and dendrites formation hinder the application of aqueous zinc ion batteries due to inferior cycling life and low reversibility. Against this background, N-methyl formamide (NMF), a multi-function electrolyte additive is applied to enhance the electrochemical performance. Studied via advanced synchrotron radiation spectroscopy and DFT calculations, the NMF additive simultaneously modifies the Zn2+ solvation structure and ensures uniform zinc deposition, thus suppressing both parasitic side reactions and dendrite formation. More importantly, an ultralong cycling life of 3115 h in the Zn||Zn symmetric cell at a current density of 0.5 mA cm-2 is achieved with the NMF additive. Practically, the Zn||PANI full cell utilizing NMF electrolyte shows better rate and cycling performance compared to the pristine ZnSO4 aqueous electrolyte. This work provides useful insights for the development of high-performance aqueous metal batteries.

Analyzing the Active Site and Predicting the Overall Activity of Alloy Catalysts.

As one of the potential catalysts, disordered solid solution alloys can offer a wealth of catalytic sites. However, accurately evaluating their activity localization structure and overall activity from each individual site remains a formidable challenge. Herein, an approach based on density functional theory and machine learning was used to obtain a large number of sites of the Pt-Ru alloy as the model multisite catalyst for the hydrogen evolution reaction. Subsequently, a series of statistical approaches were employed to unveil the relationship between the geometric structure and overall activity. Based on the radial frequency distribution of metal elements and the distribution of ΔGH, we have identified the surface and subsurface sites occupied by Pt and Ru, respectively, as the most active sites. Particularly, the concept of equivalent site ratio predicts that the overall activity is highest when the Ru content is 20-30%. Furthermore, a series of Pt-Ru alloys were synthesized to validate the proposed theory. This provides crucial insights into understanding the origin of catalytic activity in alloys and thus will better guide the rational development of targeted multisite catalysts.

A temporal enhanced semi-supervised training framework for needle segmentation in 3D ultrasound images.

Objective.Automated biopsy needle segmentation in 3D ultrasound images can be used for biopsy navigation, but it is quite challenging due to the low ultrasound image resolution and interference similar to the needle appearance. For 3D medical image segmentation, such deep learning networks as convolutional neural network and transformer have been investigated. However, these segmentation methods require numerous labeled data for training, have difficulty in meeting the real-time segmentation requirement and involve high memory consumption.Approach.In this paper, we have proposed the temporal information-based semi-supervised training framework for fast and accurate needle segmentation. Firstly, a novel circle transformer module based on the static and dynamic features has been designed after the encoders for extracting and fusing the temporal information. Then, the consistency constraints of the outputs before and after combining temporal information are proposed to provide the semi-supervision for the unlabeled volume. Finally, the model is trained using the loss function which combines the cross-entropy and Dice similarity coefficient (DSC) based segmentation loss with mean square error based consistency loss. The trained model with the single ultrasound volume input is applied to realize the needle segmentation in ultrasound volume.Main results.Experimental results on three needle ultrasound datasets acquired during the beagle biopsy show that our approach is superior to the most competitive mainstream temporal segmentation model and semi-supervised method by providing higher DSC (77.1% versus 76.5%), smaller needle tip position (1.28 mm versus 1.87 mm) and length (1.78 mm versus 2.19 mm) errors on the kidney dataset as well as DSC (78.5% versus 76.9%), needle tip position (0.86 mm versus 1.12 mm) and length (1.01 mm versus 1.26 mm) errors on the prostate dataset.Significance.The proposed method can significantly enhance needle segmentation accuracy by training with sequential images at no additional cost. This enhancement may further improve the effectiveness of biopsy navigation systems.

High-Throughput Screening of Sulfur Reduction Reaction Catalysts Utilizing Electronic Fingerprint Similarity.

The catalytic performance is determined by the electronic structure near the Fermi level. This study presents an effective and simple screening descriptor, i.e., the one-dimensional density of states (1D-DOS) fingerprint similarity, to identify potential catalysts for the sulfur reduction reaction (SRR) in lithium-sulfur batteries. The Δ1D-DOS in relation to the benchmark W2CS2 was calculated. This method effectively distinguishes and identifies 30 potential candidates for the SRR from 420 types of MXenes. Further analysis of the Gibbs free energy profiles reveals that MXene candidates exhibit promising thermodynamic properties for SRR, with the protocol achieving an accuracy rate exceeding 93%. Based on the crystal orbital Hamilton population (COHP) and differential charge analysis, it is confirmed that the Δ1D-DOS could effectively differentiate the interaction between MXenes and lithium polysulfide (LiPS) intermediates. This study underscores the importance of the electronic fingerprint in catalytic performance and thus may pave a new way for future high-throughput material screening for energy storage applications.

The Impact of Baohuoside I on the Metabolism of Tofacitinib in Rats.

Purpose: To study the potential drug-drug interactions between tofacitinib and baohuoside I and to provide the scientific basis for rational use of them in clinical practice. Methods: A total of eighteen Sprague-Dawley rats were randomly divided into three groups: control group, single-dose group (receiving a single dose of 20 mg/kg of baohuoside I), and multi-dose group (receiving multiple doses of baohuoside I for 7 days). On the seventh day, each rat was orally administered with 10 mg/kg of tofacitinib 30 minutes after giving baohuoside I or vehicle. Blood samples were collected and determined using UPLC-MS/MS. In vitro effects of baohuoside I on tofacitinib was investigated in rat liver microsomes (RLMs), as well as the underlying mechanism of inhibition. The semi-inhibitory concentration value (IC50) of baohuoside I was subsequently determined and its inhibitory mechanism against tofacitinib was analyzed. Furthermore, the interactions between baohuoside I, tofacitinib and CYP3A4 were explored using Pymol molecular docking simulation. Results: The administration of baohuoside I orally has been observed to enhance the area under the concentration-time curve (AUC) of tofacitinib and decrease the clearance (CL). The observed disparity between the single-dose and multi-dose groups was statistically significant. Furthermore, our findings suggest that the impact of baohuoside I on tofacitinib metabolism may be a mixture of non-competitive and competitive inhibition. Baohuoside I exhibit an interaction with arginine (ARG) at position 106 of the CYP3A4 enzyme through hydrogen bonding, positioning itself closer to the site of action compared to tofacitinib. Conclusion: Our study has demonstrated the presence of drug-drug interactions between baohuoside I and tofacitinib, which may arise upon pre-administration of tofacitinib. Altogether, our data indicated that an interaction existed between tofacitinib and baohuoside I and additional cares might be taken when they were co-administrated in clinic.

Construction of A Smart Hospital Innovation Platform Using the Internet + Technology.

Objective: This study aims to investigate the practical application of "Internet + technology" leveraging medical big data to enhance the development of a smart hospital platform. Specifically, the focus is on optimizing patient care processes, refining medical data management systems, and enhancing operational efficiency within the hospital setting. Methods: With the help of traditional Internet and mobile Internet technology, we analyzed the patient behavior big data accumulated in our hospital for many years, simplified the diagnosis and treatment links, refined the service connotation, and improved patient satisfaction before, during and after diagnosis; With the help of barcode, RFID and 5G networks, the links that are easy to produce medical security incidents can be monitored to improve medical quality, such as digital operating room system, establishment of multidisciplinary consultation center (MDT), electronic medical record system based on digital signature, etc.; Integrate the existing data of human resources, equipment and consumables, drugs, general supplies and finance, establish the hospital ERP system, introduce BI, and realize the dynamic monitoring of revenue and cost; Artificial intelligence technologies such as natural language processing (NLP) and machine learning are used to extract, clean, transform and analyze a large number of hospital medical information data to form a medical data platform, which can assist the hospital knowledge base to be applied in diagnosis and treatment, clinical research, clinical medicine, clinical medicine and other fields. The effectiveness of these technologies was assessed through a comprehensive analysis of their impact on patient care processes, medical data management, and overall operational efficiency within the hospital setting. Results: The study yielded significant outcomes across three critical domains. Firstly, in patient care, the implementation of expanded mobile medical services, mobile applications for records and rounds, and the establishment of digital signature-based records and intelligent surgical assistance enhanced accessibility and quality of care. Secondly, operational efficiency was notably improved through the integration of ERP and BI systems, streamlining resource management and surgical procedures. Lastly, advancements in data management, including the development of a medical data platform and the deployment of AI technologies, facilitated efficient analysis and utilization of clinical data. Quantitatively, reductions in patient visit time and medical costs were observed, alongside improved diagnostic accuracy facilitated by comprehensive records. These findings collectively underscore the transformative impact of "Internet + technology" integration on hospital operations and patient outcomes. Conclusion: The integration of "Internet + technology" in a smart hospital setting has demonstrated numerous benefits. This study has revealed novel findings regarding the tangible improvements in patient outcomes and hospital efficiency resulting from technology integration. Specifically, the implementation of mobile medical services, digital records, and AI-driven systems has led to significant reductions in patient visit times and medical costs. Moreover, the streamlining of operational processes through ERP and BI systems has enhanced resource management and surgical procedures. These findings underscore the transformative potential of technology in revolutionizing healthcare delivery, ultimately leading to better patient care and operational effectiveness in smart hospital environments.

The relationship between red cell distribution width, serum calcium ratio, and in-hospital mortality among patients with acute respiratory failure: A retrospective cohort study of the MIMIC-IV database.

To investigate the impact of RDW/CA (the ratio of red cell distribution width to calcium) on in-hospital mortality in patients with acute respiratory failure (ARF). This retrospective cohort study analyzed the data of 6981 ARF patients from the Medical Information Mart for Intensive Care (MIMIC-IV) database 2.0. Critically ill participants between 2008 and 2019 at the Beth Israel Deaconess Medical Center in Boston. The primary outcome of interest was in-hospital mortality. A Cox proportional hazards regression model was used to determine whether the RDW/CA ratio independently correlated with in-hospital mortality. The Kaplan-Meier method was used to plot the survival curves of the RDW/CA. Subgroup analyses were performed to measure the mortality across various subgroups. After adjusting for potential covariates, we found that a higher RDW/CA was associated with an increased risk of in-hospital mortality (HR = 1.17, 95% CI: 1.01-1.35, P = .0365) in ARF patients. A nonlinear relationship was observed between RDW/CA and in-hospital mortality, with an inflection point of 1.97. When RDW/CA ≥ 1.97 was positively correlated with in-hospital mortality in patients with ARF (HR = 1.554, 95% CI: 1.183-2.042, P = .0015). The Kaplan-Meier curve indicated the higher survival rates for RDW/CA < 1.97 and the lower for RDW/CA ≥ 1.97 after adjustment for age, gender, body mass index, and ethnicity. RDW/CA is an independent predictor of in-hospital mortality in patients with ARF. Furthermore, a nonlinear relationship was observed between RDW/CA and in-hospital mortality in patients with ARF.

Associations of metals and metal mixtures with glucose homeostasis: A combined bibliometric and epidemiological study.

This study employs a combination of bibliometric and epidemiological methodologies to investigate the relationship between metal exposure and glucose homeostasis. The bibliometric analysis quantitatively assessed this field, focusing on study design, predominant metals, analytical techniques, and citation trends. Furthermore, we analyzed cross-sectional data from Beijing, examining the associations between 14 blood metals and 6 glucose homeostasis markers using generalized linear models (GLM). Key metals were identified using LASSO-PIPs criteria, and Bayesian kernel machine regression (BKMR) was applied to assess metal mixtures, introducing an "Overall Positive/Negative Effect" concept for deeper analysis. Our findings reveal an increasing research interest, particularly in selenium, zinc, cadmium, lead, and manganese. Urine (27.6%), serum (19.0%), and whole blood (19.0%) were the primary sample types, with cross-sectional studies (49.5%) as the dominant design. Epidemiologically, significant associations were found between 9 metals-cobalt, copper, lithium, manganese, nickel, lead, selenium, vanadium, zinc-and glucose homeostasis. Notably, positive-metal mixtures exhibited a significant overall positive effect on insulin levels, and notable interactions involving nickel were identified. These finding not only map the knowledge landscape of research in this domain but also introduces a novel perspective on the analysis strategies for metal mixtures.