Effect of dapagliflozin on uric acid in patients with chronic heart failure and hyperuricemia.

BACKGROUND: Patients with chronic heart failure (CHF) frequently develop hyperuricemia, an elevated serum uric acid level, associated with adverse outcomes. Dapagliflozin, a sodium-glucose cotransporter-2 inhibitor, demonstrates reduction in cardiovascular mortality and hospitalization in patients with CHF and ejection fraction (HFrEF), irrespective of diabetes. However, dapagliflozin's effect on the uric acid levels in patients with CHF and hyperuricemia remain unclear. AIM: To investigate the effects of dapagliflozin on uric acid levels in CHF patients with hyperuricemia. METHODS: We conducted a randomized, double-blind, placebo-controlled trial in 200 patients with CHF and hyperuricemia, with HFrEF and serum uric acid levels ≥ 7 mg/dL (≥ 416 µmol/L). The participants were randomly assigned to receive a daily dose of 10 mg dapagliflozin or placebo for 24 months. The primary endpoint was the change in serum uric acid level from baseline to 24 months. Secondary endpoints included changes in left ventricular ejection fraction (LVEF), N-terminal pro-B-type natriuretic peptide (NT-proBNP), and quality of life (QoL) scores, as well as the incidence of cardiovascular death and hospitalization for heart failure. RESULTS: At 24 months, dapagliflozin significantly reduced serum uric acid levels by 1.2 mg/dL (71 µmol/L) compared with placebo (95%CI: -1.5 to -0.9; P < 0.001). Dapagliflozin also significantly improved LVEF by 3.5% (95%CI: 2.1-4.9; P < 0.001), NT-proBNP by 25% (95%CI: 18-32; P < 0.001), and QoL scores by 10 points (95%CI: 7-13; P < 0.001) and reduced the risk of cardiovascular death and hospitalization for heart failure by 35% (95%CI: 15-50; P = 0.002) compared with the placebo. Adverse events were similar between the two groups, except for a higher rate of genital infections in the dapagliflozin group (10% vs 2%, P = 0.01). CONCLUSION: Dapagliflozin significantly lowered serum uric acid levels and improved the clinical outcomes in patients with CHF and hyperuricemia. Therefore, dapagliflozin may be a useful therapeutic option for this high-risk population.

Traditional Chinese medicine for post-viral olfactory dysfunction: A systematic review.

Background: Post-viral olfactory dysfunction (PVOD) is the common symptoms of long COVID, lacking of effective treatments. Traditional Chinese medicine (TCM) is claimed to be effective in treating olfactory dysfunction, but the evidence has not yet been critically appraised. We conducted a systematic review to evaluate the effectiveness and safety of TCM for PVOD. Methods: We searched eight databases to identified clinical controlled studies about TCM for PVOD. The Cochrane risk of bias tools and GRADE were used to evaluate the quality of evidence. Risk ratio (RR), mean differences (MD), and 95 % confidence interval (CI), were used for effect estimation and RevMan 5.4.1 was used for data analysis. Results: Six randomized controlled trials (RCTs) (545 participants), two non-randomized controlled trials (non-RCTs) (112 participants), and one retrospective cohort study (30 participants) were included. The overall quality of included studies was low. Acupuncture (n = 8) and acupoint injection (n = 3) were the mainly used TCM therapies. Five RCTs showed a better effect in TCM group. Four trials used acupuncture, and three trials used acupoint injection. The results of two non-RCTs and one cohort study were not statistically significant. Two trials reported mild to moderate adverse events (pain and brief syncope caused by acupuncture or acupoint injection). Conclusions: Limited evidence focus on acupuncture and acupoint injection for PVOD and suggests that acupuncture and acupoint injection may be effective in improving PVOD. More well-designed trials should focus on acupuncture to confirm the benefit. Protocol registration: The protocol of this review was registered at PROSPERO: CRD42022366776.

Systematic review and meta-analysis of short-term outcomes: robot-assisted versus laparoscopic surgery for gastric cancer patients with visceral obesity.

The objective of this meta-analysis was to assess the comparative efficacy of robot-assisted and laparoscopic surgery in treating gastric cancer among patients characterized by a high visceral fat area (VFA). In April 2024, we conducted a comprehensive literature review using major international databases, such as PubMed, Embase, and Google Scholar. We restricted our selection to articles written in English, excluding reviews, protocols without published data, conference abstracts, and irrelevant content. Our analysis focused on continuous data using 95% confidence intervals (CIs) and standard mean differences (SMDs), while dichotomous data were assessed with odds ratios (ORs) and 95% CIs. We set the threshold for statistical significance at P < 0.05. Data extraction included baseline characteristics, primary outcomes (such as operative time, major complications, lymph node yield, and anastomotic leakage), and secondary outcomes. The meta-analysis included three cohort studies totaling 970 patients. The robotic-assisted group demonstrated a significantly longer operative time compared to the laparoscopic group, with a weighted mean difference (WMD) of - 55.76 min (95% CI - 74.03 to - 37.50; P < 0.00001). This group also showed a reduction in major complications, with an odds ratio (OR) of 2.48 (95% CI 1.09-5.66; P = 0.03) and fewer occurrences of abdominal infections (OR 3.17, 95% CI 1.41-7.14; P = 0.005), abdominal abscesses (OR 3.83, 95% CI 1.53-9.57; P = 0.004), anastomotic leaks (OR 4.09, 95% CI 1.73-9.65; P = 0.001), and pancreatic leaks (OR 8.93, 95% CI 2.33-34.13; P = 0.001). However, no significant differences were observed between the groups regarding length of hospital stay, overall complications, estimated blood loss, or lymph node yield. Based on our findings, robot-assisted gastric cancer surgery in obese patients with visceral fat appears to be correlated with fewer major complications compared to laparoscopic surgery, while maintaining similar outcomes in other surgical aspects. However, it is important to note that robot-assisted procedures do tend to have longer operative times.

Dislocation Density-Mediated Functionality in Single-Crystal BaTiO3.

Unlike metals where dislocations carry strain singularity but no charge, dislocations in oxide ceramics are characterized by both a strain field and a local charge with a compensating charge envelope. Oxide ceramics with their deliberate engineering and manipulation are pivotal in numerous modern technologies such as semiconductors, superconductors, solar cells, and ferroics. Dislocations facilitate plastic deformation in metals and lead to a monotonous increase in the strength of metallic materials in accordance with the widely recognized Taylor hardening law. However, achieving the objective of tailoring the functionality of oxide ceramics by dislocation density still remains elusive. Here a strategy to imprint dislocations with {100}<100> slip systems and a tenfold change in dislocation density of BaTiO3 single crystals using high-temperature uniaxial compression are reported. Through a dislocation density-based approach, dielectric permittivity, converse piezoelectric coefficient, and alternating current conductivity are tailored, exhibiting a peak at medium dislocation density. Combined with phase-field simulations and domain wall potential energy analyses, the dislocation-density-based design in bulk ferroelectrics is mechanistically rationalized. These findings may provide a new dimension for employing plastic strain engineering to tune the electrical properties of ferroics, potentially paving the way for advancing dislocation technology in functional ceramics.

Pure tone audiogram classification using deep learning techniques.

OBJECTIVE: Pure tone audiometry has played a critical role in audiology as the initial diagnostic tool, offering vital insights for subsequent analyses. This study aims to develop a robust deep learning framework capable of accurately classifying audiograms across various commonly encountered tasks. DESIGN, SETTING, AND PARTICIPANTS: This single-centre retrospective study was conducted in accordance with the STROBE guidelines. A total of 12 518 audiograms were collected from 6259 patients aged between 4 and 96 years, who underwent pure tone audiometry testing between February 2018 and April 2022 at Tongji Hospital, Tongji Medical College, Wuhan, China. Three experienced audiologists independently annotated the audiograms, labelling the hearing loss in degrees, types and configurations of each audiogram. MAIN OUTCOME MEASURES: A deep learning framework was developed and utilised to classify audiograms across three tasks: determining the degrees of hearing loss, identifying the types of hearing loss, and categorising the configurations of audiograms. The classification performance was evaluated using four commonly used metrics: accuracy, precision, recall and F1-score. RESULTS: The deep learning method consistently outperformed alternative methods, including K-Nearest Neighbors, ExtraTrees, Random Forest, XGBoost, LightGBM, CatBoost and FastAI Net, across all three tasks. It achieved the highest accuracy rates, ranging from 96.75% to 99.85%. Precision values fell within the range of 88.93% to 98.41%, while recall values spanned from 89.25% to 98.38%. The F1-score also exhibited strong performance, ranging from 88.99% to 98.39%. CONCLUSIONS: This study demonstrated that a deep learning approach could accurately classify audiograms into their respective categories and could contribute to assisting doctors, particularly those lacking audiology expertise or experience, in better interpreting pure tone audiograms, enhancing diagnostic accuracy in primary care settings, and reducing the misdiagnosis rate of hearing conditions. In scenarios involving large-scale audiological data, the automated classification system could be used as a research tool to efficiently provide a comprehensive overview and statistical analysis. In the era of mobile audiometry, our deep learning framework can also help patients quickly and reliably understand their self-tested audiograms, potentially encouraging timely consultations with audiologists for further evaluation and intervention.

Association Between Neuroinflammation and Parkinson's Disease: A Comprehensive Mendelian Randomization Study.

The objective of the study is to determine the causal relationship and potential mechanisms between Parkinson's disease (PD) and neuroinflammatory and neurotoxic mediators. We conducted two-sample Mendelian randomization (2SMR) study and multivariable Mendelian randomization (MVMR) analysis to investigate the causality between PD and neuroinflammatory and neurotoxic mediators. The mediation analysis with MR was also conducted to determine the potential mediating effect of neuroinflammatory and neurotoxic mediators between asthma and PD. Genetically predicted levels of nine neuroinflammation were associated with changes in PD risk. The associations of PD with CCL24, galectin-3 levels, haptoglobin, and Holo-Transcobalamin-2 remained significant in multivariable analyses. The mediation analysis with MR revealed that asthma affects PD through CCL24 and galectin-3. The results showed neuroinflammation could affect the pathogenesis of PD. In the combined analysis of these nine variables, CCL24, galectin-3 levels, HP, and Holo-Transcobalamin-2 alone were found to be significant. Asthma plays an intermediary role through CCL24 and galectin-3 levels.

Performance of ChatGPT on Chinese Master's Degree Entrance Examination in Clinical Medicine.

BACKGROUND: ChatGPT is a large language model designed to generate responses based on a contextual understanding of user queries and requests. This study utilised the entrance examination for the Master of Clinical Medicine in Traditional Chinese Medicine to assesses the reliability and practicality of ChatGPT within the domain of medical education. METHODS: We selected 330 single and multiple-choice questions from the 2021 and 2022 Chinese Master of Clinical Medicine comprehensive examinations, which did not include any images or tables. To ensure the test's accuracy and authenticity, we preserved the original format of the query and alternative test texts, without any modifications or explanations. RESULTS: Both ChatGPT3.5 and GPT-4 attained average scores surpassing the admission threshold. Noteworthy is that ChatGPT achieved the highest score in the Medical Humanities section, boasting a correct rate of 93.75%. However, it is worth noting that ChatGPT3.5 exhibited the lowest accuracy percentage of 37.5% in the Pathology division, while GPT-4 also displayed a relatively lower correctness percentage of 60.23% in the Biochemistry section. An analysis of sub-questions revealed that ChatGPT demonstrates superior performance in handling single-choice questions but performs poorly in multiple-choice questions. CONCLUSION: ChatGPT exhibits a degree of medical knowledge and the capacity to aid in diagnosing and treating diseases. Nevertheless, enhancements are warranted to address its accuracy and reliability limitations. Imperatively, rigorous evaluation and oversight must accompany its utilization, accompanied by proactive measures to surmount prevailing constraints.

[Downregulation of SETDB1 suppresses epithelial mesenchymal transition and invasion and metastasis in oral cancer via SOX7 methylation].

PURPOSE: To explore the mechanism of SETDB1 inhibiting epithelial mesenchymal transition (EMT),migration and invasion in oral cancer via SOX 7 methylation. METHODS: SETDB1 and SOX7 mRNA and protein expression levels in KB cells of oral cancer and oral mucosal epithelial ATCC cells were determined by qRT-PCR and Western blot (WB). SETDB1 si-RNA was structured, then transfect into KB cells of oral cancer by liposome-mediated method. siRNA-SETDB1 was the experimental group (si-S), siRNA empty vector was the negative control group (si-N), and untransfected KB cells were the blank control group(NC). SETDB1 mRNA and protein expression levels were detected by qRT-PCR and Western blot(WB), to verify the transfection effect. The methylation levels of SOX7 were determined by pyrosequencing. The expression of N-cadherin, Vimentin, ß-catenin, and Slug proteins was detected by WB. Cell viability was measured by MTT assay, migration ability was tested by scratch healing assay, and invasion ability was tested by Transwell chamber assay. Statistical analysis was performed with SPSS 21.0 software package. RESULTS: The results of Rt-qPCR and WB showed that the SETDB1 mRNA and protein expression decreased significantly in si-S group(P＜0.05). Pyrosequencing test results showed that the regulation of SETDB1 could significantly reduce the SOX7 methylation rate and increased the SOX7 protein expression. WB results showed that knockdown of SETDB1 significantly inhibited the expression of EMT-related proteins N-cadherin, Vimentin, ß-catenin and Slug in oral cancer KB cells (P＜0.05). The results of cell functology experiments showed that knockdown of SETDB1 could significantly inhibit survival, migration and invasion of KB cells. CONCLUSIONS: Downregulation of SETDB1 could suppress EMT, migration and invasion of oral cancer cells by regulating SOX7 methylation level, providing new ideas and targets for the diagnosis and treatment of oral cancer.

Turning a CLIP Model into a Scene Text Spotter.

We exploit the potential of the large-scale Contrastive Language-Image Pretraining (CLIP) model to enhance scene text detection and spotting tasks, transforming it into a robust backbone, FastTCM-CR50. This backbone utilizes visual prompt learning and cross-attention in CLIP to extract image and text-based prior knowledge. Using predefined and learnable prompts, FastTCM-CR50 introduces an instance-language matching process to enhance the synergy between image and text embeddings, thereby refining text regions. Our Bimodal Similarity Matching (BSM) module facilitates dynamic language prompt generation, enabling offline computations and improving performance. FastTCM-CR50 offers several advantages: 1) It can enhance existing text detectors and spotters, improving performance by an average of 1.6% and 1.5%, respectively. 2) It outperforms the previous TCM-CR50 backbone, yielding an average improvement of 0.2% and 0.55% in text detection and spotting tasks, along with a 47.1% increase in inference speed. 3) It showcases robust few-shot training capabilities. Utilizing only 10% of the supervised data, FastTCM-CR50 improves performance by an average of 26.5% and 4.7% for text detection and spotting tasks, respectively. 4) It consistently enhances performance on out-of-distribution text detection and spotting datasets, particularly the NightTime-ArT subset from ICDAR2019-ArT and the DOTA dataset for oriented object detection. The code is available at https://github.com/wenwenyu/TCM.

Analysis of the Relationship Between Parkinson's Disease and Diabetic Retinopathy Based on Bioinformatics Methods.

The objective of the study was to explore the relationship and potential mechanism between Parkinson's disease (PD) and diabetic retinopathy (DR) using bioinformatics methods. We first examined the causal relationship between PD and DR by Mendelian randomization (MR) analysis. The datasets of PD and DR patients from the Gene Expression Omnibus database were used to identify differentially expressed genes (DEGs). Then, we performed the Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and immune infiltration analysis. We also constructed a protein-protein interaction network and receiver operating characteristic (ROC) curve. Finally, an online website was used for drug prediction. The MR analysis demonstrated a causal relationship between DR and PD (odds ratio [OR] = 0.86; 95% confidence interval [CI] 0.79-0.93; p = 3.24E - 04), in which DR acted as a protective factor against PD. There were 81 DEGs identified from the PD and DR datasets, of which 29 genes had protein interaction relationships, and enrichment analysis showed that these genes were mainly related to immune pathways. As indicated by immune cell infiltration analysis, the expression of immune cells between PD and the control group was significantly different. ROC curve results showed five genes had diagnostic value, and several potential chemical compounds were predicted to target the genes. Our findings demonstrate a reduced risk of PD in patients with DR. We also found that PD and DR are closely related in terms of inflammation, which provides clues for further exploring the common mechanisms and interaction of these two diseases.

Charge-Assisted Ionic Hydrogen-Bonded Organic Frameworks: Designable and Stabilized Multifunctional Materials.

Hydrogen-bonded organic frameworks (HOFs) are a class of crystalline framework materials assembled by hydrogen bonds. HOFs have the advantages of high crystallinity, mild reaction conditions, good solution processability, and reproducibility. Coupled with the reversibility and flexibility of hydrogen bonds, HOFs can be assembled into a wide diversity of crystalline structures. Since the bonding energy of hydrogen bonds is lower than that of ligand and covalent bonds, the framework of HOFs is prone to collapse after desolventisation and the stability is not high, which limits the development and application of HOFs. In recent years, numerous stable and functional HOFs have been developed by π-π stacking, highly interpenetrated networks, charge-assisted, ligand-bond-assisted, molecular weaving, and covalent cross-linking. Charge-assisted ionic HOFs introduce electrostatic attraction into HOFs to improve stability while enriching structural diversity and functionality. In this paper, we review the development, the principles of rational design and assembly of charge-assisted ionic HOFs, and introduces the different building block construction modes of charge-assisted ionic HOFs. Highlight the applications of charge-assisted ionic HOFs in gas adsorption and separation, proton conduction, biological applications, etc., and prospects for the diverse design of charge-assisted ionic HOFs structures and multifunctional applications.

Research progress in micro/nanobubbles for ultrasound diagnosis or treatment / 药学学报

In the past few decades, microbubbles were widely used as ultrasound contrast agents in the field of tumor imaging. With the development of research, ultrasound targeted microbubble destruction technology combined with drug-loaded microbubbles can achieve precise drug release and play a therapeutic role. As a micron-scale carrier, microbubbles are difficult to penetrate the endothelial cell space of tumors, and nano-scale drug delivery system—nanobubbles came into being. The structure of the two is similar, but the difference in size highlights the unique advantages of nanobubbles in drug delivery. Based on the classification principle of shell materials, this review summarized micro/nanobubbles used for ultrasound diagnosis or treatment and discussed the possible development directions, providing references for the subsequent development.

Understanding apoptotic induction by Sargentodoxa cuneata-Patrinia villosa herb pair via PI3K/AKT/mTOR signalling in colorectal cancer cells using network pharmacology and cellular studies.

ETHNOPHARMACOLOGICAL RELEVANCE: Sargentodoxa cuneata (Sargentodoxa cuneata (Oliv.) Rehder & E.H.Wilson, DXT)-Patrinia villosa(Patrinia villosa (Thunb.) Dufr, BJC) constitutes a commonly employed herb pair in Chinese medicine for colorectal cancer (CRC) treatment. Modern pharmacological investigations have revealed the anticancer activities of both Sargentodoxa cuneata and Patrinia villosa. Nevertheless, comprehensive studies are required to discern the specific antitumor active ingredients and mechanism of action when these two herbs are used in combination. AIM OF THE STUDY: Through the integration of network pharmacology, molecular docking techniques, experimental assays, and bioinformatics analysis, our study aims to forecast the active ingredients, potential targets, and molecular mechanisms underlying the therapeutic efficacy of this herb pair against CRC. MATERIALS AND METHODS: Plant names (1, Sargentodoxa cuneata (Oliv.) Rehder & E.H.Wilson; 2, Patrinia villosa (Thunb.) Dufr.) have been verified through WorldFloraOnline (www.worldFloraonline.org) and MPNs (http://mpns.kew.org). The Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) were utilized for screening the active ingredients of the herb pair. The PharmMapper database was employed to predict the target proteins for each active ingredient. CRC-related targets were obtained from the Genecards database, Online Mendelian Inheritance in Man (OMIM) database, Disease Gene Network (DisGeNET) database, and Therapeutic Target Database (TTD). Common targets were identified by intersecting the target proteins of all active ingredients with CRC-related targets. Protein-protein interactions (PPI) for the common target proteins were constructed using the String database and Cytoscape 3.9.1 software. Network topology analysis facilitated the identification of core targets. These core targets were subjected to enrichment analysis of Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) using the Metascape database. Molecular docking was performed using Discovery Studio 2019 to investigate the interactions between the active ingredients and core target proteins. The core targets were validated through bioinformatics analysis using GEPIA, HPA, and the cBioPortal database. Finally, a series of experiments were conducted to further validate the results in vitro. RESULT: A total of 15 active ingredients and 255 herb targets were identified, resulting in 66 common targets in conjunction with 6113 disease targets. The PPI analysis highlighted AKT1, EGFR, CASP3, SRC, and ESR1 as core targets. KEGG enrichment analysis indicated significant enrichment in the PI3K-AKT signaling pathway, a pathway associated with cancer. Molecular docking experiments confirmed favorable interactions between dihydroguaiaretic acid and the core target proteins (AKT1, EGFR, CASP3, and ESR1). Bioinformatics analysis revealed differential expression of EGFR and CASP3 in normal and CRC tissues. Cellular experiments further verified that dihydroguaiaretic acid induces apoptosis in colorectal cancer cells through the PI3K-AKT signaling pathway. CONCLUSION: Our network pharmacology study has elucidated that the Sargentodoxa cuneata-Patrinia villosa herb pair exerts the negative regulation of the PI3K/AKT/mTOR signaling pathway, ultimately leading to the induction of apoptosis in colorectal cancer cells. This research has predicted and validated the active ingredients, potential targets, and molecular mechanisms of Sargentodoxa cuneata-Patrinia villosa in the treatment of CRC, providing scientific evidence for the use of traditional Chinese medicine in managing CRC.

Promoting wound recovery through stable intestinal flora: Reducing post-operative complications in colorectal cancer surgery patients.

In recent years, the relationship between intestinal flora and post-operative recovery, particularly in colorectal cancer (CRC) surgery patients, it has been hypothesized that intestinal flora stability influences wound healing, reduces complications and improves overall recovery outcomes after surgical interventions. This study examined the relationship between intestinal flora stability and post-operative recovery in patients undergoing CRC surgery. Between May 2020 and 2023, 80 CRC patients from our hospital's Colorectal Surgery Department were enrolled. A random number table was used to divide them into two categories. Both groups were subjected to distinct gastrointestinal preparation protocols. Indicators of clinical therapeutic effect, intestinal flora balance following surgery, post-operative complications and quality of life were evaluated. The observation group, which adhered to a distinct gastrointestinal regimen, demonstrated a statistically significant improvement in post-operative outcomes, with a clinical effectiveness rate of 97.5% compared to the control group's 75%. In addition, the observation group had a lower incidence of intestinal flora imbalance following surgery than the control group. The observation group had lower incidences of intestinal obstruction, infection, anastomotic leakage, incisional tumour implantation and delayed diarrhoea. Using the KPS score and the BMI, post-treatment assessments of the observation group's quality of life revealed significant enhancements in comparison to the control group. Additionally, wound healing rates were superior in the observation group, with a correlation between stable intestinal flora and decreased wound infection rates. The type of post-operative diet influenced the stabilization of the gut flora, with a high-fibre diet producing superior results in both groups. The stability of intestinal flora influences the post-operative rehabilitation of patients undergoing CRC surgery favourably. Appropriate bowel preparation and dietary considerations can reduce post-operative complications, improve wound healing rates and enhance overall quality of life.

Epidemiological analysis reveals a surge in inflammatory bowel disease among children and adolescents: A global, regional, and national perspective from 1990 to 2019 - insights from the China study.

Background: The burden of inflammatory bowel disease (IBD) among children and adolescents is rising globally, with substantial variation in levels and trends of disease in different countries and regions, while data on the burden and trends were sparse in children and adolescents. We aimed to assess the trends and geographical differences in children and adolescents aged zero to 19 in 204 countries and territories over the past 30 years. Methods: Data on IBD among children and adolescents was collected from the Global Burden of Disease (GBD) 2019 database from 1990 to 2019. We used the GBD data and methodologies to describe the change in the burden of IBD among children and adolescents involving prevalence, incidence, disability-adjusted life years (DALYs), and mortality. Results: Globally, the IBD prevalence cases increased between 1990 and 2019. Annual percentage changes (AAPC) = 0.15; 95% confidence interval (CI) = 0.11-0.19, and incidence cases of IBD increased from 20 897.4 (95% CI = 17 008.6-25 520.2 in 1990 to 25 658.6 (95% CI = 21 268.5-31 075.6) in 2019, representing a 22.78% increase, DALYs cases decreased between 1990 and 2019 (AAPC = -3.02; 95% CI = -3.15 to -2.89), and mortality cases of IBD decreased from 2756.5 (95% CI = 1162.6-4484.9) in 1990 to 1208.0 (95% CI = 802.4-1651.4) in 2019, representing a 56.17% decrease. Decomposition analysis showed that IBD prevalence and incidence increased significantly, and a trend exhibited a decrease in underlying age and population-adjusted IBD DALYs and mortality rates. Correlation analysis showed that countries with high health care quality and access (HAQ) had relatively higher IBD age-standardised prevalence rate (ASPR) and age-standardised incidence rate (ASIR), but lower age-standardised DALYs rate (ASDR) and age-standardised mortality rate (ASMR). Conclusions: Global prevalence and incidence rate of IBD among children and adolescents have been increasing from 1990 to 2019, while the DALYs and mortality have been decreasing. Rising prevalence and rising incidence in areas with historically low rates will have crucial health and economic implications.

Clinical efficacy and safety of tigecycline based on therapeutic drug monitoring for carbapenem-resistant Gram-negative bacterium pneumonia in intensive care units.

BACKGROUND: We investigated the associations between the different doses of tigecycline, its efficacy and safety, and the role of tigecycline therapeutic drug monitoring for patients in the intensive care unit. METHODS: This study was a single-center cohort including patients infected with multidrug-resistant Acinetobacter baumannii (MDR-AB) and multidrug-resistant Klebsiella pneumoniae (MDR-KP) causing pulmonary infections. The steady-state plasma concentration after tigecycline administration was determined by High-Performance Liquid Chromatography (HPLC) in patients admitted to the ICU between October 2020 and December 2021. Multivariate analyses of tigecycline's clinical efficacy and safety were performed to control confounding factors. RESULTS: For this study, we included 45 patients and 45 blood samples to determine steady-state trough concentrations of tigecycline. All patients were divided into the High Dose (HD) and Standard Dose (SD) groups. The median trough concentration of tigecycline was 0.56 µg/mL in the HD group, which was higher than in the SD group (0,21 µg/mL), p = 0.000. There was no significant difference between the two groups of patients in terms of bacterial eradication rate, mortality rate, and clinical efficacy. Multiple regression analysis showed that the ICU days were correlated with mortality OR 1.030(1.005-1.056), p = 0.017. APACHE II was significantly associated with clinical efficacy OR 0.870(0.755-1.002), p = 0.045. The level of fibrinogen decline in the HD group was significantly higher than in the SD group (-3.05 ± 1.67 vs -1.75 ± 1.90), p = 0.038. We identified that age and tigecycline treatment duration influenced fibrinogen decline. CONCLUSIONS: Tigecycline plasma concentrations are significantly increased when using a high dose. However, the plasma concentration of tigecycline is not correlated with clinical efficacy and adverse reactions. Fibrinogen decline appears to be related to the patient's age and days of tigecycline. Large sample data are still needed to confirm the clinical guidance significance of tigecycline TDM.

SPTS v2: Single-Point Scene Text Spotting.

End-to-end scene text spotting has made significant progress due to its intrinsic synergy between text detection and recognition. Previous methods commonly regard manual annotations such as horizontal rectangles, rotated rectangles, quadrangles, and polygons as a prerequisite, which are much more expensive than using single-point. Our new framework, SPTS v2, allows us to train high-performing text-spotting models using a single-point annotation. SPTS v2 reserves the advantage of the auto-regressive Transformer with an Instance Assignment Decoder (IAD) through sequentially predicting the center points of all text instances inside the same predicting sequence, while with a Parallel Recognition Decoder (PRD) for text recognition in parallel, which significantly reduces the requirement of the length of the sequence. These two decoders share the same parameters and are interactively connected with a simple but effective information transmission process to pass the gradient and information. Comprehensive experiments on various existing benchmark datasets demonstrate the SPTS v2 can outperform previous state-of-the-art single-point text spotters with fewer parameters while achieving 19× faster inference speed. Within the context of our SPTS v2 framework, our experiments suggest a potential preference for single-point representation in scene text spotting when compared to other representations. Such an attempt provides a significant opportunity for scene text spotting applications beyond the realms of existing paradigms.

Variational quantitative phase-field modeling of nonisothermal sintering process.

Phase-field modeling has become a powerful tool in describing the complex pore-structure evolution and the intricate multiphysics in nonisothermal sintering processes. However, the quantitative validity of conventional variational phase-field models involving diffusive processes is a challenge. Artificial interface effects, like the trapping effects, may originate at the interface when the kinetic properties of two opposing phases are different. On the other hand, models with prescribed antitrapping terms do not necessarily guarantee the thermodynamics variational nature of the model. This issue has been solved for liquid-solid interfaces via the development of the variational quantitative solidification phase-field model. However, there is no related work addressing the interfaces in nonisothermal sintering, where the free surfaces between the solid phase and surrounding pore regions exhibit strong asymmetry of mass and thermal properties. Also, additional challenges arise due to the conserved order parameter describing the free surfaces. In this work, we present a variational and quantitative phase-field model for nonisothermal sintering processes. The model is derived via an extended nondiagonal phase-field model. The model evolution equations have naturally cross-coupling terms between the conserved kinetics (i.e., mass and thermal transfer) and the nonconserved one (grain growth). These terms are shown via asymptotic analysis to be instrumental in ensuring the elimination of interface artifacts, while also examined to not modify the thermodynamic equilibrium condition (characterized by a dihedral angle). Moreover, we demonstrate that the trapping effects and the existence of surface diffusion in conservation laws are direction-dependent. An anisotropic interpolation scheme of the kinetic mobilities that differentiates between the normal and tangential directions along the interface is discussed. Numerically, we demonstrate the importance of the cross-couplings and the anisotropic interpolation by presenting thermal-microstructural evolutions.

SAN: Side Adapter Network for Open-Vocabulary Semantic Segmentation.

This article concentrates on open-vocabulary semantic segmentation, where a well optimized model is able to segment arbitrary categories that appear in an image. To achieve this goal, we present a novel framework termed Side Adapter Network, or SAN for short. Our design principles are three-fold: 1) Recent large-scale vision-language models (e.g. CLIP) show promising open-vocabulary image classification capability; it is training-economized to adapt a pre-trained CLIP model to open-vocabulary semantic segmentation. 2) Our SAN model should be both lightweight and effective in order to reduce the inference cost-to achieve this, we fuse the CLIP model's intermediate features to enhance the representation capability of the SAN model, and drive the CLIP model to focus on the informative areas of an image with the aid of the attention biases predicted by a side adapter network. 3) Our approach should empower mainstream segmentation architectures to have the capability of open-vocabulary segmentation-we present P-SAN and R-SAN, to support widely adopted pixel-wise segmentation and region-wise segmentation, respectively. Experimentally, our approach achieves state-of-the-art performance on 5 commonly used benchmarks while having much less trainable parameters and GFLOPs. For instance, our R-SAN outperforms previous best method OvSeg by +2.3 averaged mIoU across all benchmarks while using only 6% of trainable parameters and less than 1% of GFLOPs. In addition, we also conduct a comprehensive analysis of the open-vocabulary semantic segmentation datasets and verify the feasibility of transferring a well optimzied R-SAN model to video segmentation task.

Focalizing regions of biomarker relevance facilitates biomarker prediction on histopathological images.

Image-based AI has thrived as a potentially revolutionary tool for predicting molecular biomarker statuses, which aids in categorizing patients for appropriate medical treatments. However, many methods using hematoxylin and eosin-stained (H&E) whole-slide images (WSIs) have been found to be inefficient because of the presence of numerous uninformative or irrelevant image patches. In this study, we introduced the region of biomarker relevance (ROB) concept to identify the morphological areas most closely associated with biomarkers for accurate status prediction. We actualized this concept within a framework called saliency ROB search (SRS) to enable efficient and effective predictions. By evaluating various lung adenocarcinoma (LUAD) biomarkers, we showcased the superior performance of SRS compared to current state-of-the-art AI approaches. These findings suggest that AI tools, built on the ROB concept, can achieve enhanced molecular biomarker prediction accuracy from pathological images.