Exploration of uricase-like activity in Pd@Ir nanosheets and their application in relieving acute gout using self-cascade reaction.

Gout, marked by the deposition of sodium urate crystals in joints and peripheral tissues, presents a considerable health challenge. Recent research has shown a growing interest in nanozyme-based treatments for gout. However, literature on nanozymes that combine uricase-like (UOX) activity for uric acid (UA) degradation with catalase (CAT)-like activity for H2O2 elimination through a self-cascade reaction is limited. Herein, we discovered that two-dimensional Pd@Ir nanosheets (NSs) exhibit UOX and CAT activities effectively. Notably, we observed a size-dependent effect of Pd@Ir on activation energy during UA degradation, with the larger Pd@Ir NSs demonstrating a lower energy barrier. The 46-nm Pd@Ir had activation energy as low as 35.9 kJ/mol, surpassing the efficiency of natural bacterial uricase and most reported nanozymes. Through a tandem self-cascade reaction of Pd@Ir, UA was effectively degraded via UOX activity, while the byproduct H2O2 was simultaneously eliminated by CAT-like activity. Cell experiments revealed that Pd@Ir protect normal cells from oxidative stress and promote cell proliferation, demonstrating an excellent self-cascade effect. Additionally, Pd@Ir substantially alleviated gout symptoms in monosodium urate-induced acute gout mice without causing toxic effects on biological organs and tissues. This study opens new avenues for using nanozyme-based cascade reaction systems in the treatment of metabolic diseases.

Chinese colposcopists' attitudes toward the colposcopic artificial intelligence auxiliary diagnostic system (CAIADS): A nation-wide, multi-center survey.

Objective: The objective of this study was to assess the attitudes toward the Colposcopic Artificial Intelligence Auxiliary Diagnostic System (CAIADS) of colposcopists working in mainland China. Methods: A questionnaire was developed to collect participants' sociodemographic information and assess their awareness, attitudes, and acceptance toward the CAIADS. Results: There were 284 respondents from 24 provinces across mainland China, with 55% working in primary care institutions. Participant data were divided into two subgroups based on their colposcopy case load per year (i.e. ≥50 cases; <50 cases). The analysis showed that participants with higher loads had more experience working with CAIADS and were more knowledgeable about CAIADS and AI systems. Overall, in both groups, about half of the participants understood the potential applications of big data and AI-assisted diagnostic systems in medicine. Although less than one-third of the participants were knowledgeable about CAIADS and its latest developments, more than 90% of the participants were open with the idea of using CAIADS. Conclusions: While a related lack of acknowledgement of CAIADS exists, the participants in general had an open attitude toward CAIADS. Practical experience with colposcopy or CAIADS contributed to participants' awareness and positive attitudes. The promotion of AI tools like CAIADS could help address regional health inequities to improve women's well-being, especially in low- and middle-income countries.

An assessment of ChatGPT's responses to frequently asked questions about cervical and breast cancer.

BACKGROUND: Cervical cancer (CC) and breast cancer (BC) threaten women's well-being, influenced by health-related stigma and a lack of reliable information, which can cause late diagnosis and early death. ChatGPT is likely to become a key source of health information, although quality concerns could also influence health-seeking behaviours. METHODS: This cross-sectional online survey compared ChatGPT's responses to five physicians specializing in mammography and five specializing in gynaecology. Twenty frequently asked questions about CC and BC were asked on 26th and 29th of April, 2023. A panel of seven experts assessed the accuracy, consistency, and relevance of ChatGPT's responses using a 7-point Likert scale. Responses were analyzed for readability, reliability, and efficiency. ChatGPT's responses were synthesized, and findings are presented as a radar chart. RESULTS: ChatGPT had an accuracy score of 7.0 (range: 6.6-7.0) for CC and BC questions, surpassing the highest-scoring physicians (P < 0.05). ChatGPT took an average of 13.6 s (range: 7.6-24.0) to answer each of the 20 questions presented. Readability was comparable to that of experts and physicians involved, but ChatGPT generated more extended responses compared to physicians. The consistency of repeated answers was 5.2 (range: 3.4-6.7). With different contexts combined, the overall ChatGPT relevance score was 6.5 (range: 4.8-7.0). Radar plot analysis indicated comparably good accuracy, efficiency, and to a certain extent, relevance. However, there were apparent inconsistencies, and the reliability and readability be considered inadequate. CONCLUSIONS: ChatGPT shows promise as an initial source of information for CC and BC. ChatGPT is also highly functional and appears to be superior to physicians, and aligns with expert consensus, although there is room for improvement in readability, reliability, and consistency. Future efforts should focus on developing advanced ChatGPT models explicitly designed to improve medical practice and for those with concerns about symptoms.

Synthesis and crystallization of a carboxylate functionalized N-heterocyclic carbene-based Au13 cluster with strong photo-luminescence.

Here we report the synthesis and crystallization of a -COOH-capped N-heterocyclic carbene (NHC)-protected Au13 cluster. The single-crystal structure of the -COOH-capped NHC-Au13 cluster reveals a classic icosahedral core with one Au atom in its center. The icosahedral core is surrounded by five NHC ligands with pseudo C5 symmetry and exposed carboxyls in a pentagonal antiprism fashion. The detailed formula of the Au cluster was identified as Au13(bi-NHC carboxyl)5Cl2 (hereafter abbreviated as Au13-c). The density functional theory (DFT) calculations confirm that Au13-c is an electronically stable eight-electron super-atom cluster and elucidate its optical transitions in the UV-Vis range. The Au13-c cluster exhibits excellent thermal and chemical stability under bio-relevant conditions. Additionally, this cluster shows a strong red emission in DMF and H2O with an excellent quantum yield (QY) of 40% and 12.6%, respectively. The high QY of Au13-c enables its use in cell imaging on both cancer and noncancerous cells.

Deep learning algorithms for melanoma detection using dermoscopic images: A systematic review and meta-analysis.

BACKGROUND: Melanoma is a serious risk to human health and early identification is vital for treatment success. Deep learning (DL) has the potential to detect cancer using imaging technologies and many studies provide evidence that DL algorithms can achieve high accuracy in melanoma diagnostics. OBJECTIVES: To critically assess different DL performances in diagnosing melanoma using dermatoscopic images and discuss the relationship between dermatologists and DL. METHODS: Ovid-Medline, Embase, IEEE Xplore, and the Cochrane Library were systematically searched from inception until 7th December 2021. Studies that reported diagnostic DL model performances in detecting melanoma using dermatoscopic images were included if they had specific outcomes and histopathologic confirmation. Binary diagnostic accuracy data and contingency tables were extracted to analyze outcomes of interest, which included sensitivity (SEN), specificity (SPE), and area under the curve (AUC). Subgroup analyses were performed according to human-machine comparison and cooperation. The study was registered in PROSPERO, CRD42022367824. RESULTS: 2309 records were initially retrieved, of which 37 studies met our inclusion criteria, and 27 provided sufficient data for meta-analytical synthesis. The pooled SEN was 82 % (range 77-86), SPE was 87 % (range 84-90), with an AUC of 0.92 (range 0.89-0.94). Human-machine comparison had pooled AUCs of 0.87 (0.84-0.90) and 0.83 (0.79-0.86) for DL and dermatologists, respectively. Pooled AUCs were 0.90 (0.87-0.93), 0.80 (0.76-0.83), and 0.88 (0.85-0.91) for DL, and junior and senior dermatologists, respectively. Analyses of human-machine cooperation were 0.88 (0.85-0.91) for DL, 0.76 (0.72-0.79) for unassisted, and 0.87 (0.84-0.90) for DL-assisted dermatologists. CONCLUSIONS: Evidence suggests that DL algorithms are as accurate as senior dermatologists in melanoma diagnostics. Therefore, DL could be used to support dermatologists in diagnostic decision-making. Although, further high-quality, large-scale multicenter studies are required to address the specific challenges associated with medical AI-based diagnostics.

Distribution and diagnostic value of single and multiple high-risk HPV infections in detection of cervical intraepithelial neoplasia: A retrospective multicenter study in China.

The risk associated with single and multiple human papillomavirus (HPV) infections in cervical intraepithelial neoplasia (CIN) remains uncertain. This study aims to explore the distribution and diagnostic significance of the number of high-risk HPV (hr-HPV) infections in detecting CIN, addressing a crucial gap in our understanding. This comprehensive multicenter, retrospective study meticulously analyzed the distribution of single and multiple hr-HPV, the risk of CIN2+, the relationship with CIN, and the impact on the diagnostic performance of colposcopy using demographic information, clinical histories, and tissue samples. The composition of a single infection was predominantly HPV16, 52, 58, 18, and 51, while HPV16 and 33 were identified as the primary causes of CIN2+. The primary instances of dual infection were mainly observed in combinations such as HPV16/18, HPV16/52, and HPV16/58, while HPV16/33 was identified as the primary cause of CIN2+. The incidence of hr-HPV infections shows a dose-response relationship with the risk of CIN (p for trend <0.001). Compared to single hr-HPV, multiple hr-HPV infections were associated with increased risks of CIN1 (1.44, 95% confidence interval [CI]: 1.20-1.72), CIN2 (1.70, 95% CI: 1.38-2.09), and CIN3 (1.08, 95% CI: 0.86-1.37). The colposcopy-based specificity of single hr-HPV (93.4, 95% CI: 92.4-94.4) and multiple hr-HPV (92.9, 95% CI: 90.8-94.6) was significantly lower than negative (97.9, 95% CI: 97.0-98.5) in detecting high-grade squamous intraepithelial lesion or worse (HSIL+). However, the sensitivity of single hr-HPV (73.5, 95% CI: 70.8-76.0) and multiple hr-HPV (71.8, 95% CI: 67.0-76.2) was higher than negative (62.0, 95% CI: 51.0-71.9) in detecting HSIL+. We found that multiple hr-HPV infections increase the risk of developing CIN lesions compared to a single infection. Colposcopy for HSIL+ detection showed high sensitivity and low specificity for hr-HPV infection. Apart from HPV16, this study also found that HPV33 is a major pathogenic genotype.

Novel Coumarins Derivatives for A. baumannii Lung Infection Developed by High-Throughput Screening and Reinforcement Learning.

With the increasing resistance of Acinetobacter baumannii (A. baumannii) to antibiotics, researchers have turned their attention to the development of new antimicrobial agents. Among them, coumarin-based heterocycles have attracted much attention due to their unique biological activities, especially in the field of antibacterial infection. In this study, a series of coumarin derivatives were synthesized and screened for their bactericidal activities (Ren et al. 2018; Salehian et al. 2021). The inhibitory activities of these compounds on bacterial strains were evaluated, and the related mechanism of the new compounds was explored. Firstly, the MIC values and bacterial growth curves were measured after compound treatment to evaluate the antibacterial activity in vitro. Then, the in vivo antibacterial activities of the new compounds were assessed on A. baumannii-infected mice by determining the mice survival rates, counting bacterial CFU numbers, measuring inflammatory cytokine levels, and histopathology analysis. In addition, the ROS levels in the bacterial cells were measured with DCFH-DA detection kit. Furthermore, the potential target and detailed mechanism of the new compounds during infection disease therapy were predicted and evidenced with molecular docking. After that, ADMET characteristic prediction was completed, and novel, synthesizable, drug-effective molecules were optimized with reinforcement learning study based on the probed compound as a training template. The interaction between the selected structures and target proteins was further evidenced with molecular docking. This series of innovative studies provides important theoretical and experimental data for the development of new anti-A. baumannii infection drugs.

Genotype, cervical intraepithelial neoplasia, and type-specific cervical intraepithelial neoplasia distributions in hrHPV+ cases referred to colposcopy: A multicenter study of Chinese mainland women.

To investigate age and type-specific prevalences of high-risk human papillomavirus (hrHPV) and cervical intraepithelial neoplasia (CIN) in hrHPV+ women referred to colposcopy. This is a retrospective, multicenter study. Participants were women referred to one of seven colposcopy clinics in China after testing positive for hrHPV. Patient characteristics, hrHPV genotyping, colposcopic impressions, and histological diagnoses were abstracted from electronic records. Main outcomes were age-related type-specific prevalences associated with hrHPV and CIN, and colposcopic accuracy. Among 4419 hrHPV+ women referred to colposcopy, HPV 16, 52, and 58 were the most common genotypes. HPV 16 prevalence was 39.96%, decreasing from 42.57% in the youngest group to 30.81% in the eldest group. CIN3+ prevalence was 15.00% and increased with age. As lesion severity increases, HPV16 prevalence increased while the prevalence of HPV 52 and 58 decreased. No age-based trend was identified with HPV16 prevalence among CIN2+, and HPV16-related CIN2+ was less common in women aged 60 and above (44.26%) compared to those younger than 60 years (59.61%). Colposcopy was 0.73 sensitive at detecting CIN2+ (95% confidence interval[CI]: 0.71, 0.75), with higher sensitivity (0.77) observed in HPV16+ women (95% CI: 0.74, 0.80) compared to HPV16- women (0.68, 95% CI: 0.64, 0.71). Distributions of hrHPV genotypes, CIN, and type-specific CIN in Chinese mainland hrHPV+ women referred to colposcopy were investigated for the first time. Distributions were found to be age-dependent and colposcopic performance appears related to HPV genotypes. These findings could be used to improve the management of women referred to colposcopy.

A continuously efficient O2-supplying strategy for long-term modulation of hypoxic tumor microenvironment to enhance long-acting radionuclides internal therapy.

Radionuclides internal radiotherapy (RIT) is a clinically powerful method for cancer treatment, but still poses unsatisfactory therapeutic outcomes due to the hypoxic characteristic of tumor microenvironment (TME). Catalase (CAT) or CAT-like nanomaterials can be used to enzymatically decompose TME endogenous H2O2 to boost TME oxygenation and thus alleviate the hypoxic level within tumors, but their effectiveness is still hindered by the short-lasting of hypoxia relief owing to their poor stability or degradability, thereby failing to match the long therapeutic duration of RIT. Herein, we proposed an innovative strategy of using facet-dependent CAT-like Pd-based two-dimensional (2D) nanoplatforms to continuously enhance RIT. Specifically, rationally designed 2D Pd@Au nanosheets (NSs) enable consistent enzymatic conversion of endogenous H2O2 into O2 to overcome hypoxia-induced RIT resistance. Furthermore, partially coated Au layer afford NIR-II responsiveness and moderate photothermal treatment that augmenting their enzymatic functionality. This approach with dual-effect paves the way for reshaping TME and consequently facilitating the brachytherapy ablation of cancer. Our work offers a significant advancement in the integration of catalytic nanomedicine and nuclear medicine, with the overarching goal of amplifying the clinical benefits of RIT-treated patients.

Profibrogenic macrophage-targeted delivery of mitochondrial protector via exosome formula for alleviating pulmonary fibrosis.

Pulmonary fibrosis (PF) is a devastating lung disease with limited treatment options. During this pathological process, the profibrogenic macrophage subpopulation plays a crucial role, making the characterization of this subpopulation fundamentally important. The present study revealed a positive correlation between pulmonary macrophages with higher mitochondrial mass (Mømitohigh) and fibrosis. Among the Mømitohigh subpopulation of CD206+ M2, characterized by higher expression of dynamin 1-like (Drp1), as determined by flow cytometry and RNA-seq analysis, a therapeutic intervention was developed using an exosome-based formula composed of pathfinder and therapeutics. A pathfinder exosome called "exosomeMMP19 (ExoMMP19)", was constructed to display matrix metalloproteinase-19 (MMP19) on the surface to locally break down the excessive extracellular matrix (ECM) in the fibrotic lung. A therapeutic exosome called "exosome therapeutics (ExoTx)", was engineered to display D-mannose on the surface while encapsulating siDrp1 inside. Prior delivery of ExoMMP19 degraded excessive ECM and thus paved the way for ExoTx to be delivered into Mømitohigh, where ExoTx inhibited mitochondrial fission and alleviated PF. This study has not only identified Mømitohigh as profibrotic macrophages but it has also provided a potent strategy to reverse PF via a combination of formulated exosomes.

Machine Learning Models for Blood Glucose Level Prediction in Patients With Diabetes Mellitus: Systematic Review and Network Meta-Analysis.

BACKGROUND: Machine learning (ML) models provide more choices to patients with diabetes mellitus (DM) to more properly manage blood glucose (BG) levels. However, because of numerous types of ML algorithms, choosing an appropriate model is vitally important. OBJECTIVE: In a systematic review and network meta-analysis, this study aimed to comprehensively assess the performance of ML models in predicting BG levels. In addition, we assessed ML models used to detect and predict adverse BG (hypoglycemia) events by calculating pooled estimates of sensitivity and specificity. METHODS: PubMed, Embase, Web of Science, and Institute of Electrical and Electronics Engineers Explore databases were systematically searched for studies on predicting BG levels and predicting or detecting adverse BG events using ML models, from inception to November 2022. Studies that assessed the performance of different ML models in predicting or detecting BG levels or adverse BG events of patients with DM were included. Studies with no derivation or performance metrics of ML models were excluded. The Quality Assessment of Diagnostic Accuracy Studies tool was applied to assess the quality of included studies. Primary outcomes were the relative ranking of ML models for predicting BG levels in different prediction horizons (PHs) and pooled estimates of the sensitivity and specificity of ML models in detecting or predicting adverse BG events. RESULTS: In total, 46 eligible studies were included for meta-analysis. Regarding ML models for predicting BG levels, the means of the absolute root mean square error (RMSE) in a PH of 15, 30, 45, and 60 minutes were 18.88 (SD 19.71), 21.40 (SD 12.56), 21.27 (SD 5.17), and 30.01 (SD 7.23) mg/dL, respectively. The neural network model (NNM) showed the highest relative performance in different PHs. Furthermore, the pooled estimates of the positive likelihood ratio and the negative likelihood ratio of ML models were 8.3 (95% CI 5.7-12.0) and 0.31 (95% CI 0.22-0.44), respectively, for predicting hypoglycemia and 2.4 (95% CI 1.6-3.7) and 0.37 (95% CI 0.29-0.46), respectively, for detecting hypoglycemia. CONCLUSIONS: Statistically significant high heterogeneity was detected in all subgroups, with different sources of heterogeneity. For predicting precise BG levels, the RMSE increases with a rise in the PH, and the NNM shows the highest relative performance among all the ML models. Meanwhile, current ML models have sufficient ability to predict adverse BG events, while their ability to detect adverse BG events needs to be enhanced. TRIAL REGISTRATION: PROSPERO CRD42022375250; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=375250.

Enhancing colposcopy training using a widely accessible digital education tool in China.

BACKGROUND: Colposcopy is a cornerstone of cervical cancer prevention; however, there is a global shortage of colposcopists. It is challenging to train a sufficient number of colposcopists through in-person methods, which hinders our ability to adequately diagnose and manage positive cases. A digital platform is needed to make colposcopy training more efficient, scalable, and sustainable; however, current online training programs are generally based on didactic curricula that do not incorporate image analysis training. In addition, long-term assessments of online training are not readily available. Therefore, innovative digital training and an assessment of its effectiveness are needed. OBJECTIVE: This study aimed to evaluate the short- and long-term effects of DECO (an online Digital Education Tool for Colposcopy) on trainees' colposcopy competencies and confidence. STUDY DESIGN: DECO can be used both on laptops and smartphones and comprises 4 training modules (image interpretation; terminology learning; video teaching; and collection of guidelines and typical cases) and 2 test modules. DECO was tested through a pre-post study between September and November 2022. Participants were recruited in China, and DECO training lasted 12 days. Trainees initially learned basic theory before completing training using 200 image-based cases. Pretest, posttest, and follow-up testing included 20 distinct image-based questions, and was conducted on Days 0, 13, and 60. Primary outcomes were competence and confidence scores. Secondary measures were response distributions for colposcopic diagnoses, biopsies, and DECO training satisfaction. Multilevel modeling was used to determine improvement from baseline to posttraining and follow-up for the outcomes of interest. RESULTS: Among 402 participants recruited, 96.8% (n=389) completed pretesting, 84.1% (n=338) posttesting, and 75.1% (n=302) follow-up testing. Colposcopic competence and confidence increased across this study. Diagnostic scores improved on average from 55.3 (53.7-56.9) to 70.4 (68.9-71.9). The diagnostic accuracy for normal/benign lesions, low-grade squamous intraepithelial lesions, and high-grade squamous intraepithelial lesions or worse increased by 16.9%, 13.1%, and 16.9%, respectively. Mean confidence scores increased from 48.1 (45.6-50.6) to 56.2 (54.5-57.9). These improvements remained evident 2 months after training. Trainees were also satisfied with DECO overall. Most found DECO to be scientific (82.5%), easy to use (75.2%), and clinically useful (98.4%), and would recommend it to colleagues (93.2%). CONCLUSION: DECO is a useful, acceptable digital education tool that improves colposcopy competencies and confidence. DECO could make colposcopy training more efficient, scalable, and sustainable because there are no geographic or time limitations. Therefore, DECO could be used to alleviate the shortage of trained colposcopists around the world.

Correction: Unassisted Clinicians Versus Deep Learning-Assisted Clinicians in Image-Based Cancer Diagnostics: Systematic Review With Meta-analysis.

[This corrects the article DOI: 10.2196/43832.].

Unassisted Clinicians Versus Deep Learning-Assisted Clinicians in Image-Based Cancer Diagnostics: Systematic Review With Meta-analysis.

BACKGROUND: A number of publications have demonstrated that deep learning (DL) algorithms matched or outperformed clinicians in image-based cancer diagnostics, but these algorithms are frequently considered as opponents rather than partners. Despite the clinicians-in-the-loop DL approach having great potential, no study has systematically quantified the diagnostic accuracy of clinicians with and without the assistance of DL in image-based cancer identification. OBJECTIVE: We systematically quantified the diagnostic accuracy of clinicians with and without the assistance of DL in image-based cancer identification. METHODS: PubMed, Embase, IEEEXplore, and the Cochrane Library were searched for studies published between January 1, 2012, and December 7, 2021. Any type of study design was permitted that focused on comparing unassisted clinicians and DL-assisted clinicians in cancer identification using medical imaging. Studies using medical waveform-data graphics material and those investigating image segmentation rather than classification were excluded. Studies providing binary diagnostic accuracy data and contingency tables were included for further meta-analysis. Two subgroups were defined and analyzed, including cancer type and imaging modality. RESULTS: In total, 9796 studies were identified, of which 48 were deemed eligible for systematic review. Twenty-five of these studies made comparisons between unassisted clinicians and DL-assisted clinicians and provided sufficient data for statistical synthesis. We found a pooled sensitivity of 83% (95% CI 80%-86%) for unassisted clinicians and 88% (95% CI 86%-90%) for DL-assisted clinicians. Pooled specificity was 86% (95% CI 83%-88%) for unassisted clinicians and 88% (95% CI 85%-90%) for DL-assisted clinicians. The pooled sensitivity and specificity values for DL-assisted clinicians were higher than for unassisted clinicians, at ratios of 1.07 (95% CI 1.05-1.09) and 1.03 (95% CI 1.02-1.05), respectively. Similar diagnostic performance by DL-assisted clinicians was also observed across the predefined subgroups. CONCLUSIONS: The diagnostic performance of DL-assisted clinicians appears better than unassisted clinicians in image-based cancer identification. However, caution should be exercised, because the evidence provided in the reviewed studies does not cover all the minutiae involved in real-world clinical practice. Combining qualitative insights from clinical practice with data-science approaches may improve DL-assisted practice, although further research is required. TRIAL REGISTRATION: PROSPERO CRD42021281372; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=281372.

Leonurine promotes the maturation of healthy donors and multiple myeloma patients derived-dendritic cells via the regulation on arachidonic acid metabolism.

Objective: Leonurine is a bioactive alkaloid compound extracted from Leonurus japonicus Houtt, which potentially has immunomodulatory effects. The immunomodulatory effect and mechanism of leonurine on monocyte derived dendritic cells (moDCs) from healthy donors (HDs) and multiple myeloma (MM) patients were investigated for the first time. Methods: Peripheral blood from HDs and MM patients was isolated for peripheral blood mononuclear cells (PBMCs). The generation of moDCs was conducted by the incubation of monocytes from PBMCs in the medium consisting of RPMI 1640 medium, 2 mmol/L L-glutamine, 5% human serum, 800 U/mL GM-CSF, 500 U/mL IL-4, 100 U/mL penicillin and 0.1 mg/mL streptomycin. During the incubation of 7 days, the cells were administrated with 1 µM leonurine or 1 × PBS as the control group. On the 8th day, cells were harvested. The expression of maturation associated surface markers CD40, CD83, and HLA-DR on moDCs was analyzed by flow cytometry. Moreover, moDCs with or without 1 µM leonurine administration were evaluated by LC-MS/MS for metabolomics which was further analyzed for the potential mechanism of leonurine on moDCs. Results: The proportion of moDCs in the harvested cells was significantly higher in the HD group (n = 14) than in the MM patient group (n = 11) (p = 0.000). Leonurine significantly enhanced the median fluorescence intensity of CD83, HLA-DR and CD40 expression on HD-moDCs (n = 14; p = 0.042, p = 0.013, p = 0.084) as well as MM paitent-moDCs (n = 11; p = 0.020, p = 0.006, p = 0.025). The metabolomics data showed that in moDCs (HD, n = 15), 18 metabolites in the pathway of arachidonic acid metabolism showed significant differences between the leonurine group and the control group (VIP all >1 and P all <0.05). To be specific, 6-Keto-PGE1, 8,9-DHET, 11 (R)-HETE, 12-Keto-LTB4, 12-OxoETE, 15 (S)-HETE, 15-Deoxy-Delta12,14-PGJ2, 15-Keto-PGF2a, 20-COOH-LTB4, Lecithin, PGA2, PGB2, PGE2, PGF2a, PGG2, Prostacyclin were significantly upregulated in the leonurine group than in the control group, while Arachidonic Acid and TXB2 were significantly downregulated in the leonurine group than in the control group. Conclusion: Leonurine significantly promotes the maturation of moDCs derived from HDs and MM patients, the mechanism of which is related to arachidonic acid metabolism.

The Causal Relationship between the Morning Chronotype and the Gut Microbiota: A Bidirectional Two-Sample Mendelian Randomization Study.

BACKGROUND: Numerous observational studies have documented an association between the circadian rhythm and the composition of the gut microbiota. However, the bidirectional causal effect of the morning chronotype on the gut microbiota is unknown. METHODS: A two-sample Mendelian randomization study was performed, using the summary statistics of the morning chronotype from the European Consortium and those of the gut microbiota from the largest available genome-wide association study meta-analysis, conducted by the MiBioGen consortium. The inverse variance-weighted (IVW), weighted mode, weighted median, MR-Egger regression, and simple mode methods were used to examine the causal association between the morning chronotype and the gut microbiota. A reverse Mendelian randomization analysis was conducted on the gut microbiota, which was identified as causally linked to the morning chronotype in the initial Mendelian randomization analysis. Cochran's Q statistics were employed to assess the heterogeneity of the instrumental variables. RESULTS: Inverse variance-weighted estimates suggested that the morning chronotype had a protective effect on Family Bacteroidaceae (ß = -0.072; 95% CI: -0.143, -0.001; p = 0.047), Genus Parabacteroides (ß = -0.112; 95% CI: -0.184, -0.039; p = 0.002), and Genus Bacteroides (ß = -0.072; 95% CI: -0.143, -0.001; p = 0.047). In addition, the gut microbiota (Family Bacteroidaceae (OR = 0.925; 95% CI: 0.857, 0.999; p = 0.047), Genus Parabacteroides (OR = 0.915; 95% CI: 0.858, 0.975; p = 0.007), and Genus Bacteroides (OR = 0.925; 95% CI: 0.857, 0.999; p = 0.047)) demonstrated positive effects on the morning chronotype. No significant heterogeneity in the instrumental variables, or in horizontal pleiotropy, was found. CONCLUSION: This two-sample Mendelian randomization study found that Family Bacteroidaceae, Genus Parabacteroides, and Genus Bacteroides were causally associated with the morning chronotype. Further randomized controlled trials are needed to clarify the effects of the gut microbiota on the morning chronotype, as well as their specific protective mechanisms.

Pomalidomide enhances the maturation of dendritic cells derived from healthy donors and multiple myeloma patients.

Objective: To explore the effect of pomalidomide on the maturation of monocyte-derived dendritic cells (moDCs) from healthy donors (HDs) and multiple myeloma (MM) patients. Methods: MoDCs were generated by the incubation of monocytes from peripheral blood mononuclear cells (PBMCs) for 7 days in a medium consisting of 800 U/ml granulocyte-macrophage colony stimulating factor (GM-CSF), 500 U/ml interleukin-4 (IL-4), RPMI 1,640 medium, 5% human serum, 100 U/ml penicillin and 0.1 mg/ml streptomycin. Meanwhile, the incubation system was administrated with 10 µM pomalidomide or 1 × PBS as the control group. On the eighth day, cells were harvested and analyzed by flow cytometry. The CD80+CD86+ cell population in total cells was gated as moDCs in the FACS analyzing system. After that, the expression of CD40 and HLA-DR on moDCs was analyzed. Meanwhile, the supernatant from the incubation system was evaluated for the secretion of cytokines interleukin-12 (IL-12), tumor necrosis factor-α (TNF-α), and macrophage inflammatory protein 1α (MIP-1α) by enzyme-linked immunosorbent assay (ELISA). Results: When analyzing all the HD-moDCs together (n = 15), pomalidomide significantly increased the mean fluorescence intensity (MFI) of CD40 expression and HLA-DR expression on moDCs compared with the control group (p = 0.003, p = 0.040). Meanwhile, the proportion of CD40+ moDCs and HLA-DR+ moDCs in total moDCs was significantly higher in the pomalidomide group than in the control group (p = 0.008, p = 0.032). When analyzing all MM patient-moDCs together (n = 11), pomalidomide significantly increased the MFI of CD40 expression and HLA-DR expression on moDCs compared with the control group (p = 0.047, p = 0.006). Meanwhile, the proportion of HLA-DR+ moDCs in total DCs was significantly higher in the pomalidomide group than in the control group (p < 0.001). Moreover, HD-moDCs (n = 8) treated with pomalidomide secreted 192% IL-12, 110% TNF-α, and 112% MIP-1α of the untreated moDCs (p = 0.020, p = 0.006, p = 0.055). However, when analyzing MM patient-moDCs (n = 10) together, the secretion of IL-12, TNF-α and MIP-1α from moDCs showed no significant difference between the pomalidomide group and the control group (p = 0.458, p = 0.377, p = 0.248). Conclusion: In vitro, 10 µM pomalidomide enhances the maturation of moDCs derived from both HDs and MM patients. Pomalidomide shows potential to be applied as a DC adjuvant for DC-based immunotherapy, such as the DC vaccine and DC cell therapy in MM.

Cyanidin Chloride Improves LPS-Induced Depression-Like Behavior in Mice by Ameliorating Hippocampal Inflammation and Excitotoxicity.

Depression is a global disease that places a significant burden on human health. Neuroinflammation and disturbance of glutamate metabolism in brain regions, such as the hippocampus, play vital roles in the development of depression. Previous studies have shown that cyanidin chloride (Cycl) has anti-inflammatory and antioxidant properties with neuroprotective effects in peripheral tissues. However, the effects of Cycl on depression and the possible mechanism by which this compound targets brain regions remain less elucidated. We investigated the role of Cycl in lipopolysaccharide (LPS)-induced depression and examined the influence of the drug on central inflammation and the expression of excitatory amino acid transporters in the hippocampus. We found that prophylactic i.p. application of Cycl at 20 or 40 mg/kg for 5 days significantly reduced the immobility time assessed by the tail suspension test (TST) and forced swim test (FST) in LPS-challenged mice, suggesting an effective antidepressant activity of the drug. Western blotting and immunofluorescence staining in the hippocampus revealed that Cycl inhibited the upregulation of proinflammatory cytokines, including TNF-α and IL-6, and suppressed the hyperactivity of microglia induced by LPS, indicating an anti-inflammatory role in the hippocampus. Moreover, treatment with Cycl also recovered the downregulated expression of glial fibrillary acidic protein (GFAP), brain-derived neurotrophic factor (BDNF), and glutamate-aspartate transporter (GLAST) and excitatory amino acid transporter 2 (EAAT2), two members in the excitatory amino acid transporter family. The role of Cycl was also verified in cultured BV2 and U251 cells. In conclusion, the present in vivo and in vitro studies demonstrate that Cycl exerts potent antidepressant action in an LPS-induced depression model and the underlying mechanism is associated with reduced hippocampal inflammation, improved neurotrophic function, and attenuated excitotoxicity induced by glutamate.

Development and validation of a predictive model for endocervical curettage in patients referred for colposcopy: A multicenter retrospective diagnostic study in China.

Objective: This study aimed to develop a nomogram that can predict occult high-grade squamous intraepithelial lesions or worse (HSIL+) and determine the need for endocervical curettage (ECC) in patients referred for colposcopy. Methods: This retrospective multicenter study included 4,149 patients who were referred to any one of six tertiary hospitals in China for colposcopy between January 2020 and November 2021 because of abnormal screening results. ECC data were extracted from the medical records. Univariate and multivariate logistic regression analyses were performed to identify factors that could predict HSIL+ on ECC. Patients were randomly assigned to a training set or to an internal validation set for performance and comparability testing. The model was externally validated and tested in patients from two additional hospitals. The nomogram was assessed in terms of discrimination and calibration and subjected to decision curve analysis. Results: HSIL+ was found on ECC in 38.8% (n=388) of cases. Our predictive nomogram included age group, cytology, human papillomavirus (HPV) status, visibility of the cervix and colposcopic impression. The nomogram had good overall discrimination, which was internally validated [area under the receiver-operator characteristic (AUC), 0.839; 95% confidence interval (95% CI), 0.773-0.904]. In terms of external validation, the AUC was 0.843 (95% CI, 0.773-0.912) for the consecutive sample and 0.843 (95% CI, 0.783-0.902) for the comparative sample. Calibration analysis suggested good consistency between predicted and observed probabilities. Decision curve analysis suggested this nomogram would be clinically useful with almost the entire range of threshold probabilities. Conclusions: This internally and externally validated nomogram can be easily applied and incorporates multiple clinically relevant variables that can be used to identify patients with occult HSIL+ who need ECC.