Deep learning-guided adjuvant chemotherapy selection for elderly patients with breast cancer.

PURPOSE: The efficacy of adjuvant chemotherapy in elderly breast cancer patients is currently controversial. This study aims to provide personalized adjuvant chemotherapy recommendations using deep learning (DL). METHODS: Six models with various causal inference approaches were trained to make individualized chemotherapy recommendations. Patients who received actual treatment recommended by DL models were compared with those who did not. Inverse probability treatment weighting (IPTW) was used to reduce bias. Linear regression, IPTW-adjusted risk difference (RD), and SurvSHAP(t) were used to interpret the best model. RESULTS: A total of 5352 elderly breast cancer patients were included. The median (interquartile range) follow-up time was 52 (30-80) months. Among all models, the balanced individual treatment effect for survival data (BITES) performed best. Treatment according to following BITES recommendations was associated with survival benefit, with a multivariate hazard ratio (HR) of 0.78 (95% confidence interval (CI): 0.64-0.94), IPTW-adjusted HR of 0.74 (95% CI: 0.59-0.93), RD of 12.40% (95% CI: 8.01-16.90%), IPTW-adjusted RD of 11.50% (95% CI: 7.16-15.80%), difference in restricted mean survival time (dRMST) of 12.44 (95% CI: 8.28-16.60) months, IPTW-adjusted dRMST of 7.81 (95% CI: 2.93-11.93) months, and p value of the IPTW-adjusted Log-rank test of 0.033. By interpreting BITES, the debiased impact of patient characteristics on adjuvant chemotherapy was quantified, which mainly included breast cancer subtype, tumor size, number of positive lymph nodes, TNM stages, histological grades, and surgical type. CONCLUSION: Our results emphasize the potential of DL models in guiding adjuvant chemotherapy decisions for elderly breast cancer patients.

ROS-Targeted Depression Therapy via BSA-Incubated Ceria Nanoclusters.

Depression is one of the most fatal mental diseases, and there is currently a lack of efficient drugs for the treatment of depression. Emerging evidence has indicated oxidative stress as a key pathological feature of depression. We targeted reactive oxygen species (ROS) and synthesized CeO2@BSA nanoclusters as a novel antidepression nanodrug via a convenient, green, and highly effective bovine serum albumin (BSA) incubation strategy. CeO2@BSA has ultrasmall size (2 nm) with outstanding ROS scavenging and blood-brain barrier crossing capacity, rapid metabolism, and negligible adverse effects in vitro and in vivo. CeO2@BSA administration alleviates depressive behaviors and depression-related pathological changes of the chronic restraint stress-induced depressive model, suggesting promising therapeutic effects of CeO2@BSA for the treatment of depression. Our study proved the validity by directly using nanodrugs as antidepression drugs instead of using them as a nanocarrier, which greatly expands the application of nanomaterials in depression treatment.

Insulin-incubated palladium clusters promote recovery after brain injury.

Traumatic brain injury (TBI) is a cause of disability and death worldwide, but there are currently no specific treatments for this condition. Release of excess reactive oxygen species (ROS) in the injured brain leads to a series of pathological changes; thus, eliminating ROS could be a potential therapeutic strategy. Herein, we synthesized insulin-incubated ultrasmall palladium (Pd@insulin) clusters via green biomimetic chemistry. The Pd@insulin clusters, which were 3.2 nm in diameter, exhibited marked multiple ROS-scavenging ability testified by the theoretical calculation. Pd@insulin could be rapidly excreted via kidney-urine metabolism and induce negligible adverse effects after a long-time treatment in vivo. In a TBI mouse model, intravenously injected Pd@insulin clusters aggregated in the injured cortex, effectively suppressed excessive ROS production, and significantly rescued motor function, cognition and spatial memory. We found that the positive therapeutic effects of the Pd@insulin clusters were mainly attributed to their ROS-scavenging ability, as they inhibited excessive neuroinflammation, reduced cell apoptosis, and prevented neuronal loss. Therefore, the ability of Pd@insulin clusters to effectively eliminate ROS, as well as their simple structure, easy synthesis, low toxicity, and rapid metabolism may facilitate their clinical translation for TBI treatment.

Domino Annulation Reaction of Sulfur Ylides and Morita-Baylis-Hillman Carbonates of Isatins: Synthesis of Oxospiro[bicyclo[3.1.0]hexane-6,3'-indolin] Scaffolds.

A sequential [3 + 2]/[2 + 1] annulation domino reaction of crotonate-derived sulfur ylides and Morita-Baylis-Hillman carbonates of isatins for the construction of oxospiro[bicyclo[3.1.0]hexane-6,3'-indolin] scaffolds in moderate to good yields with almost 1:1 diastereoselectivity has been developed. Mild reaction conditions and readily accessible starting materials as well as excellent functional group compatibility render this transformation a powerful tool for the synthesis of spirocyclopropyloxindoles. A gram-scale synthetic procedure was also successfully carried out and a plausible reaction mechanism could be proposed.

Optimizing adjuvant treatment options for patients with glioblastoma.

Background: This study focused on minimizing the costs and toxic effects associated with unnecessary chemotherapy. We sought to optimize the adjuvant therapy strategy, choosing between radiotherapy (RT) and chemoradiotherapy (CRT), for patients based on their specific characteristics. This selection process utilized an innovative deep learning method. Methods: We trained six machine learning (ML) models to advise on the most suitable treatment for glioblastoma (GBM) patients. To assess the protective efficacy of these ML models, we employed various metrics: hazards ratio (HR), inverse probability treatment weighting (IPTW)-adjusted HR (HRa), the difference in restricted mean survival time (dRMST), and the number needed to treat (NNT). Results: The Balanced Individual Treatment Effect for Survival data (BITES) model emerged as the most effective, demonstrating significant protective benefits (HR: 0.53, 95% CI, 0.48-0.60; IPTW-adjusted HR: 0.65, 95% CI, 0.55-0.78; dRMST: 7.92, 95% CI, 7.81-8.15; NNT: 1.67, 95% CI, 1.24-2.41). Patients whose treatment aligned with BITES recommendations exhibited notably better survival rates compared to those who received different treatments, both before and after IPTW adjustment. In the CRT-recommended group, a significant survival advantage was observed when choosing CRT over RT (p < 0.001). However, this was not the case in the RT-recommended group (p = 0.06). Males, older patients, and those whose tumor invasion is confined to the ventricular system were more frequently advised to undergo RT. Conclusion: Our study suggests that BITES can effectively identify GBM patients likely to benefit from CRT. These ML models show promise in transforming the complex heterogeneity of real-world clinical practice into precise, personalized treatment recommendations.

Recent progress on defect-engineering in ferroelectric HfO2: The next step forward via multiscale structural optimization.

The discovery of unconventional scale-free ferroelectricity in HfO2-based fluorite thin films has attracted great attention in recent years for their promising applications in low-power logic and nonvolatile memories. The ferroelectricity of HfO2 is intrinsically originated from the widely accepted ferroelectric metastable orthorhombic Pca21 phase. In the last decade, defect-doping/solid solution has shown excellent prospects in enhancing and stabilizing the ferroelectricity via isovalent or aliovalent defect-engineering. Here, the recent advances in defect-engineered HfO2-based ferroelectrics are first reviewed, including progress in mono-ionic doping and mixed ion-doping. Then, the defect-lattice correlation, the point-defect promoted phase transition kinetics, and the interface-engineered dynamic behaviour of oxygen vacancy are summarized. In addition, thin film preparation and ion bombardment doping are summarized. Finally, the outlook and challenges are discussed. A multiscale structural optimization approach is suggested for further property optimization. This article not only covers an overview of the state-of-art advances of defects in fluorite ferroelectrics, but also future prospects that may inspire their further property-optimization via defect-engineering.

Individualized survival prediction and surgery recommendation for patients with glioblastoma.

Background: There is a lack of individualized evidence on surgical choices for glioblastoma (GBM) patients. Aim: This study aimed to make individualized treatment recommendations for patients with GBM and to determine the importance of demographic and tumor characteristic variables in the selection of extent of resection. Methods: We proposed Balanced Decision Ensembles (BDE) to make survival predictions and individualized treatment recommendations. We developed several DL models to counterfactually predict the individual treatment effect (ITE) of patients with GBM. We divided the patients into the recommended (Rec.) and anti-recommended groups based on whether their actual treatment was consistent with the model recommendation. Results: The BDE achieved the best recommendation effects (difference in restricted mean survival time (dRMST): 5.90; 95% confidence interval (CI), 4.40-7.39; hazard ratio (HR): 0.71; 95% CI, 0.65-0.77), followed by BITES and DeepSurv. Inverse probability treatment weighting (IPTW)-adjusted HR, IPTW-adjusted OR, natural direct effect, and control direct effect demonstrated better survival outcomes of the Rec. group. Conclusion: The ITE calculation method is crucial, as it may result in better or worse recommendations. Furthermore, the significant protective effects of machine recommendations on survival time and mortality indicate the superiority of the model for application in patients with GBM. Overall, the model identifies patients with tumors located in the right and left frontal and middle temporal lobes, as well as those with larger tumor sizes, as optimal candidates for SpTR.

Personalized surgical recommendations and quantitative therapeutic insights for patients with metastatic breast cancer: Insights from deep learning.

Background: The role of surgery in metastatic breast cancer (MBC) is currently controversial. Several novel statistical and deep learning (DL) methods promise to infer the suitability of surgery at the individual level. Objective: The objective of this study was to identify the most applicable DL model for determining patients with MBC who could benefit from surgery and the type of surgery required. Methods: We introduced the deep survival regression with mixture effects (DSME), a semi-parametric DL model integrating three causal inference methods. Six models were trained to make individualized treatment recommendations. Patients who received treatments in line with the DL models' recommendations were compared with those who underwent treatments divergent from the recommendations. Inverse probability weighting (IPW) was used to minimize bias. The effects of various features on surgery selection were visualized and quantified using multivariate linear regression and causal inference. Results: In total, 5269 female patients with MBC were included. DSME was an independent protective factor, outperforming other models in recommending surgery (IPW-adjusted hazard ratio [HR] = 0.39, 95% confidence interval [CI]: 0.19-0.78) and type of surgery (IPW-adjusted HR = 0.66, 95% CI: 0.48-0.93). DSME was superior to other models and traditional guidelines, suggesting a higher proportion of patients benefiting from surgery, especially breast-conserving surgery. The debiased effect of patient characteristics, including age, tumor size, metastatic sites, lymph node status, and breast cancer subtypes, on surgery decision was also quantified. Conclusions: Our findings suggested that DSME could effectively identify patients with MBC likely to benefit from surgery and the specific type of surgery needed. This method can facilitate the development of efficient, reliable treatment recommendation systems and provide quantifiable evidence for decision-making.

Unveiling the binding details and esterase-like activity effect of methyl yellow on human serum albumin: spectroscopic and simulation study.

The food sector uses methyl yellow (MY) extensively as a colorant. The primary transporter in vivo that influences MY absorption, metabolism, distribution, and excretion is human serum albumin (HSA). Exploring the binding process and looking at how HSA and MY work physiologically at the molecular level is therefore very important. Experiments using steady-state fluorescence and fluorescence lifetimes proved that HSA and MY's quenching mechanisms were static. The HSA-MY complex's binding constant was estimated using thermodynamic parameters to be around 104 M-1. The hydrophobic forces were a major factor in the binding process, as evidenced by the negative ΔG, positive ΔH, and ΔS, which suggested that this contact was spontaneous. Site tests showed that MY linked to HSA's site I. Circular dichroism and three-dimensional fluorescence analysis revealed that the 1.33% α-helix content dropped and the amino acid microenvironment altered. While HSA's protein surface hydrophobicity decreased when engaging MY, the binding of MY to HSA reduced in the presence of urea. The stability of the system was assessed using molecular modeling. Additionally, HSA's esterase-like activity decreased when MY was present, and Ibf/Phz affected the inhibition mechanism of MY on HSA. These findings offer a distinctive perspective for comprehending the structure and functioning of HSA and evaluating the safety of MY.

Analyzing and predicting the risk of death in stroke patients using machine learning.

Background: Stroke is an acute disorder and dysfunction of the focal neurological system that has long been recognized as one of the leading causes of death and severe disability in most regions globally. This study aimed to supplement and exploit multiple comorbidities, laboratory tests and demographic factors to more accurately predict death related to stroke, and furthermore, to make inferences about the heterogeneity of treatment in stroke patients to guide better treatment planning. Methods: We extracted data from the Medical Information Mart from the Intensive Care (MIMIC)-IV database. We compared the distribution of the demographic factors between the control and death groups. Subsequently, we also developed machine learning (ML) models to predict mortality among stroke patients. Furthermore, we used meta-learner to recognize the heterogeneity effects of warfarin and human albumin. We comprehensively evaluated and interpreted these models using Shapley Additive Explanation (SHAP) analysis. Results: We included 7,483 patients with MIMIC-IV in this study. Of these, 1,414 (18.9%) patients died during hospitalization or 30 days after discharge. We found that the distributions of age, marital status, insurance type, and BMI differed between the two groups. Our machine learning model achieved the highest level of accuracy to date in predicting mortality in stroke patients. We also observed that patients who were consistent with the model determination had significantly better survival outcomes than the inconsistent population and were better than the overall treatment group. Conclusion: We used several highly interpretive machine learning models to predict stroke prognosis with the highest accuracy to date and to identify heterogeneous treatment effects of warfarin and human albumin in stroke patients. Our interpretation of the model yielded a number of findings that are consistent with clinical knowledge and warrant further study and verification.

MOFs-alginate/polyacrylic acid/poly (ethylene imine) heparin-mimicking beads as a novel hemoadsorbent for bilirubin removal in vitro and vivo models.

Metal-organic frameworks (MOFs) have a potential application in blood purification, but their microcrystalline nature has hampered their industrial application. Here, novel MOFs-polymer beads based on UiO, sodium alginate, polyacrylic acid, and poly (ethylene imine) were prepared and applied as a whole blood hemoadsorbent for the first time. The amidation among polymers immobilized UiO66-NH2 into the network of the optimal product (SAP-3), and the NH2 of UiO66-NH2 significantly increased the removal rate (70 % within 5 min) of SAP-3 on bilirubin. The adsorption of SAP-3 on bilirubin mainly obeyed the pseudo-second-order kinetic, Langmuir isotherm and Thomas models with a maximum adsorption capacity (qm) of 63.97 mg·g-1. Experimental and density functional theory simulation results show that bilirubin was mainly adsorbed by UiO66-NH2via electrostatic force, hydrogen bonding, and π-π interactions. Notably, the adsorption in vivo show that the total bilirubin removal rate in the whole blood of the rabbit model was up to 42 % after 1 h of adsorption. Given its excellent stability, cytotoxicity, and hemocompatibility, SAP-3 has a great potential in hemoperfusion therapy. This study proposes an effective strategy for settling the powder property of MOFs and could provide experimental and theoretical references for application of MOFs in blood purification.

Reasoning and causal inference regarding surgical options for patients with low-grade gliomas using machine learning: A SEER-based study.

BACKGROUND: Due to the heterogeneity of low-grade gliomas (LGGs), the lack of randomized control trials, and strong clinical evidence, the effect of the extent of resection (EOR) is currently controversial. AIM: To determine the best choice between subtotal resection (STR) and gross-total resection (GTR) for individual patients and to identify features that are potentially relevant to treatment heterogeneity. METHODS: Patients were enrolled from the SEER database. We used a novel DL approach to make treatment recommendations for patients with LGG. We also made causal inference of the average treatment effect (ATE) of GTR compared with STR. RESULTS: The patients were divided into the Consis. and In-consis. groups based on whether their actual treatment and model recommendations were consistent. Better brain cancer-specific survival (BCSS) outcomes in the Consis. group was observed. Overall, we also identified two subgroups that showed strong heterogeneity in response to GTR. By interpreting the models, we identified numerous variables that may be related to treatment heterogeneity. CONCLUSIONS: This is the first study to infer the individual treatment effect, make treatment recommendation, and guide surgical options through deep learning approach in LGG research. Through causal inference, we found that heterogeneous responses to STR and GTR exist in patients with LGG. Visualization of the model yielded several factors that contribute to treatment heterogeneity, which are worthy of further discussion.

The potency of psychiatric questionnaires to distinguish major mental disorders in Chinese outpatients.

Background: Considering the huge population in China, the available mental health resources are inadequate. Thus, our study aimed to evaluate whether mental questionnaires, serving as auxiliary diagnostic tools, have efficient diagnostic ability in outpatient psychiatric services. Methods: We conducted a retrospective study of Chinese psychiatric outpatients. Altogether 1,182, 5,069, and 4,958 records of Symptom Checklist-90 (SCL-90), Hamilton Anxiety Rating Scale (HAM-A), and Hamilton Depression Rating Scale (HAM-D), respectively, were collected from March 2021 to July 2022. The Mann-Whitney U test was applied to subscale scores and total scores of SCL-90, HAM-A, and HAM-D between the two sexes (male and female groups), different age groups, and four diagnostic groups (anxiety disorder, depressive disorder, bipolar disorder, and schizophrenia). Kendall's tau coefficient analysis and machine learning were also conducted in the diagnostic groups. Results: We found significant differences in most subscale scores for both age and gender groups. Using the Mann-Whitney U test and Kendall's tau coefficient analysis, we found that there were no statistically significant differences in diseases in total scale scores and nearly all subscale scores. The results of machine learning (ML) showed that for HAM-A, anxiety had a small degree of differentiation with an AUC of 0.56, while other diseases had an AUC close to 0.50. As for HAM-D, bipolar disorder was slightly distinguishable with an AUC of 0.60, while the AUC of other diseases was lower than 0.50. In SCL-90, all diseases had a similar AUC; among them, bipolar disorder had the lowest score, schizophrenia had the highest score, while anxiety and depression both had an AUC of approximately 0.56. Conclusion: This study is the first to conduct wide and comprehensive analyses on the use of these three scales in Chinese outpatient clinics with both traditional statistical approaches and novel machine learning methods. Our results indicated that the univariate subscale scores did not have statistical significance among our four diagnostic groups, which highlights the limit of their practical use by doctors in identifying different mental diseases in Chinese outpatient psychiatric services.

Astragaloside IV attenuates ferroptosis after subarachnoid hemorrhage via Nrf2/HO-1 signaling pathway.

Subarachnoid hemorrhage (SAH) is a severe type of stroke featuring exceptionally high rate of morbidity and mortality due to the lack of effective management. Ferroptosis can be defined as a novel iron-dependent programmed cell death in contrast to classical apoptosis and necrosis. Astragaloside IV (AS-IV) is an active ingredient extracted from Astragalus membranaceus with established therapeutic effect on CNS diseases. However, the exact role of ferroptosis in Astragaloside IV-mediated neuroprotection after SAH is yet to be demonstrated. In the present study, the SAH model of SD male rats with endovascular perforation was used to gauge the neuroprotective effect of AS-IV on SAH-induced early brain injury (EBI) and to clarify the potential molecular mechanism. We found that the induction of SAH reduced the levels of SLC7A11 and glutathione peroxidase 4 (GPX4) in the brain, exacerbated iron accumulation, enhanced lipid reactive oxygen species (ROS) level, and stimulated neuronal ferroptosis. However, the administration of AS-IV and the ferroptosis inhibitor Ferrostatin-1 (Fer-1) enhanced the antioxidant capacity after SAH and suppressed the accumulation of lipid peroxides. Meanwhile, AS-IV triggered Nrf2/HO-1 signaling pathway and alleviated ferroptosis due to the induction of SAH. The Nrf2 inhibitor ML385 blocked the beneficial effects of neuroprotection. These results consistently suggest that ferroptosis is profoundly implicated in facilitating EBI in SAH, and that AS-IV thwarts the process of ferroptosis in SAH by activating Nrf2/HO-1 pathway.

Controlled Decompression Alleviates Brain Injury via Attenuating Oxidative Damage and Neuroinflammation in Acute Intracranial Hypertension.

BACKGROUND: The benefits of controlled decompression (CDC) for patients with acute intracranial hypertension especially in terms of alleviating the complications caused by rapid decompression (RDC) have been confirmed by clinical studies. This study is aimed at evaluating the therapeutic potency of CDC with ubiquitin C-terminal hydrolase-L1 (UCH-L1) and glial fibrillary acidic protein (GFAP) by investigating the potential molecular mechanism in the acute intracranial hypertension (AICH) rabbit model. METHODS: Male New Zealand white rabbits were randomly subdivided into the sham-operated (SH) group, CDC group, and RDC group. Blood plasma samples and brain tissue were collected 2 days before operation (baseline) and at 3, 6, 24, and 72 hours after operation to measure the levels of UCH-L1, GFAP, oxidative stress indicators, and inflammatory cytokines by performing ELISA or Western blot. The neurological score of the rabbits and brain water content was graded 24 h after surgery. qPCR, immunofluorescence, and FJ-C staining were conducted. RESULTS: CDC improved neurological function, lowered brain water content, ameliorated neuronal degeneration, attenuated oxidative damage, and inflammatory responses to a greater extent than RDC. Plasma UCH-L1 level was significantly lower in the CDC group at 3 h postoperatively than in the RDC group. CDC reduced plasma GFAP levels to various degrees at 3 h, 6 h, and 24 h postoperatively compared with RDC. Immunofluorescence confirmed that the expression of UCH-L1 and GFAP in the cortex of the CDC group was lower than that of the RDC group. CONCLUSIONS: Our data collectively demonstrate that CDC could attenuate oxidative damage and inflammatory responses, downregulate UCH-L1 and GFAP levels, and contribute to an improved neuroprotective effect compared with RDC.

Edonerpic maleate regulates glutamate receptors through CRMP2- and Arc-mediated mechanisms in response to brain trauma.

Dysfunction of ionotropic glutamate receptors (iGluRs) is a key molecular mechanism of excitotoxic neuronal injury following traumatic brain injury (TBI). Edonerpic maleate is a low molecular-weight compound that was screened as a candidate neuroprotective agent. In this study, we investigated its effects on TBI and GluRs signaling. Traumatic neuronal injury (TNI) induced by scratch followed by glutamate treatment was performed to mimic TBI in vitro. Edonerpic maleate at 1 and 10 µM exerted protective activity when it was added within 2 h following injury. The protective activities were also confirmed by the reduction of lipid peroxidation and oxidative stress. In addition, edonerpic maleate inhibited the expression of surface NR2B, total GluR1, and surface GluR1, and mitigated the intracellular Ca2+ responses following injury in vitro. Western blot analysis showed that edonerpic maleate reduced the cleavage of collapsing response mediator protein 2 (CRMP2), but increased the expression of postsynaptic protein Arc. By using gene overexpression and silencing technologies, CRMP2 was overexpressed and Arc was knockdown in cortical neurons. The results showed that the effect of edonerpic maleate on NMDA receptor expression was mediated by CRMP2, whereas the edonerpic maleate-induced AMPA receptor regulation was dependent on Arc activation. In in vivo TBI model, 30 mg/kg edonerpic maleate alleviated the TBI-induced brain edema, neuronal loss, and microglial activation, with no effect on locomotor function at 24 h. However, edonerpic maleate improves long-term neurological function after TBI. Furthermore, edonerpic maleate inhibited CRMP2 cleavage but increased Arc activation in vivo. In summary, our results identify edonerpic maleate as a clinically potent small compound with which to attenuate TBI-related brain damage through regulating GluRs signaling.

Propofol Inhibits Microglial Activation via miR-106b/Pi3k/Akt Axis.

Propofol is an established intravenous anesthetic agent with potential neuroprotective effects. In this study, we investigated the roles and the underlying mechanisms of propofol in inhibiting the pro-inflammatory responses of microglia. Propofol significantly reduced the messenger RNA (mRNA) levels of Tnf, Nos2, and NF-κB pathway related genes Ticam1, Myd88, Irf3, and Nfkb1 in lipopolysaccharide (LPS)-treated primary microglia. After screening the miRNA profiles in microglia under LPS and propofol treatment conditions, we found propofol abrogated the LPS-induced misexpression of miRNAs including miR-106b, miR-124, miR-185, and miR-9. Perturbation of function approaches suggested miR-106b as the core miRNA that mediated the anti-inflammatory effects of propofol on microglial activation. RNA sequencing (RNA-seq) analysis further identified Pi3k/Akt signaling as one of the most affected pathways after miR-106b perturbation of function. The treatment of Pi3k/Akt signaling agonist 740Y-P elevated miR-106b-reduced Akt phosphorylation and abolished the inhibitory effects of miR-106b on the pro-inflammatory responses of microglia. Our results suggest propofol inhibits microglial activation via miR-106b/Pi3k/Akt axis, shedding light on a novel molecular mechanism of propofol-mediated immunomodulatory effects and implying propofol as potential therapeutics for treating neuroinflammation-related neurodegenerative diseases.

Safety, Tolerability, and Immunogenicity of COVID-19 Vaccines: A Systematic Review and Meta-Analysis.

We aimed to summarize reliable medical evidence by the meta-analysis of all published clinical trials that investigated the safety, tolerability, and immunogenicity of vaccine candidates against coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The PubMed, Cochrane Library, EMBASE, and medRxiv databases were used to select the studies. 7094 articles were identified initially and 43 were retrieved for more detailed evaluation. 5 randomized, double-blind, placebo-controlled trials were selected. A total of 1604 subjects with either vaccines or placebo infections were included in the meta-analysis within the scope of these articles. According to the results, there is an increase in total adverse events for subjects with either low (95% CI: 1.90-4.29) or high (CI: 2.65-5.63) dose vaccination. The adverse effects of COVID-19 vaccine are mainly local ones including pain, itching, and redness, and no significant difference was identified in the systemic reactions. All adverse effects were transient and resolved within a few days. Moreover, the neutralizing and IgG antibody levels post different dose vaccinations were all significantly increased at day 14/21 (P = 0.0004 and P = 0.0003, respectively) and day 28/35 (P < 0.00001) in vaccine groups compared to placebo controls. Besides, the levels of neutralizing and IgG antibodies were also elevated significantly at from day 14 to 35, versus day 0 (All P < 0.001). In conclusion, our analysis suggests that the current COVID-19 vaccine candidates are safe, tolerated, and immunogenic, which provides important information for further development, evaluation, and clinical application of COVID-19 vaccine.

Subthreshold diode micropulse laser versus conventional laser photocoagulation monotherapy or combined with anti-VEGF therapy for diabetic macular edema: A Bayesian network meta-analysis.

AIMS: To assess the effects of laser photocoagulation as monotherapy or adjuvant therapy for the treatment of DME. METHODS: A search of the Cochrane Library, Pubmed, Embase, and the clinicaltrial.gov registry for randomized clinical trials comparing any two treatments of interest (SDMLP monotherapy, CLP monotherapy, CLP plus anti-VEGF therapy) was performed. Data were collected and pooled by Bayesian network meta-analyses which accounts for both direct and indirect comparisons. The primary outcome was the mean change in best-corrected visual acuity measured by the logarithm of the minimal angle of resolution units. The secondary outcome was the mean change in central macular thickness from baseline to month 12. RESULTS: Ranibizumab therapy combined with CLP was more effective than SDMLP alone (MD, -0.396; 95% CrI, -0.746 to -0.062) and CLP alone (MD, -0.621; 95% CrI, -0.823 to -0.431). There was no apparent difference of efficacy between SDMLP alone and CLP alone (MD, -0.225; 95% CrI, -0.501 to 0.058). There was no apparent difference of efficacy between SDMLP alone and Bevacizumab therapy combined with CLP (MD, -0.003, 95% CrI, -0.815 to 0.805). CONCLUSION: There was no apparent difference on improving vision between SDMLP monotherapy and CLP monotherapy. The most effective treatment in the network was ranibizumab therapy combined with CLP followed by SDMLP monotherapy, Bevacizumab therapy combined with CLP, and CLP monotherapy in rank order.

Prevalence and Risk Factors of Low Health Literacy: A Community-Based Study in Shanghai, China.

Background: Health literacy is an increasingly important public health concern. However, little is known about the health literacy of general public in China. The aim of this study was to evaluate the prevalence of low health literacy and demographic associations in Shanghai, China. Methods: This study was a community-based cross-sectional health survey utilizing a multi-stage random sampling design. The sample consisted of 1360 individuals aged 15-69 years with the total community-dwelling Chinese as the sample frame. Health literacy was measured by a questionnaire developed on the basis of a national health literacy manual released by the Chinese Ministry of Health. Multiple logistic regression models were used to identify whether common socio-demographic features were associated with health literacy level. Results: The prevalence of low health literacy was 84.49% (95% CI, 82.56% to 86.41%). The prevalence of low health literacy was negatively associated with the level of education, occupation, and annual household income, but was not associated with gender, age, or the presence of non-communicable chronic disease. Conclusions: Simplifying health services, enhancing health education, and promoting interventions to improve health literacy in high-risk populations should be considered as part of the strategies in the making of health policy in China.