Renal-Targeted Drug Delivery by Chitosan Oligosaccharide Micelles with HSA-Enriched Protein Corona for the Treatment of Ischemia/Reperfusion-Induced Acute Kidney Injury.

Renal-specific nanoparticulate drug delivery systems have shown great potential in reducing systemic side effects and improving the safety and efficacy of treatments for renal diseases. Here, stearic acid-grafted chitosan oligosaccharide (COS-SA) was synthesized as a renal-targeted carrier due to the high affinity of the 2-glucosamine moiety on COS to the megalin receptor expressed on renal proximal tubular epithelial cells. Specifically, COS-SA/CLT micelles were prepared by encapsulating celastrol (CLT) with COS-SA, and different proportions of human serum albumin (HSA) were then adsorbed onto its surface to explore the interaction between the protein corona and cationic polymeric micelles. Our results showed that a multilayered protein corona, consisting of an inner "hard" corona and an outer "soft" corona, was formed on the surface of COS-SA/CLT@HSA8, which was beneficial in preventing its recognition and phagocytosis by macrophages. The formation of HSA protein corona on COS-SA/CLT micelles also increased its accumulation in the renal tubules. Furthermore, the electropositivity of COS-SA/CLT micelles affected the conformation of adsorbed proteins to various degrees. During the adsorption process, the protein corona on the surface of COS-SA/CLT@HSA1 was partially denatured. Overall, COS-SA/CLT and COS-SA/CLT@HSA micelles demonstrated sufficient safety with renal targeting potential, providing a viable strategy for the management of ischemia/reperfusion-induced acute kidney injury.

A novel testing method for ultra-low-frequency vibration signal based on passive radio frequency tag sensing.

Ultra-low-frequency vibration is prevalent in many critical research fields. Nevertheless, for ultra-low-frequency vibration signals below 1 Hz, there is currently a lack of a cost-effective and efficient measurement method. A new ultra-low-frequency vibration signal testing method based on the passive radio frequency tag phase is proposed using the Radio Frequency Identification (RFID) sensing method. By employing vibration detection on ultra-low-frequency vibration signals, the effectiveness of the proposed approach across different frequencies is validated while thoroughly considering factors such as measurement range, precision, distance, and occlusion effects. The results indicate that this method can accurately measure ultra-low frequency vibration signals as low as 0.01 Hz, with an average relative error of only less than 1.5% for all measurement results, and the error decreases with increasing detection frequency. For the measurement of a 1 Hz vibration signal, the average relative error is less than 1%. In addition, the measurement accuracy remains unaffected by distance or occlusion. Sensitivity and stability tests are also conducted. Continuous monitoring for 8 hours demonstrates the excellent measurement stability of the proposed method. Finally, a performance comparison has been made with laser displacement sensors commonly used in non-contact ultra-low-frequency measurement methods. The results show that the RFID sensing method can detect lower vibration frequencies and has a larger amplitude measurement range and better environmental adaptability. Overall, for ultra-low-frequency vibration, this method offers advantages such as high precision, passive non-contact operation, non-line-of-sight path monitoring, affordability, and convenience. These attributes render it suitable for extensive application in various engineering scenarios requiring ultra-low-frequency vibration testing.

Unveiling MRI markers for Parkinson's Disease: GABAergic dysfunction and cortical changes.

OBJECTIVE: The study aimed to investigate changes in basal levels of the inhibitory γ-aminobutyric acid (GABA) neurotransmitter in the sensorimotor cortex (SMC) and cortical gyrification in patients with Parkinson's disease (PD), which could further identify potential imaging biomarkers for PD, particularly in patients with early-onset Parkinson's disease (EOPD). METHOD: Fifty patients with PD (EOPD: 10, late-onset Parkinson's disease [LOPD]: 40) and fifty-two age- and gender-matched healthy controls (HC) underwent GABA-edited 1H MRS of the SMC and high-resolution 3D T1-weighted brain imaging. GABA levels and local gyrification index (LGI) were calculated to assess GABAergic and cortical gyrification deficits in PD. RESULT: The Pearson correlation coefficients revealed significant negative associations between eight indicators, including GABA/Cr level and local gyrification index (LGI) of specific cortical regions (precentral, postcentral, entorhinal, superiortemporal, posteriorcingulate, cuneus, and transversetemporal cortex), and the likelihood of Parkinson's disease (r < -0.4, p < 0.001). Additionally, GABA levels were significantly lower in the SMC region of both EOPD and LOPD patients compared to healthy controls (mean ± SD [u.i.]: EOPD=0.081 ± 0.022 vs. Young-HC=0.112 ± 0.021, p = 0.003; LOPD=0.054 ± 0.024 vs. Old-HC=0.099 ± 0.021, p < 0.001). The logistic regression model was established by using multivariate analysis, identifying two statistically significant indicators: GABA/Cr and LGI of the transversetemporal. The combined model exhibited the highest AUC values in both younger and older populations. CONCLUSION: GABAergic dysfunction may play an important role in the pathogenesis of PD patients. Changes in neurotransmitter and morphological may serve as potential markers for the preclinical diagnosis and progression of PD, including EOPD.

Efficacy and prognostic factors of COVID-19 vaccine in patients with hepatocellular carcinoma: Analysis of data from a prospective cohort study.

BACKGROUND: The efficacy of coronavirus disease 2019 (COVID-19) vaccines in preventing SARS-CoV-2 infection in patients with hepatocellular carcinoma (HCC) is not clear. METHODS: From January 2022 to October 2022, patients diagnosed with HCC in a prospective, multicenter, observational cohort were analyzed. RESULTS: One hundred and forty-one patients with (n = 107) or without COVID-19 vaccination (n = 34) were included. The number of patients with severe or very severe infection was relatively lower in the vaccinated group (3.7% vs. 11.8%, p = 0.096). Median infection-free survival in the vaccinated group (14.0 vs. 8.3 months, p = 0.010) was significantly longer than that in the unvaccinated group. COVID-19 vaccination (hazard ratio (HR) HR = 0.47), European Cooperative Oncology Group performance score = 0 (HR = 2.06), and extrahepatic spread (HR = 0.28) were found to be the independent predictive factors for infection-free survival. CONCLUSION: COVID-19 vaccines could effectively reduce the SARS-Cov-2 infection in patients with HCC.

A new long-acting analgesic formulation for postoperative pain management.

Local anesthetics (LA), as part of multimodal analgesia, have garnered significant interest for their role in delaying the initiation of opioid therapy, reducing postoperative opioid usage, and mitigating both hospitalization duration and related expenses. Despite numerous endeavors to extend the duration of local anesthetic effects, achieving truly satisfactory long-acting analgesia remains elusive. Drawing upon prior investigations, vesicular phospholipid gels (VPGs) emerge as promising candidates for extended-release modalities in small-molecule drug delivery systems. Therefore, we tried to use the amphiphilicity of phospholipids to co-encapsulate levobupivacaine hydrochloride and meloxicam, two drugs with different hydrophilicity, to obtain a long-term synergistic analgesic effect. Initially, the physicochemical attributes of the formulation were characterized, followed by an examination of its in vitro release kinetics, substantiating the viability of extending the release duration of the dual drugs. Sequentially, in vivo investigations encompassing pharmacokinetic profiling and assessment of analgesic efficacy were undertaken, revealing a prolonged release duration of up to 120 h and attainment of optimal postoperative analgesia. Subsequently, inquiries into the mechanism underlying synergistic analgesic effects and safety evaluations pertinent to the delivery strategy were pursued. In summation, we successfully developed a promising formulation to achieve long-acting analgesia.

Oncolytic Virus Senecavirus A Inhibits Hepatocellular Carcinoma Proliferation and Growth by Inducing Cell Cycle Arrest and Apoptosis.

Background and Aims: Hepatocellular carcinoma (HCC) is a highly aggressive tumor with limited treatment options and high mortality. Senecavirus A (SVA) has shown potential in selectively targeting tumors while sparing healthy tissues. This study aimed to investigate the effects of SVA on HCC cells in vitro and in vivo and to elucidate its mechanisms of action. Methods: The cell counting kit-8 assay and colony formation assay were conducted to examine cell proliferation. Flow cytometry and nuclear staining were employed to analyze cell cycle distribution and apoptosis occurrence. A subcutaneous tumor xenograft HCC mouse model was created in vivo using HepG2 cells, and Ki67 expression in the tumor tissues was assessed. The terminal deoxynucleotidyl transferase dUTP nick end labeling assay and hematoxylin and eosin staining were employed to evaluate HCC apoptosis and the toxicity of SVA on mouse organs. Results: In vitro, SVA effectively suppressed the growth of tumor cells by inducing apoptosis and cell cycle arrest. However, it did not have a notable effect on normal hepatocytes (MIHA cells). In an in vivo setting, SVA effectively suppressed the growth of HCC in a mouse model. SVA treatment resulted in a significant decrease in Ki67 expression and an increase in apoptosis of tumor cells. No notable histopathological alterations were observed in the organs of mice during SVA administration. Conclusions: SVA inhibits the growth of HCC cells by inducing cell cycle arrest and apoptosis. It does not cause any noticeable toxicity to vital organs.

Comparison of intelligent virtual reality first-aid training outcomes among individuals with different demographic characteristics.

OBJECTIVE: To compare the outcomes of intelligent first-aid training based on virtual reality (VR) among individuals with different demographic characteristics. METHODS: A total of 50 nonmedical professional volunteers from Nanchang were conveniently sampled in March 2021. All participants underwent intelligent VR first-aid training, and a comparative analysis was conducted by dividing them into different groups based on demographic characteristics. RESULTS: Male participants had a lower chest compression interruption time compared to female participants (7.40 ± 0.50 vs. 8.04 ± 0.56, t = -4.231, p < 0.001). Additionally, male participants had a higher proportion of compressions with correct compression depth (81.33 ± 1.24 vs. 79.78 ± 1.48, t = 4.038, p < 0.001), higher mean ventilation volume (518.11 ± 1.50 vs. 516.61 ± 2.17, t = 2.881, p = 0.006), and higher theoretical knowledge test score (8.74 ± 0.59 vs. 8.00 ± 0.43, t = 4.981, p < 0.001). There were statistically significant differences in the mean chest compression frequency (110.38 ± 5.74 vs. 105.00 ± 4.78 vs. 107.80 ± 5.97, F = 5.187, p = 0.009) among participants with different educational backgrounds. Pairwise comparisons showed that technical degree holders had a higher mean chest compression frequency than bachelor's degree holders, whereas no statistically significant difference was observed between master's degree holders and bachelor's degree holders. CONCLUSION: The outcomes of first-aid training differ among participants of different genders and with different educational backgrounds. With all participants meeting the training qualifications, it is believed that the application of intelligent VR first-aid training platforms can improve the first aid capabilities of the public.

Exploring the Processing Paradigm of Input Data for End-to-End Deep Learning in Tool Condition Monitoring.

Tool condition monitoring technology is an indispensable part of intelligent manufacturing. Most current research focuses on complex signal processing techniques or advanced deep learning algorithms to improve prediction performance without fully leveraging the end-to-end advantages of deep learning. The challenge lies in transforming multi-sensor raw data into input data suitable for direct model feeding, all while minimizing data scale and preserving sufficient temporal interpretation of tool wear. However, there is no clear reference standard for this so far. In light of this, this paper innovatively explores the processing methods that transform raw data into input data for deep learning models, a process known as an input paradigm. This paper introduces three new input paradigms: the downsampling paradigm, the periodic paradigm, and the subsequence paradigm. Then an improved hybrid model that combines a convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) was employed to validate the model's performance. The subsequence paradigm demonstrated considerable superiority in prediction results based on the PHM2010 dataset, as the newly generated time series maintained the integrity of the raw data. Further investigation revealed that, with 120 subsequences and the temporal indicator being the maximum value, the model's mean absolute error (MAE) and root mean square error (RMSE) were the lowest after threefold cross-validation, outperforming several classical and contemporary methods. The methods explored in this paper provide references for designing input data for deep learning models, helping to enhance the end-to-end potential of deep learning models, and promoting the industrial deployment and practical application of tool condition monitoring systems.

Adipocyte-targeted celastrol delivery via biguanide-modified micelles improves treatment of obesity in DIO mice.

Obesity has emerged as a significant global health burden, exacerbated by serious side effects associated with existing anti-obesity medications. Celastrol (CLT) holds promise for weight loss but encounters challenges related to poor solubility and systemic toxicity. Here, we present chondroitin sulfate (CS)-derived micelles engineered for adipocyte-specific targeting, aiming to enhance the therapeutic potential of CLT while minimizing its systemic toxicity. To further enhance adipocyte affinity, we introduced a biguanide moiety into a micellar vehicle. CS is sequentially modified with hydrophilic metformin and hydrophobic 4-aminophenylboronic acid pinacol ester (PBE), resulting in the self-assembly of CLT-encapsulated micelles (MET-CS-PBE@CLT). This innovative design imparts amphiphilicity via the PBE moieties while ensuring the outward exposure of hydrophilic metformin moieties, facilitating active interactions with adipocytes. In vitro studies confirmed the enhanced uptake of MET-CS-PBE@CLT micelles by adipocytes, while in vivo studies demonstrated increased distribution within adipose tissues. In a diet-induced obese mouse model, MET-CS-PBE@CLT exhibited remarkable efficacy in weight loss without affecting food intake. This pioneering strategy offers a promising, low-risk, and highly effective solution to address the global obesity epidemic.

Application of high-resolution MRI in evaluating statin efficacy on symptomatic intracranial atherosclerosis.

OBJECTIVES: To investigate the efficacy of statins on symptomatic intracranial atherosclerotic plaques using high-resolution 3.0 T MR vessel wall imaging (HR-MRI). METHODS: Patients with symptomatic intracranial atherosclerotic plaques (cerebral ischemic events within the last three months) confirmed by HR-MRI from July 2017 to August 2022 were retrospectively included in this study. The enrolled patients started statin therapy at baseline. All the patients underwent the follow-up HR-MRI examination after statin therapy for at least 3 months. A paired sample t-test and Wilcoxon rank sum test were used to evaluate the changes in plaque characteristics after statin therapy. Multivariate linear regression was further used to investigate the clinical factors associated with statin efficacy. RESULTS: A total of 48 patients (37 males; overall mean age = 60.2 ± 11.7 years) were included in this study. The follow-up time was 7.0 (5.6-12.0) months. In patients treated with statins for > 6 months (n = 31), plaque length, wall thickness, plaque burden, luminal stenosis and plaque enhancement were significantly reduced. Similar results were found in patients with good lipid control (n = 21). Younger age, lower BMI and hypertension were associated with decreased plaque burden. Lower BMI, hypertension and longer duration of statin therapy were associated with decreased plaque enhancement. Younger age and hypertension were associated with decreased luminal stenosis (all p < 0.05). CONCLUSION: HR-MRI can effectively evaluate plaques changes after statin therapy. Statins can reduce plaque burden and stabilize plaques. The effect of statin may have a relationship with age, BMI, hypertension, and duration of statin therapy. CLINICAL RELEVANCE STATEMENT: High-resolution MRI can be applied to evaluate the efficacy of statins on symptomatic intracranial atherosclerotic plaques. Long-term statin use and well-controlled blood lipid levels can help reduce plaque burden and stabilize plaques. KEY POINTS: High-resolution MRI provides great help evaluating the changes of plaque characteristics after statin therapy. Efficacy of statins is associated with duration of use, controlled lipid levels, and clinical factors. High-resolution MRI can serve as an effective method for following-up symptomatic intracranial atherosclerosis.

The role of Fusobacterium nucleatum in cancer and its implications for clinical applications.

Fusobacterium nucleatum, a gram-negative anaerobic bacterium abundantly found in the human oral cavity, is widely recognized as a key pathobiont responsible for the initiation and progression of periodontal diseases due to its remarkable aggregative capabilities. Numerous clinical studies have linked F. nucleatum with unfavorable prognostic outcomes in various malignancies. In further research, scholars have partially elucidated the mechanisms underlying F. nucleatum's impact on various types of cancer, thus gaining a certain comprehension of the role played by F. nucleatum in cancer. In this comprehensive review, we present an in-depth synthesis of the interplay between F. nucleatum and different cancers, focusing on aspects such as tumor initiation, metastasis, chemoresistance, and modulation of the tumor immune microenvironment and immunotherapy. The implications for cancer diagnosis and treatment are also summarized. The objective of this review is to enhance our comprehension of the intricate relationship between F. nucleatum and oncogenic pathogenesis, while emphasizing potential therapeutic strategies.

Development and validation of a diagnostic model for cerebral small vessel disease among rural older adults in China.

Objectives: Cerebral small vessel disease (CSVD) visible on MRI can be asymptomatic. We sought to develop and validate a model for detecting CSVD in rural older adults. Methods: This study included 1,192 participants in the MRI sub-study within the Multidomain Interventions to Delay Dementia and Disability in Rural China. Total sample was randomly divided into training set and validation set. MRI markers of CSVD were assessed following the international criteria, and total CSVD burden was assessed on a scale from 0 to 4. Logistic regression analyses were used to screen risk factors and develop the diagnostic model. A nomogram was used to visualize the model. Model performance was assessed using the area under the receiver-operating characteristic curve (AUC), calibration plot, and decision curve analysis. Results: The model included age, high blood pressure, white blood cell count, neutrophil-to-lymphocyte ratio (NLR), and history of cerebral infarction. The AUC was 0.71 (95% CI, 0.67-0.76) in the training set and 0.69 (95% CI, 0.63-0.76) in the validation set. The model showed high coherence between predicted and observed probabilities in both the training and validation sets. The model had higher net benefits than the strategy assuming all participants either at high risk or low risk of CSVD for probability thresholds ranging 50-90% in the training set, and 65-98% in the validation set. Conclusion: A model that integrates routine clinical factors could detect CSVD in older adults, with good discrimination and calibration. The model has implication for clinical decision-making.

Accelerating multipool CEST MRI of Parkinson's disease using deep learning-based Z-spectral compressed sensing.

PURPOSE: To develop a deep learning-based approach to reduce the scan time of multipool CEST MRI for Parkinson's disease (PD) while maintaining sufficient prediction accuracy. METHOD: A deep learning approach based on a modified one-dimensional U-Net, termed Z-spectral compressed sensing (CS), was proposed to recover dense Z-spectra from sparse ones. The neural network was trained using simulated Z-spectra generated by the Bloch equation with various parameter settings. Its feasibility and effectiveness were validated through numerical simulations and in vivo rat brain experiments, compared with commonly used linear, pchip, and Lorentzian interpolation methods. The proposed method was applied to detect metabolism-related changes in the 6-hydroxydopamine PD model with multipool CEST MRI, including APT, CEST@2 ppm, nuclear Overhauser enhancement, direct saturation, and magnetization transfer, and the prediction performance was evaluated by area under the curve. RESULTS: The numerical simulations and in vivo rat-brain experiments demonstrated that the proposed method could yield superior fidelity in retrieving dense Z-spectra compared with existing methods. Significant differences were observed in APT, CEST@2 ppm, nuclear Overhauser enhancement, and direct saturation between the striatum regions of wild-type and PD models, whereas magnetization transfer exhibited no significant difference. Receiver operating characteristic analysis demonstrated that multipool CEST achieved better predictive performance compared with individual pools. Combined with Z-spectral CS, the scan time of multipool CEST MRI can be reduced to 33% without distinctly compromising prediction accuracy. CONCLUSION: The integration of Z-spectral CS with multipool CEST MRI can enhance the prediction accuracy of PD and maintain the scan time within a reasonable range.

The Advances in Phospholipids-Based Phase Separation Gels for the Sustained Release of Peptides, Proteins, and Chemotherapeutics.

Implantable drug delivery systems formed upon injection offer a host of advantages, including localized drug administration, sustained release, minimized side effects, and enhanced patient compliance. Among the various techniques utilized for the development of in situ forming drug implants, solvent-induced phase inversion emerges as a particularly promising approach. However, synthetic polymer-based implants have been associated with undesirable effects arising from polymer degradation. In response to this challenge, a novel category of drug delivery systems, known as phospholipids-based phase separation gels (PPSGs), has emerged. These gels, characterized by their low initial viscosity, exhibit injectability and undergo rapid transformation into in situ implants when exposed to an aqueous environment. A typical PPSG formulation comprises biodegradable components, such as phospholipids, pharmaceutical oil, and a minimal amount of ethanol. The minimized organic solvents in the composition show good biocompatibility. And the relatively simple composition holds promise for industrial-scale manufacturing. This comprehensive review provides an overview of the principles and advancements in PPSG systems, with specific emphasis on their suitability as drug delivery systems for a wide range of active pharmaceutical ingredients (APIs), spanning from small molecules to peptides and proteins. Additionally, we explore the critical parameters and underlying principles governing the formulation of PPSG-based drug delivery strategies, offering valuable insights on optimization strategies.

ABT­737 increases cisplatin sensitivity through the ROS­ASK1­JNK MAPK signaling axis in human ovarian cancer cisplatin­resistant A2780/DDP cells.

Ovarian cancer is a gynecological malignant tumor with the highest mortality rate, and chemotherapy resistance seriously affects patient therapeutic outcomes. It has been shown that the high expression of anti­apoptotic proteins Bcl­2 and Bcl­xL is closely related to ovarian cancer chemotherapy resistance. Therefore, reducing Bcl­2 and Bcl­xL expression levels may be essential for reversing drug resistance in ovarian cancer. ABT­737 is a BH3­only protein mimetic, which can effectively inhibit the expression of the anti­apoptotic proteins Bcl­xL and Bcl­2. Although it has been shown that ABT­737 can increase the sensitivity of ovarian cancer cells to cisplatin, the specific molecular mechanism remains unclear and requires further investigation. In the present study, the results revealed that ABT­737 can significantly increase the activation levels of JNK and ASK1 induced by cisplatin in A2780/DDP cells, which are cisplatin­resistant ovarian cancer cells. Inhibition of the JNK and ASK1 pathway could significantly reduce cisplatin cytotoxicity increased by ABT­737 in A2780/DDP cells, while inhibiting the ASK1 pathway could reduce JNK activation. In addition, it was further determined that ABT­737 could increase reactive oxygen species (ROS) levels in A2780/DDP cells induced by cisplatin. Furthermore, the inhibition of ROS could significantly reduce JNK and ASK1 activation and ABT­737­mediated increased cisplatin cytotoxicity in A2780/DDP cells. Overall, the current data identified that activation of the ROS­ASK1­JNK signaling axis plays an essential role in the ability of ABT­737 to increase cisplatin sensitivity in A2780/DDP cells. Therefore, upregulation the ROS­ASK1­JNK signaling axis is a potentially novel molecular mechanism by which ABT­737 can enhance cisplatin sensitivity of ovarian cancer cells. In addition, the present research can also provide new therapeutic strategies and new therapeutic targets for patients with cisplatin­resistant ovarian cancer with high Bcl­2/Bcl­xL expression patterns.

Dexamethasone Palmitate Encapsulated in Palmitic Acid Modified Human Serum Albumin Nanoparticles for the Treatment of Rheumatoid Arthritis.

Rheumatoid arthritis (RA) is a chronic inflammatory joint condition characterized by symmetric, erosive synovitis leading to cartilage erosion and significant disability. Macrophages, pivotal in disease progression, release pro-inflammatory factors upon activation. We developed a nanoparticle delivery system (DXP-PSA NPs), based on palmitic acid modified human serum albumin (PSA), to deliver dexamethasone palmitate (DXP) directly to sites of inflammation, enhancing treatment effectiveness and minimizing possible side effects. The system actively targets scavenger receptor-A on activated macrophages, achieving selective accumulation at inflamed joints. In vitro effect and preliminary targeting abilities were investigated on LPS-activated RAW264.7 cells. The in vivo efficacy and safety were evaluated and compared side to side with commercially available lipid emulsion Limethason® in an advanced adjuvant-induced arthritis rat model. DXP-PSA NPs offer a novel approach to RA treatment and presents promising prospects for clinical translation.

The efficacy and safety of Dachaihu decoction in the treatment of nonalcoholic fatty liver disease: a systematic review and meta-analysis.

Background: Nonalcoholic fatty liver disease (NAFLD), also known as metabolic associated fatty liver disease (MAFLD), is a common liver condition characterized by excessive fat accumulation in the liver which is not caused by alcohol. The main causes of NAFLD are obesity and insulin resistance. Dachaihu decoction (DCHD), a classic formula in traditional Chinese medicine, has been proved to treat NAFLD by targeting different aspects of pathogenesis and is being progressively used in the treatment of NAFLD. DCHD is commonly applied in a modified form to treat the NAFLD. In light of this, it is imperative to conduct a systematic review and meta-analysis to assess the effectiveness and safety of DCHD in the management of NAFLD. There is a need for a systematic review and meta-analysis to assess the effectiveness and safety of modified DCHD in treating NAFLD. Objective: The objective of this meta-analysis was to systematically assess the clinical effectiveness and safety of DCHD in treating NAFLD. Methods: This meta-analysis adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Including seven databases, both Chinese and English databases were searched for relevant studies. The quality of included studies was carefully assessed using the bias risk assessment tool in the Cochrane Handbook. Eligible articles were the source of extracted data which was meta-analyzed by using Review Manager 5.4 and Stata 17.0. Results: A total of 10 studies containing 825 patients were included. Compared with conventional treatments, combined treatment could clearly improve the liver function of NAFLD patients, which could reduce the levels of ALT (MD = -7.69 U/L, 95% CI: -11.88 to -3.51, p < 0.001), AST (MD = -9.58 U/L, 95% CI: -12.84 to -6.33, p < 0.01), and it also had a certain impact on regulating lipid metabolism, which could reduce the levels of TC (MD = -0.85 mmol/L, 95% CI: -1.22 to 0.48, p < 0.01), TG (MD = -0.45 mmol/L, 95% CI: -0.64 to 0.21, p < 0.01). Adverse event showed that DCHD was relatively safe. Due to the inclusion of less than 10 trials in each group, it was not possible to conduct a thorough analysis of publication bias. Conclusion: According to the meta-analysis, in the treatment of the NAFLD, it is clear that the combination of DCHD was advantages over conventional treatment alone in improving liver function, regulating lipid metabolism. Additionally, DCHD demonstrates a relatively safe profile. Nevertheless, due to limitations in the quality and quantity of the studies incorporated, the effectiveness and safety of DCHD remain inconclusive. Consequently, further high-quality research is imperative to furnish more substantial evidence supporting the widespread clinical application of DCHD. Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023397353, CRD42023397353.

Glomerulus-Targeted ROS-Responsive Polymeric Nanoparticles for Effective Membranous Nephropathy Therapy.

Membranous nephropathy (MN) is a common immune-mediated glomerular disease that requires the development of safe and highly effective therapies. Celastrol (CLT) has shown promise as a therapeutic molecule candidate, but its clinical use is currently limited due to off-target toxicity. Given that excess levels of reactive oxygen species (ROS) contributing to podocyte damage is a key driver of MN progression to end-stage renal disease, we rationally designed ROS-responsive cationic polymeric nanoparticles (PPS-CPNs) with a well-defined particle size and surface charge by employing poly(propylene sulfide)-polyethylene glycol (PPS-PEG) and poly(propylene sulfide)-polyethylenimine (PPS-PEI) to selectively deliver CLT to the damaged glomerulus for MN therapy. Experimental results show that PPS-CPNs successfully crossed the fenestrated endothelium, accumulated in the glomerular basement membrane (GBM), and were internalized by podocytes where rapid drug release was triggered by the overproduction of ROS, thereby outperforming nonresponsive CLT nanotherapy to alleviate subepithelial immune deposits, podocyte foot process effacement, and GBM expansion in a rat MN model. Moreover, the ROS-responsive CLT nanotherapy was associated with significantly lower toxicity to major organs than free CLT. These results suggest that encapsulating CLT into PPS-CPNs can improve efficacy and reduce toxicity as a promising treatment option for MN.

Effectiveness of azvudine against severe outcomes among hospitalized COVID-19 patients in Xinjiang, China: a single-center, retrospective, matched cohort study.

BACKGROUND: Since the end of 2022, Azvudine was widely used to treat hospitalized coronavirus disease 2019 (COVID-19) patients in China. However, data on the real-world effectiveness of Azvudine against severe outcomes and post-COVID-19-conditions (PCC) among patients infected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variants was limited. This study evaluates the effectiveness of Azvudine in hospitalized COVID-19 patients during a SARS-CoV-2 Omicron BA.5 dominance period. METHODS: From 1 November 2022 to 1 July 2023, an SARS-CoV-2 Omicron BA.5 dominant period, we conducted a single-center retrospective cohort study based on hospitalized patients with laboratory-confirmed SARS-CoV-2 infection from a tertiary hospital in Shihezi, China. Patients treated with Azvudine and usual care were propensity-score matched (PSM) at a 1:1 ratio to a control group in which patients received usual care only, with matching based on covariates such as sex, age, ethnicity, number of preexisting conditions, antibiotic use at admission, and baseline complete blood cell count. The primary outcomes were all-cause death and short-term (60 days) PCC post discharge. The secondary outcomes included the initiation of invasive mechanical ventilation and PCC at long-term post discharge (120 days). Cox proportional hazards (PH) regression models were employed to estimate the hazard ratios (HR) of Azvudine treatment for both all-cause death and invasive mechanical ventilation, and logistic regression models were used to estimate the odds ratios (OR) for short-term and long-term PCC. Subgroup analyses were performed based on a part of the matched covariates. RESULTS: A total of 2,639 hospitalized patients with SARS-CoV-2 infection were initially identified, and 2,069 ineligible subjects were excluded from analyses. After matching, 297 Azvudine recipients and 297 matched controls were eligible for analyses. The incidence rate of all-cause death was relatively lower in the Azvudine group than in control group (0.007 per person, 95% confidence interval [CI]: 0.001, 0.024 vs 0.128, 95% CI: 0.092, 0.171), and the use of Azvudine was associated with a significantly lower risk of death (HR: 0.049, 95% CI: 0.012, 0.205). Subgroup analyses suggested protection of Azvudine against the risks of all-cause death among men, age over 65, patients without the preexisting conditions, and patients with antibiotics dispensed at admission. Statistical differences were not observed between the Azvudine group and the control group for the risks of invasive mechanical ventilation or short and long-term PCC. CONCLUSIONS: Our findings indicated that Azvudine was associated with lower risk of all-cause death among hospitalized patients with Omicron BA.5 infection in a real-world setting. Further investigation is needed to explore the effectiveness of Azvudine against the PCC after discharge.

This study aims to evaluate the real-world effectiveness of Azvudine among hospitalized COVID-19 patients during a SARS-CoV-2 Omicron BA.5 dominant epidemic phase. Cox proportional hazards (PH) regression models were employed to estimate the hazard ratios (HR) for all-cause death. We found that the use of Azvudine was associated with a significantly reduced risk of all-cause death among hospitalized patients with SARS-CoV-2 infection.

Innovative road distress detection (IR-DD): an efficient and scalable deep learning approach.

In the rapidly evolving landscape of transportation infrastructure, the quality and condition of road networks play a pivotal role in societal progress and economic growth. In the realm of road distress detection, traditional methods have long grappled with manual intervention and high costs, requiring trained observers for time-consuming and expensive data collection processes. The limitations of these approaches are compounded by challenges in adapting to diverse road surfaces and handling low-resolution data, particularly in early automated distress survey technologies. This article addresses the critical need for efficient road distress detection, a key component of ensuring safe and reliable transportation systems. Effectively addressing these challenges is crucial for enhancing the efficiency, accuracy, and safety of road distress detection systems. Leveraging advancements in object detection, we introduce the Innovative Road Distress Detection (IR-DD), a novel framework that integrates the YOLOv8 algorithm to enhance the accuracy and real-time capabilities of road distress detection, catering to applications such as smart cities and autonomous vehicles. Our approach incorporates bidirectional feature pyramid network (BiFPN) recursive feature fusion and bidirectional connections to optimize the utilization of multi-scale features, addressing challenges related to information loss and gradients encountered in traditional methods. Comprehensive experimental analysis demonstrates the superior performance, efficiency, and robustness of our integrated approach, positioning it as a cost-effective and compelling alternative to conventional road distress detection methods. Our findings demonstrate the superior performance of our approach compared to other state-of-the-art methods across various evaluation metrics, including precision, recall, F1 score, and mean average precision (mAP) at different intersection over union (IoU) thresholds. Specifically, our method achieves notable results with a precision of 0.666, F1 score of 0.630, mAP@0.5 of 0.650, all while operating at a speed of 86 frames per second (FPS). These outcomes underscore the effectiveness of our approach in real-time road distress detection. This article contributes to the ongoing innovation in object detection techniques, emphasizing the practicality and effectiveness of our proposed solution in advancing the field of road distress detection.