Colon-targeted hydroxyethyl starch-curcumin microspheres with high loading capacity ameliorate ulcerative colitis via alleviating oxidative stress, regulating inflammation, and modulating gut microbiota.

Curcumin (CUR) is a natural polyphenol that holds promise for treating ulcerative colitis (UC), yet oral administration of CUR exhibits limited bioavailability and existing formulations for oral delivery of CUR often suffer from unsatisfactory loading capacity. This study presents hydroxyethyl starch-curcumin microspheres (HC-MSs) with excellent CUR loading capacity (54.52 %), and the HC-MSs can further encapsulate anti-inflammatory drugs dexamethasone (DEX) to obtain a combination formulation (DHC-MSs) with high DEX loading capacity (19.91 %), for combination therapy of UC. The microspheres were successfully engineered, retaining the anti-oxidative and anti-inflammatory activities of parental CUR and demonstrating excellent biocompatibility and controlled release properties, notably triggered by α-amylase, facilitating targeted drug delivery to inflamed sites. In a mouse UC model induced by dextran sulfate sodium, the microspheres effectively accumulated in inflamed colons and both HC-MSs and DHC-MSs exhibited superior therapeutic efficacy in alleviating UC symptoms compared to free DEX. Moreover, mechanistic exploration uncovered the multifaceted therapeutic mechanisms of these formulations, encompassing anti-inflammatory actions, mitigation of spleen enlargement, and modulation of gut microbiota composition. These findings underscore the potential of HC-MSs and DHC-MSs as promising formulations for UC, with implications for advancing treatment modalities for various inflammatory bowel disorders.

Photoelectrochemical Sensor for H2S Based on a Lead-Free Perovskite/Metal-Organic Framework Composite.

Halide perovskites have emerged as a highly promising class of photoelectric materials. However, the application of lead-based perovskites has been hindered by their toxicity and relatively weak stability. In this work, a composite material comprising a lead-free perovskite cesium copper iodide (CsCu2I3) nanocrystal and a metal-organic framework (MOF-801) has been synthesized through an in situ growth approach. The resulting composite material, denoted as CsCu2I3/MOF-801, demonstrates outstanding stability and exceptional optoelectronic characteristics. MOF-801 may serve a dual role by acting as a protective barrier between CsCu2I3 nanocrystals and the external environment, as well as promoting the efficient transfer of photogenerated charge carriers, thereby mitigating their recombination. Consequently, CsCu2I3/MOF-801 demonstrates its utility by providing both stability and a notably high initial photocurrent. Leveraging the inherent reactivity between H2S and the composite material, which results in the formation of Cu2S and structural alteration, an exceptionally sensitive photoelectrochemical sensor for H2S detection has been designed. This sensor exhibits a linear detection range spanning from 0.005 to 100 µM with a remarkable detection limit of 1.67 nM, rendering it highly suitable for precise quantification of H2S in rat brains. This eco-friendly sensor significantly broadens the application horizon of perovskite materials and lays a robust foundation for their future commercialization.

Novel antimicrobial peptides against Cutibacterium acnes designed by deep learning.

The increasing prevalence of antibiotic resistance in Cutibacterium acnes (C. acnes) requires the search for alternative therapeutic strategies. Antimicrobial peptides (AMPs) offer a promising avenue for the development of new treatments targeting C. acnes. In this study, to design peptides with the specific inhibitory activity against C. acnes, we employed a deep learning pipeline with generators and classifiers, using transfer learning and pretrained protein embeddings, trained on publicly available data. To enhance the training data specific to C. acnes inhibition, we constructed a phylogenetic tree. A panel of 42 novel generated linear peptides was then synthesized and experimentally evaluated for their antimicrobial selectivity and activity. Five of them demonstrated their high potency and selectivity against C. acnes with MIC of 2-4 µg/mL. Our findings highlight the potential of these designed peptides as promising candidates for anti-acne therapeutics and demonstrate the power of computational approaches for the rational design of targeted antimicrobial peptides.

Injectable Hydrogels with Integrated Ph Probes and Ultrasound-Responsive Microcapsules as Smart Wound Dressings for Visual Monitoring and On-Demand Treatment of Chronic Wounds.

Hydrogel dressings capable of infection monitoring and precise treatment administration show promise for advanced wound care. Existing methods involve embedd ingorganic dyes or flexible electronics into preformed hydrogels, which raise safety issues and adaptability challenges. In this study, an injectable hydrogel based smart wound dressing is developed by integrating food-derived anthocyanidin as a visual pH probe for infection monitoring and poly(L-lactic acid) microcapsules as ultrasound-responsive delivery systems for antibiotics into a poly(ethylene glycol) hydrogel. This straightforwardly prepared hydrogel dressing maintains its favorable properties for wound repair, including porous morphology and excellent biocompatibility. In vitro experiments demonstrated that the hydrogel enabled visual assessment of pH within the range of 5 âˆ¼ 9.Meanwhile, the release of antibiotics could be triggered and controlled by ultrasound. In vivo evaluations using infected wounds and diabetic wounds revealed that the wound dressing effectively detected wound infection by monitoring pH levels and achieved antibacterial effects through ultrasound-triggered drug release. This led to significantly enhanced wound healing, as validated by histological analysis and the measurement of inflammatory cytokine levels. This injectable hydrogel-based smart wound dressing holds great potential for use in clinical settings to inform timely and precise clinical intervention and in community to improve wound care management.

MMBERT: a unified framework for biomedical named entity recognition.

Named entity recognition (NER) is an important task in natural language processing (NLP). In recent years, NER has attracted much attention in the biomedical field. However, due to the lack of biomedical named entity identification datasets, the complexity and rarity of biomedical named entities and so on, biomedical NER is more difficult than general domain NER. So in this paper, we propose a framework (MMBERT) based on Transformer to solve the problems above. To address the scarcity of biomedical named entity recognition datasets, we introduce ERNIE-Health, a new Chinese language representation model pre-trained on large-scale biomedical text corpora. Because of the complexity and rarity of biomedical named entities, we use the Bert and CW-LSTM structures to get the joint feature vector of word pairs relations. In addition, we design multi-granularity 2D convolution to refine the relationship and representation between word pairs. Finally, we design a convolutional neural network (CNN) structure and a co-predictor to improve the model's generalization capability and prediction accuracy. We have conducted extensive experiments on three benchmark datasets, and the experimental results show that our model achieves the best results compared with several baseline models in the experiment.

Colon-Targeted Oral Delivery of Hydroxyethyl Starch-Curcumin Microcapsules Loaded with Multiple Drugs Alleviates DSS-Induced Ulcerative Colitis in Mice.

Combination therapy through colon-targeted oral delivery of multiple drugs presents a promising approach for effectively treating ulcerative colitis (UC). However, the codelivery of drugs with diverse physicochemical properties in a single formulation remains a formidable challenge. Here, microcapsules are designed based on hydroxyethyl starch-curcumin (HES─CUR) conjugates to enable the simultaneous delivery of hydrophobic dexamethasone acetate (DA) and hydrophilic cefazolin sodium (CS), yielding multiple drug-loaded microcapsules (CS/DA-loaded HES─CUR microcapsules, CDHC-MCs) tailored for colon-targeted therapy of UC. Thorough characterization confirms the successful synthesis and exceptional biocompatibility of CDHC-MCs. Biodistribution studies demonstrate that the microcapsules exhibit an impressive inflammatory targeting effect, accumulating preferentially in inflamed colons. In vivo experiments employing a dextran-sulfate-sodium-induced UC mouse model reveal that CDHC-MCs not only arrest UC progression but also facilitate the restoration of colon length and alleviate inflammation-related splenomegaly. These findings highlight the potential of colon-targeted delivery of multiple drugs within a single formulation as a promising strategy to enhance UC treatment, and the CDHC-MCs developed in this study hold great potential in developing novel oral formulations for advanced UC therapy.

Unimolecular Cascaded Multienzyme Conjugates Modulate the Microenvironment of Diabetic Wound to Promote Healing.

An abnormal microenvironment underlies poor healing in chronic diabetic chronic wounds. However, effectively modulating the microenvironment of the diabetic wound remains a great challenge due to sustained oxidative stress and chronic inflammation. Here, we present a unimolecular enzyme-polymer conjugate that demonstrates excellent multienzymatic cascade activities. The cascaded enzyme conjugates (CECs) were synthesized by grafting poly(N-acryloyl-lysine) (pLAAm) from the glycan moieties of glucose oxidase (GOx) via glycan-initiated polymerization. The resulting CECs exhibited multiple enzymatic properties of GOx, superoxide dismutase mimic, and catalase mimic activities simultaneously. The CECs facilitated the depletion of high blood glucose, ROS scavenging, bacteria-killing, anti-inflammatory effects, and sustained oxygen generation, which restored the microenvironment in diabetic wounds. In vivo results from a diabetic mouse model confirmed the capacity and efficiency of the cascade reaction for diabetic wound healing. Our findings demonstrate that the three-in-one enzyme-polymer conjugates alone can modulate the diabetic microenvironment for wound healing.

DDI-GCN: Drug-drug interaction prediction via explainable graph convolutional networks.

Drug-drug interactions (DDI) may lead to unexpected side effects, which is a growing concern in both academia and industry. Many DDIs have been reported, but the underlying mechanisms are not well understood. Predicting and understanding DDIs can help researchers to improve drug safety and protect patient health. Here, we introduce DDI-GCN, a method that utilizes graph convolutional networks (GCN) to predict DDIs based on chemical structures. We demonstrate that this method achieves state-of-the-art prediction performance on the independent hold-out set. It can also provide visualization of structural features associated with DDIs, which can help us to study the underlying mechanisms. To make it easy and accessible to use, we developed a web server for DDI-GCN, which is freely available at http://wengzq-lab.cn/ddi/.

Recent Advances in Polymer-Based Biosensors for Food Safety Detection.

The excessive use of pesticides and drugs, coupled with environmental pollution, has resulted in the persistence of contaminants on food. These pollutants tend to accumulate in humans through the food chain, posing a significant threat to human health. Therefore, it is crucial to develop rapid, low-cost, portable, and on-site biosensors for detecting food contaminants. Among various biosensors, polymer-based biosensors have emerged as promising probes for detection of food contaminants in recent years, due to their various functions such as target binding, enrichment, and simple signal reading. This paper aims to discuss the characteristics of five types of food pollutants-heavy metals, pesticide residues, pathogenic bacteria, allergens, and antibiotics-and their adverse effects on human health. Additionally, this paper focuses on the principle of polymer-based biosensors and their latest applications in detecting these five types of food contaminants in actual food samples. Furthermore, this review briefly examines the future prospects and challenges of biosensors for food safety detection. The insights provided in this review will facilitate the development of biosensors for food safety detection.

Oxygen-induced dual-signal point-of-care testing aptasensor for aflatoxin B1 detection using platinum nanoparticle catalysis in visual fluorometry and gravimetry.

Point-of-care testing (POCT) has experienced rapid development owing to its advantages of rapid testing, low cost and strong operability, making it indispensable for analyte detection in outdoor or rural areas. In this study, we propose a novel method for the detection of aflatoxin B1 (AFB1) using a dual-signal readout approach within a unified system. This method employs dual channel modes, namely visual fluorescence and weight measurements, as the signal readouts. Specifically, a pressure-sensitive material is utilized as a visual fluorescent agent, its signal can be quenched in the presence of high oxygen pressure. Additionally, an electronic balance, commonly used for weight measurement, is adopted as another signal device, where the signal is generated through the catalytic decomposition of H2O2 by platinum nanoparticles. The experimental results demonstrate that the proposed device enables accurate AFB1 detection within the concentration range of 1.5-32 µg mL-1, with a detection limit of 0.47 µg mL-1. Moreover, this method has been successfully applied for practical AFB1 detection with satisfactory results. Notably, this study pioneers the use of a pressure-sensitive material as a visual signal in POCT. By addressing the limitations of single-signal readout approaches, our method fulfills requirements of intuitiveness, sensitivity, quantitative analysis and reusability.

[Correlation between blood pressure indexes and prognosis in sepsis patients: a cohort study based on MIMIC-III database].

OBJECTIVE: To investigate the correlation between early-stage blood pressure indexes and prognosis in sepsis patients. METHODS: A retrospective cohort study was conducted on the medical records of patients diagnosed with sepsis from 2001 to 2012 in the Medical Information Mart for Intensive Care-III (MIMIC-III) database. Patients were divided into survival group and death group according to the 28-day prognosis. General data of patients and heart rate (HR) and blood pressure at admission to ICU and within 24 hours after admission were collected. The blood pressure indexes including the maximum, median and mean value of systolic index, diastolic index and mean arterial pressure (MAP) index were calculated. The data were randomly divided into training set and validation set (4 : 1). Univariate Logistic regression analysis was used to screen covariates, and multivariate Logistic stepwise regression models were further developed. Model 1 (including HR, blood pressure, and blood pressure index related variables with P < 0.1 and other variables with P < 0.05) and Model 2 (including HR, blood pressure, and blood pressure index related variables with P < 0.1) were developed respectively. The receiver operator characteristic curve (ROC curve), precision recall curve (PRC) and decision curve analysis (DCA) curve were used to evaluate the quality of the two models, and the influencing factors of the prognosis of sepsis patients were analyzed. Finally, nomogram model was developed according to the better model and effectiveness of it was evaluated. RESULTS: A total of 11 559 sepsis patients were included in the study, with 10 012 patients in the survival group and 1 547 patients in the death group. There were significant differences in age, survival time, Elixhauser comorbidity score and other 46 variables between the two groups (all P < 0.05). Thirty-seven variables were preliminarily screened by univariate Logistic regression analysis. After multivariate Logistic stepwise regression model screening, among the indicators related to HR, blood pressure and blood pressure index, the HR at admission to ICU [odds ratio (OR) = 0.992, 95% confidence interval (95%CI) was 0.988-0.997] and the maximum HR (OR = 1.006, 95%CI was 1.001-1.011), maximum MAP index (OR = 1.620, 95%CI was 1.244-2.126), mean diastolic index (OR = 0.283, 95%CI was 0.091-0.856), median systolic index (OR = 2.149, 95%CI was 0.805-4.461), median diastolic index (OR = 3.986, 95%CI was 1.376-11.758) were selected (all P < 0.1). There were 14 other variables with P < 0.05, including age, Elixhauser comorbidity score, continuous renal replacement therapy (CRRT), use of ventilator, sedation and analgesia, norepinephrine, norepinephrine, highest serum creatinine (SCr), maximum blood urea nitrogen (BUN), highest prothrombin time (PT), highest activated partial thromboplastin time (APTT), lowest platelet count (PLT), highest white blood cell count (WBC), minimum hemoglobin (Hb). The ROC curve showed that the area under the curve (AUC) of Model 1 and Model 2 were 0.769 and 0.637, respectively, indicating that model 1 had higher prediction accuracy. The PRC curve showed that the AUC of Model 1 and Model 2 were 0.381 and 0.240, respectively, indicating that Model 1 had a better effect. The DCA curve showed that when the threshold was 0-0.8 (the probability of death was 0-80%), the net benefit rate of Model 1 was higher than that of Model 2. The calibration curve showed that the prediction effect of the nomogram model developed according to Model 1 was in good agreement with the actual outcome. The Bootstrap verification results showed that the nomogram model was consistent with the above results and had good prediction effects. CONCLUSIONS: The nomogram model constructed has good prediction effects on the 28-day prognosis in sepsis patients, and the blood pressure indexes are important predictors in the model.

Deep Learning-Based System for Disease Screening and Pathologic Region Detection From Optical Coherence Tomography Images.

Purpose: This study was designed to apply deep learning models in retinal disease screening and lesion detection based on optical coherence tomography (OCT) images. Methods: We collected 37,138 OCT images from 775 patients and labelled by ophthalmologists. Multiple deep learning models including ResNet50 and YOLOv3 were developed to identify the types and locations of diseases or lesions based on the images. Results: The model were evaluated using patient-based independent holdout set. For binary classification of OCT images with or without lesions, the performance accuracy was 98.5%, sensitivity was 98.7%, specificity was 98.4%, and the F1 score was 97.7%. For multiclass multilabel disease classification, the models was able to detect vitreomacular traction syndrome and age-related macular degeneration both with an accuracy of more than 99%, sensitivity of more than 98%, specificity of more than 98%, and an F1 score of more than 97%. For lesion location detection, the recalls for different lesion types ranged from 87.0% (epiretinal membrane) to 98.2% (macular pucker). Conclusions: Deep learning-based models have potentials to aid retinal disease screening, classification and diagnosis with excellent performance, which may serve as useful references for ophthalmologists. Translational Relevance: The deep learning-based models are capable of identifying and predicting different eye diseases and lesions from OCT images and may have potential clinical application to assist the ophthalmologists for fast and accuracy retinal disease screening.

LINC00664/miR-411-5p/KLF9 feedback loop contributes to the human oral squamous cell carcinoma progression.

OBJECTIVES: Oral squamous cell carcinoma (OSCC) is one of the most aggressive head and neck cancers with high incidence. Multiple studies have revealed that long non-coding RNAs (lncRNAs) play pivotal roles in tumorigenesis. However, the role of long intergenic non-protein coding RNA 664 (LINC00664) on the progression of OSCC was still unclear. SUBJECTS AND METHODS: In this study, the expression of LINC00664 in OSCC tissues and cell lines was detected by quantitative real-time polymerase chain reaction (qRT-PCR). The functional role of LINC0664 was estimated by cell counting kit-8 (CCK-8), transwell assays, Western blot in vitro, and xenograft tumor model in vivo. The regulatory mechanism was investigated by RNA-binding protein immunoprecipitation (RIP), chromatin immunoprecipitation (ChIP), and luciferase reporter assays. RESULTS: LINC00664 was found to be upregulated in OSCC tissues and cell lines and was associated with poor prognosis of OSCC patients. LINC00664 knockdown suppressed OSCC cell proliferation, migration, invasion, and epithelial-mesenchymal transition (EMT). Moreover, Kruppel like factor 9 (KLF9) enhanced LINC00664 expression at transcription level. Interestingly, LINC00664 upregulated KLF9 expression by sponging miR-411-5p. In addition, knockdown of LINC00664 restrained tumor growth of OSCC in vivo. CONCLUSION: Our study identified the oncogenic roles of LINC00664 in OSCC tumorigenesis and EMT via KLF9/LINC00664/miR-411-5p/KLF9 feedback loop, which provides new perspectives of the potential therapeutic target for OSCC.

Cohesion Design-Led Tough Sealants with Controllably Dissolvable Properties.

Leakage is a common complication of surgeries and injuries, causing pain and increasing the economic burden on patients. Although there are commercially available sealants for leakage prevention, few of them are entirely satisfactory due to disease transmission, high cost, and poor biocompatibility. In addition, none of them can be controllably removed for further healthcare. In this paper, by using cohesion design, a sealant based on amino-modified gelatin (AG) and bi-polyethylene glycol N-hydroxysuccinimide active ester (Bi-PEG-SS) was fabricated. To increase the bursting pressure, the cohesion strength was enhanced by increasing the cross-linking density of the sealant. To endow the sealant with controllably dissolvable properties, the smart succinic ester units were introduced into the cohesion network. Both the in vitro and in vivo experiments showed that this sealant processed high bursting pressure with efficient hemorrhage control. Moreover, no side effects were observed after 7 days of in vivo sealing, including little inflammation and fibrogenesis. These results, together with the easy availability of the raw materials, revealed that this sealant might be a promising alternative for leakage sealing.

Morphological characteristics of zebrafish's yolk sac for malformation based on orthogonal-polarization-gating optical coherence tomography.

In this study, an automatic algorithm combining an ellipsoid approximation and U-net has been presented for the characterization of a zebrafish's yolk sac. The polarization-difference-balanced-detection image of zebrafish was obtained based on orthogonal-polarization-gating optical coherence tomography and used to segment the yolk sac region. And ellipsoid can approximate the shape of the three-dimensional yolk sac, and the multiple parameters of volume and the three principal axes (k, l and m) can be used to quantify the yolk sac. In addition, the multiple parameters of two principal axes (l and m) and volume can distinguish the malformation from the normal controlled group. Finally, the volume malformation of the yolk sac calculated by the proposed algorithm ranges from 16.55% to 46.05%. Thus, the degree of malformation can be applied for toxicity analysis. And this method provides a potential application for an accurate judgment index for biotoxicological testing.

Colon-targeted oral nanoparticles based on ROS-scavenging hydroxyethyl starch-curcumin conjugates for efficient inflammatory bowel disease therapy.

Co-delivery of anti-inflammatory drugs and reactive oxygen species (ROS) scavengers by stimuli-responsive oral nanoparticles is deemed to be a favorable strategy for inflammatory bowel disease (IBD) therapy. In this study, using micelles formed by CUR conjugated hydroxyethyl starch (HES) as vehicles, dexamethasone (DEX)-loaded HES-CUR nanoparticles (DHC NPs) with desirable size, negative surface charge, good stability in the harsh gastric environment, and excellent ROS scavenging activity are developed as a colon-targeted oral formulation for treating IBD. Due to the degradation of HES in response to α-amylase overexpressed in the inflamed colon, the DHC NPs release drugs in an α-amylase-responsive manner. Meanwhile, the DHC NPs can be effectively internalized by macrophages and show excellent cytocompatibility with macrophages since they are composed of food-derived compounds. Importantly, in vivo studies reveal that the DHC NPs are capable of targeting the inflamed colon induced by dextran sulfate sodium (DSS), and the targeted and combination therapy enhances the efficacy of free DEX and significantly relieves the impairment caused by DSS-induced ulcerative colitis. Incorporating the merits of targeted drug delivery and combined therapy with an anti-inflammatory drug and ROS scavenger, the DHC NPs are promising for developing novel oral formulations for IBD therapy.

Study on the Characteristics of Small-Molecule Kinase Inhibitors-Related Drug-Induced Liver Injury.

Background and Aim: More than half of the small-molecule kinase inhibitors (KIs) induced liver injury clinically. Meanwhile, studies have shown a close relationship between mitochondrial damage and drug-induced liver injury (DILI). We aimed to study KIs and the binding between drugs and mitochondrial proteins to find factors related to DILI occurrence. Methods: A total of 1,223 oral FDA-approved drugs were collected and analyzed, including 44 KIs. Fisher's exact test was used to analyze DILI potential and risk of different factors. A total of 187 human mitochondrial proteins were further collected, and high-throughput molecular docking was performed between human mitochondrial proteins and drugs in the data set. The molecular dynamics simulation was used to optimize and evaluate the dynamic binding behavior of the selected mitochondrial protein/KI complexes. Results: The possibility of KIs to produce DILI is much higher than that of other types (OR = 46.89, p = 9.28E-13). A few DILI risk factors were identified, including molecular weight (MW) between 400 and 600, the defined daily dose (DDD) ≥ 100 mg/day, the octanol-water partition coefficient (LogP) ≥ 3, and the degree of liver metabolism (LM) more than 50%. Drugs that met this combination of rules were found to have a higher DILI risk than controls (OR = 8.28, p = 4.82E-05) and were more likely to cause severe DILI (OR = 8.26, p = 5.06E-04). The docking results showed that KIs had a significant higher affinity with human mitochondrial proteins (p = 4.19E-11) than other drug types. Furthermore, the five proteins with the lowest docking score were selected for molecular dynamics simulation, and the smallest fluctuation of the backbone RMSD curve was found in the protein 5FS8/KI complexes, which indicated the best stability of the protein 5FS8 bound to KIs. Conclusions: KIs were found to have the highest odds ratio of causing DILI. MW was significantly related to the production of DILI, and the average docking scores of KI drugs were found to be significantly different from other classes. Further analysis identified the top binding mitochondrial proteins for KIs, and specific binding sites were analyzed. The optimization of molecular docking results by molecular dynamics simulation may contribute to further studying the mechanism of DILI.

Statistical Analysis and Machine Learning Prediction of Disease Outcomes for COVID-19 and Pneumonia Patients.

The Coronavirus Disease 2019 (COVID-19) has spread all over the world and impacted many people's lives. The characteristics of COVID-19 and other types of pneumonia have both similarities and differences, which confused doctors initially to separate and understand them. Here we presented a retrospective analysis for both COVID-19 and other types of pneumonia by combining the COVID-19 clinical data, eICU and MIMIC-III databases. Machine learning models, including logistic regression, random forest, XGBoost and deep learning neural networks, were developed to predict the severity of COVID-19 infections as well as the mortality of pneumonia patients in intensive care units (ICU). Statistical analysis and feature interpretation, including the analysis of two-level attention mechanisms on both temporal and non-temporal features, were utilized to understand the associations between different clinical variables and disease outcomes. For the COVID-19 data, the XGBoost model obtained the best performance on the test set (AUROC = 1.000 and AUPRC = 0.833). On the MIMIC-III and eICU pneumonia datasets, our deep learning model (Bi-LSTM_Attn) was able to identify clinical variables associated with death of pneumonia patients (AUROC = 0.924 and AUPRC = 0.802 for 24-hour observation window and 12-hour prediction window). The results highlighted clinical indicators, such as the lymphocyte counts, that may help the doctors to predict the disease progression and outcomes for both COVID-19 and other types of pneumonia.

Preliminary Study on Hepatoprotective Effect and Mechanism of (-)-Epigallocatechin-3-gallate against Acetaminophen-induced Liver Injury in Rats.

Antipyretic acetaminophen (APAP) is a commonly used drug that generally associates with liver injury. (-)-Epigallocatechin-3-gallate (EGCG), an active polyphenol extracted from green tea, is extensively reported to have the potential to impact a variety of human diseases. However, few studies were reported regarding the protective effect of EGCG on APAP-induced liver injury and the mechanism is still unclear. In this study, in-vitro and in-vivo experiments were carried out to verify the hepatoprotective effect of EGCG against APAP-induced liver injury and explore the potential mechanism. Results indicated that EGCG effectively relieved the liver injury caused by APAP, as well as APAP-induced mitochondrial dysfunction. The protective role of EGCG was not only attributed to its antioxidant capacity; but also might be related to the protective effect on hepatic mitochondrial impairment; based on that, EGCG could improve the membrane potential and activities of the respiratory chain complexes in liver mitochondria. Our study casts a new light on the mechanism of EGCG's hepatoprotective effect and suggests that EGCG has considerable potential in developing tonics for relieving APAP-induced liver injury.

Hydroxyethyl Starch Curcumin Enhances Antiproliferative Effect of Curcumin Against HepG2 Cells via Apoptosis and Autophagy Induction.

It is well documented that curcumin (CUR), as a polyphenol molecule originated from turmeric, has many advantages such as antioxidative, anti-inflammatory, neuroprotective, and antitumor effects. However, because of its poor water solubility and low bioavailability, the biomedical applications of CUR are limited. So, in this study, we modified CUR with conjugation to a food-derived hydrophilic hydroxyethyl starch (HES) via an ester linkage to fabricate the amphiphilic conjugate HES-CUR prior to self-assembling into uniform nanoparticles (HES-CUR NPs). And, the results of the 1H NMR spectra and FT-IR spectrum showed successful synthesis of HES-CUR NPs; moreover, the solubility and the drug loading efficiency of CUR were significantly increased. Next, we further explored the differences on the antitumor effects between HES-CUR NPs and CUR in HepG2 cells, and the results of the CCK8-assay and cell counting experiment showed that HES-CUR NPs exhibited a more significant antiproliferative effect than that of CUR in HepG2 cells. And HepG2 cells were more sensitive to apoptosis induced by HES-CUR NPs as evidenced by flow cytometry, increased cytochrome c level, and decreased full length caspase-3 and Bcl-2 protein expressions. Additionally, we found that the efficacy of HES-CUR NPs against HepG2 cells might be related to the enhanced degree of mitochondrial damage (decrease of the mitochondrial membrane potential and ATP) and autophagy (increased levels of Beclin-1 and LC3-II proteins). So, the findings in this study suggest that HES-CUR NPs have a great application potential in antitumor efficacy and play an important role in multiple signal pathways.