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Drug delivery systems with high content of drug can minimize excipients administration, reduce side effects, improve therapeutic efficacy and/or promote patient compliance. However, engineering such systems is extremely challenging, as their loading capacity is inherently limited by the compatibility between drug molecules and carrier materials. To mitigate the drug-carrier compatibility limitation towards therapeutics encapsulation, we developed a sequential solidification strategy. In this strategy, the precisely controlled diffusion of solvents from droplets ensures the fast in-droplet precipitation of drug molecules prior to the solidification of polymer materials. After polymer solidification, a mass of drug nanoparticles is embedded in the polymer matrix, forming a nano-in-micro structured microsphere. All the obtained microspheres exhibit long-term storage stability, controlled release of drug molecules, and most importantly, high mass fraction of therapeutics (21.8–63.1 wt%). Benefiting from their high drug loading degree, the nano-in-micro structured acetalated dextran microspheres deliver a high dose of methylprednisolone (400 μg) within the limited administration volume (10 μL) by one single intrathecal injection. The amount of acetalated dextran used was 1/433 of that of low drug-loaded microspheres. Moreover, the controlled release of methylprednisolone from high drug-loaded microspheres contributes to improved therapeutic efficacy and reduced side effects than low drug-loaded microspheres and free drug in spinal cord injury therapy. High drug loading improves therapeutic efficacy and reduces side effects in drug delivery. Here, the authors use controlled diffusion of solvents to precipitate drug nanoparticles in polymer particles while the polymer is solidifying and demonstrate the particles for drug delivery in a spinal cord injury model.

Animal experiments traditionally identify sensitizers in cosmetic materials. However, with growing concerns over animal ethics and bans on such experiments globally, alternative methods like machine learning are gaining prominence for their efficiency and cost‐effectiveness. In this study, to develop a robust sensitizer detector model, we first constructed benchmark data sets using data from previous studies and a public database, then 589 sensitizers and 831 nonsensitizers were collected. In addition, a graph‐based autoencoder and Mondrian conformal prediction (MCP) were combined to build a robust sensitizer detector, iSKIN. In the independent test set, the Matthews correlation coefficient (MCC) and the area under the receiver operating characteristic curve (ROCAUC) values of the iSKIN model without MCP were 0.472 and 0.804, respectively, which are higher than those of the three baseline models. When setting the significance level in MCP at 0.7, the MCC and ROCAUC values of iSKIN could achieve 0.753 and 0.927, respectively. Regrouping experiments proved that the MCP method is robust in the improvement of model performance. Through key structure analysis, seven key substructures in sensitizers were identified to guide cosmetic material design. Notably, long chains with halogen atoms and phenyl groups with two chlorine atoms at ortho‐positions were potential sensitizers. Finally, a user‐friendly web tool (http://www.iskin.work/) of the iSKIN model was deployed to be used by other researchers. In summary, the proposed iSKIN model has achieved state‐of‐the‐art performance so far, which can contribute to the safety evaluation of cosmetic raw materials and provide a reference for the chemical structure design of these materials. Here, we propose a novel skin sensitizer detection platform iSKIN (http://www.iskin.work/). By jointly using graph‐based autoencoder and Mondrian conformal prediction, the proposed iSKIN model is able to achieve a balance between improvement in model accuracy and application domain, thereby achieving state‐of‐the‐art performance so far. The iSKIN model can contribute to the safety evaluation of cosmetic raw materials and provide a reference for the chemical structure design of the materials.

TiaoShenGongJian (TSGJ) decoction, a traditional Chinese medicine for breast cancer, has unknown active compounds, targets, and mechanisms. This study identifies TSGJ's key targets and compounds for breast cancer treatment through network pharmacology, machine learning, and experimental validation. Bioactive components and targets of TSGJ were identified from the TCMSP database, and breast cancer-related targets from GeneCards, PharmGkb, and RNA-seq datasets. Intersection of these targets revealed therapeutic targets of TSGJ. PPI analysis was performed via STRING, and machine learning methods (SVM, RF, GLM, XGBoost) identified key targets, validated by GSE70905, GSE70947, GSE22820, and TCGA-BRCA datasets. Pathway analyses and molecular docking were performed. TSGJ and core compounds' effectiveness was confirmed by MTT and RT-qPCR assays. 160 common targets of TSGJ were identified, with 30 hub targets from PPI analysis. Five predictive targets (HIF1A, CASP8, FOS, EGFR, PPARG) were screened via SVM. Their diagnostic, biomarker, immune, and clinical values were validated. Quercetin, luteolin, and baicalein were identified as core components. Molecular docking confirmed their strong affinities with predicted targets. These compounds modulated key targets and induced cytotoxicity in breast cancer cell lines in a similar way as TSGJ. This study reveals the main active components and targets of TSGJ against breast cancer, supporting its potential for breast cancer prevention and treatment.

A key challenge in drug development is identification of druggable targets, the modulation of which attenuates disease progression, while avoiding inhibition of proteins that lead to dose-limiting toxicities. Here, we investigate a drug target Casein kinase 2 (CK2) - a serine/threonine kinase implicated in cancer, for which existing inhibitors have so far failed clinical trials. Using molecular and pharmacoepidemiology approaches, we show that small molecules targeting CDK kinase family members CDK1/2/7/9 - such as the existing CK2 inhibitors - have a higher risk to induce adverse effects or fail in clinical trials. Based on this finding, we establish a machine learning assisted pipeline to redesign more specific and allosteric lead compounds against CK2, with a more selective on-target binding and favourable off-target profile. Importantly, we show that such design is possible via machine learning (ML) -powered, docking assisted discovery pipeline, when standard ML algorithms are combined with an error prediction model. In conclusion, our study reports a simple yet efficient ML-powered drug discovery pipeline and novel submicromolar inhibitors targeting clinically relevant CK2 kinase with no clinically approved antagonists. Importantly, our prediction pipeline was able to achieve a 90% hit-rate, significantly reducing the need for subsequent wet-lab validation.Competing Interest StatementJordi Mestres is the founder and research director of Chemotargets. Mitro Miihkinen and Tero Aittokallio receive research and salary funding from Mobius biotechnology. The authors declare no other competing interests.Footnotes* New figure 5 added characterising molecular properties of new compounds. New phosphoproteomic analyses added on Figure 4 and improved all figures altogether. Removed multiple typoes.* https://github.com/mitroe/CK2_kinase_inhibitor_prediction/

Upper gastrointestinal cancers (including oesophageal cancer and gastric cancer) are the most common cancers worldwide. Artificial intelligence platforms using deep learning algorithms have made remarkable progress in medical imaging but their application in upper gastrointestinal cancers has been limited. We aimed to develop and validate the Gastrointestinal Artificial Intelligence Diagnostic System (GRAIDS) for the diagnosis of upper gastrointestinal cancers through analysis of imaging data from clinical endoscopies. This multicentre, case-control, diagnostic study was done in six hospitals of different tiers (ie, municipal, provincial, and national) in China. The images of consecutive participants, aged 18 years or older, who had not had a previous endoscopy were retrieved from all participating hospitals. All patients with upper gastrointestinal cancer lesions (including oesophageal cancer and gastric cancer) that were histologically proven malignancies were eligible for this study. Only images with standard white light were deemed eligible. The images from Sun Yat-sen University Cancer Center were randomly assigned (8:1:1) to the training and intrinsic verification datasets for developing GRAIDS, and the internal validation dataset for evaluating the performance of GRAIDS. Its diagnostic performance was evaluated using an internal and prospective validation set from Sun Yat-sen University Cancer Center (a national hospital) and additional external validation sets from five primary care hospitals. The performance of GRAIDS was also compared with endoscopists with three degrees of expertise: expert, competent, and trainee. The diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of GRAIDS and endoscopists for the identification of cancerous lesions were evaluated by calculating the 95% CIs using the Clopper-Pearson method. 1 036 496 endoscopy images from 84 424 individuals were used to develop and test GRAIDS. The diagnostic accuracy in identifying upper gastrointestinal cancers was 0·955 (95% CI 0·952–0·957) in the internal validation set, 0·927 (0·925–0·929) in the prospective set, and ranged from 0·915 (0·913–0·917) to 0·977 (0·977–0·978) in the five external validation sets. GRAIDS achieved diagnostic sensitivity similar to that of the expert endoscopist (0·942 [95% CI 0·924–0·957] vs 0·945 [0·927–0·959]; p=0·692) and superior sensitivity compared with competent (0·858 [0·832–0·880], p<0·0001) and trainee (0·722 [0·691–0·752], p<0·0001) endoscopists. The positive predictive value was 0·814 (95% CI 0·788–0·838) for GRAIDS, 0·932 (0·913–0·948) for the expert endoscopist, 0·974 (0·960–0·984) for the competent endoscopist, and 0·824 (0·795–0·850) for the trainee endoscopist. The negative predictive value was 0·978 (95% CI 0·971–0·984) for GRAIDS, 0·980 (0·974–0·985) for the expert endoscopist, 0·951 (0·942–0·959) for the competent endoscopist, and 0·904 (0·893–0·916) for the trainee endoscopist. GRAIDS achieved high diagnostic accuracy in detecting upper gastrointestinal cancers, with sensitivity similar to that of expert endoscopists and was superior to that of non-expert endoscopists. This system could assist community-based hospitals in improving their effectiveness in upper gastrointestinal cancer diagnoses. The National Key R&D Program of China, the Natural Science Foundation of Guangdong Province, the Science and Technology Program of Guangdong, the Science and Technology Program of Guangzhou, and the Fundamental Research Funds for the Central Universities.

Hydrogel-based devices are widely used as flexible electronics, biosensors, soft robots, and intelligent human-machine interfaces. In these applications, high stretchability, low hysteresis, and anti-fatigue fracture are essential but can be rarely met in the same hydrogels simultaneously. Here, we demonstrate a hydrogel design using tandem-repeat proteins as the cross-linkers and random coiled polymers as the percolating network. Such a design allows the polyprotein cross-linkers only to experience considerable forces at the fracture zone and unfold to prevent crack propagation. Thus, we are able to decouple the hysteresis-toughness correlation and create hydrogels of high stretchability (~1100%), low hysteresis (< 5%), and high fracture toughness (~900 J m −2 ). Moreover, the hydrogels show a high fatigue threshold of ~126 J m −2 and can undergo 5000 load-unload cycles up to 500% strain without noticeable mechanical changes. Our study provides a general route to decouple network elasticity and local mechanical response in synthetic hydrogels. High stretchability, low hysteresis and anti-fatigue fracture are essential for hydrogel-based devices but it is rare to achieve. Here the authors demonstrate a hydrogel design using tandem-repeat proteins as the cross-linkers and random coiled polymers as the percolating network which results in high stretchability, low hysteresis and high fracture toughness.

The study is to evaluate the performance of ocular biometric measures and uncorrected visual acuity (UCVA) for detecting myopia among Chinese students. Among 5- to 18-year-old Chinese students from two cities of China, trained eye-care professionals performed assessment of ocular biometrics (axial length (AL), corneal curvature radius (CR), anterior chamber depth) under noncycloplegic conditions using NIDEK Optical Biometer AL-Scan, distance visual acuity using retro-illuminated logMAR chart with tumbling-E optotypes, and cycloplegic refractive error using NIDEK autorefractor with administration of 0.5% tropicamide. Spherical equivalent (SER) in diopters (D) was calculated as sphere plus half cylinder, and myopia was defined as SER ≤ − 0.5 D. Performances of ocular biometrics and UCVA (individually and in combination) for detecting myopia were evaluated using sensitivity and specificity, predictive values, and area under ROC curve (AUC) in both development dataset and validation dataset. Among 3436 students (mean age 9.7 years, 51% female), the mean (SD) cycloplegic SER was − 0.20 (2.18) D, and 1269 (36.9%) had myopia. Cycloplegic SER was significantly correlated with AL (Pearson Correlation coefficient r = − 0.82), AL/CR ratio (r = − 0.90), and UCVA (r = 0.79), but was not correlated with CR (r = 0.02, p = 0.15). The AL/CR ratio detected myopia with AUC 0.963 (95% CI 0.957–0.969) and combination with UCVA improved the AUC to 0.976 (95% CI 0.971–0.981). Using age-specific AL/CR cutoff (> 3.00 for age < 10 years, > 3.06 for 10–14 years, > 3.08 for ≥ 15 years) as myopia positive, the sensitivity and specificity were 87.0% (95% CI 84.4–89.6%) and 87.8% (86.0–89.6%), respectively, in the development dataset and 86.4% (95% CI 83.7–89.1%) and 89.4% (95% CI 87.3–91.4%), respectively, in the validation dataset. Combining AL/CR and UCVA (worse than 20/32 for age < 10 years, and 20/25 for ≥ 10 years) provided 91.9% (95% CI 90.4–93.4%) sensitivity and 87.0% (95% CI 85.6–88.4%) specificity, positive value of 80.6% (95% CI 78.5–82.6%) and negative value of 94.8% (95% CI 93.8–95.8%). These results suggest that AL/CR ratio is highly correlated with cycloplegic refractive error and detects myopia with high sensitivity and specificity,  AL/CR ratio alone or in combination with UCVA can be used as a tool for myopia screening or for estimating myopia prevalence in large epidemiological studies with limited resources for cycloplegic refraction.

Background Research on the brain-gut-microbiota axis has led to accumulating interest in gut microbiota dysbiosis and intestinal barrier dysfunction in Alzheimer’s disease (AD). Our previous studies have demonstrated neurotoxic effects of 27-hydroxycholesterol (27-OHC) in in vitro and in vivo models. Here, alterations in the gut microbiota and intestinal barrier functions were investigated as the possible causes of cognitive deficits induced by 27-OHC treatment. Methods Male APP/PS1 transgenic and C57BL/6J mice were treated for 3 weeks with 27-OHC (5.5 mg/kg/day, subcutaneous injection) and either a 27-OHC synthetase inhibitor (anastrozole, ANS) or saline. The Morris water maze and passive avoidance test were used to assess cognitive impairment. Injuries of the intestine were evaluated by histopathological examination. Intestinal barrier function was assessed by plasma diamine oxidase (DAO) activity and d -lactate. Systemic and intestinal inflammation were evaluated by IL-1β, TNF-α, IL-10, and IL-17 concentrations as determined by ELISA. The fecal microbiome and short-chain fatty acids (SCFAs) were analyzed using 16S rDNA sequencing and ultra-performance liquid chromatography-mass spectrometry (UPLC-MS). Tight junction proteins were evaluated in the ileum and colon by qRT-PCR and Western blots. Tight junction ultrastructure was examined by transmission electron microscopy. Results Treatment with 27-OHC resulted in severe pathologies in the ileum and colon. There was impaired intestinal barrier integrity as indicated by dilated tight junctions and downregulation of tight junction proteins, including occludin, claudin 1, claudin 5, and ZO-1, and signs of inflammation (increased IL-1β, TNF-α, and IL-17). Fecal 16S rDNA sequencing and taxonomic analysis further revealed a decreased abundance of Roseburia and reduced fecal levels of several SCFAs in 27-OHC-treated mice. Meanwhile, co-treatment with ANS reduced intestinal inflammation and partially preserved intestinal barrier integrity in the presence of 27-OHC. Conclusions The current study demonstrates for the first time that 27-OHC treatment aggravates AD-associated pathophysiological alterations, specifically gut microbiota dysbiosis and intestinal barrier dysfunction, which suggests that the gut microbiome and intestinal barrier function warrant further investigation as potential targets to mitigate the neurotoxic impact of 27-OHC on cognitive function and the development of AD.

Diabetic Peripheral Neuropathy (DPN) typically is accompanied by painful symptoms. Several therapeutic agents have been tried for symptomatic relief, but with varying results. The use of non-invasive neuromodulation (NINM) is a potential treatment option for DPN. The objective of our study is to evaluate NINM effects on pain rating and nerve conduction velocity in DPN patients. The search was carried out in seven databases until Aug 30th, 2019. Finally, twenty studies met the inclusion criteria. We found a significant reduction of pain scores by central NINMs (effect size [ES] =  − 0.75, 95% CI =  − 1.35 to − 0.14), but not by the overall peripheral techniques (electrical and electromagnetic) (ES =  − 0.58, 95% CI =  − 1.23 to 0.07). However, the subgroup of peripheral electrical NINMs reported a significant higher effect (ES =  − 0.84, 95% CI =  − 1.57 to − 0.11) compared to electromagnetic techniques (ES = 0.21; 95% CI =  − 1.00 to 1.42, I 2  = 95.3%) . Other subgroup analysis results show that NINMs effects are higher with intensive protocols and in populations with resistant symptoms or intolerance to analgesic medications. Besides, NINMs can increase motor nerves velocity (ES = 1.82; 95% CI = 1.47 to 2.17), and there were no effects on sensory nerves velocity (ES = 0.01, 95% CI =  − 0.79 to 0.80). The results suggest that central and peripheral electrical NINMs could reduce neuropathic pain among DPN patients, without reported adverse events. Well-powered studies are needed to confirm that NINM techniques as an alternative effective and safe treatment option.

While ectonucleotidase CD39 is a cancer therapeutic target in clinical trials, its direct effect on T-cell differentiation in human non-small-cell lung cancer (NSCLC) remains unclear. Herein, we demonstrate that human NSCLC cells, including tumor cell lines and primary tumor cells from clinical patients, efficiently drive the metabolic adaption of human CD4 + T cells, instructing differentiation of regulatory T cells while inhibiting effector T cells. Of importance, NSCLC-induced T-cell mal-differentiation primarily depends on cancer CD39, as this can be fundamentally blocked by genetic depletion of CD39 in NSCLC. Mechanistically, NSCLC cells package CD39 into their exosomes and transfer such CD39-containing exosomes into interacting T cells, resulting in ATP insufficiency and AMPK hyperactivation. Such CD39-dependent NSCLC-T cell interaction holds well in patients-derived primary tumor cells and patient-derived organoids (PDOs). Accordingly, genetic depletion of CD39 alone or in combination with the anti-PD-1 immunotherapy efficiently rescues effector T cell differentiation, instigates anti-tumor T cell immunity, and inhibits tumor growth of PDOs. Together, targeting cancer CD39 can correct the mal-differentiation of CD4 + T cells in human NSCLC, providing in-depth insight into therapeutic CD39 inhibitors.