The human gut-resident commensal microbiota is a unique ecosystem associated with various bodily functions, especially immunity. Gut microbiota dysbiosis plays a crucial role in autoimmune disease pathogenesis as well as in bowel-related diseases. However, the role of the gut microbiota, which causes or influences systemic immunity in autoimmune diseases, remains elusive. Aryl hydrocarbon receptor, a ligand-activated transcription factor, is a master moderator of host-microbiota interactions because it shapes the immune system and impacts host metabolism. In addition, treatment optimization while minimizing potential adverse effects in autoimmune diseases remains essential, and modulation of the gut microbiota constitutes a potential clinical therapy. Here, we present evidence linking gut microbiota dysbiosis with autoimmune mechanisms involved in disease development to identify future effective approaches based on the gut microbiota for preventing autoimmune diseases.

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Major issues in photocatalysis include improving charge carrier separation efficiency at the interface of semiconductor photocatalysts and rationally developing efficient hierarchical heterostructures. Surface continuous growth deposition is used to make hollow Cu2‐xS nanoboxes, and then simple hydrothermal reaction is used to make core‐shell Cu2‐xS@ZnIn2S4 S‐scheme heterojunctions. The photothermal and photocatalytic performance of Cu2‐xS@ZnIn2S4 is improved. In an experimental hydrogen production test, the Cu2‐xS@ZnIn2S4 photocatalyst produces 4653.43 µmol h−1 g−1 of hydrogen, which is 137.6 and 13.8 times higher than pure Cu2‐xS and ZnIn2S4, respectively. Furthermore, the photocatalyst exhibits a high tetracycline degradation efficiency in the water of up to 98.8%. For photocatalytic reactions, the hollow core‐shell configuration gives a large specific surface area and more reactive sites. The photocatalytic response range is broadened, infrared light absorption enhanced, the photothermal effect is outstanding, and the photocatalytic process is promoted. Meanwhile, characterizations, degradation studies, active species trapping investigations, energy band structure analysis, and theoretical calculations all reveal that the S‐scheme heterojunction can efficiently increase photogenerated carrier separation. This research opens up new possibilities for future S‐scheme heterojunction catalyst design and development.

Triptolide (TP), a major active component of Tripterygium wilfordii Hook.F. (TWHF), is used to treat rheumatoid arthritis (RA). However, it has a narrow therapeutic window due to its serious toxicities. To increase the therapeutic index, a new triptolide-loaded transdermal delivery system, named triptolide-loaded liposome hydrogel patch (TP-LHP), has been developed. In this paper, we used a micro-needle array to deliver TP-LHP to promote transdermal absorption and evaluated this treatment on the pharmacokinetics and pharmacodynamics of TP-LHP in a rat model of collagen-induced arthritis (CIA). The pharmacokinetic results showed that transdermal delivery of microneedle TP-LHP yielded plasma drug levels which fit a one-compartment open model. The relationship equation between plasma concentration and time was C=303.59×(e−0.064t−e−0.287t). The results of pharmacodynamic study demonstrated that TP-LHP treatment mitigated the degree of joint swelling and suppressed the expressions of fetal liver kinase-1, fetal liver tyrosine kinase-4 and hypoxia-inducible factor-1α in synovium. Other indicators were also reduced by TP-LHP, including hyperfunction of immune, interleukin-1β and interleukin-6 levels in serum. The therapeutic mechanism of TP-LHP might be regulation of the balance between Th1 and Th2, as well as inhibition of the expression and biological effects of vascular endothelial growth factor.

The aim of this study was to evaluate effect of diosgenin (DG) on rats that had osteoporosis-like features induced by ovariectomy (OVX). Seventy-two six-month-old female Wistar rats were subjected to either ovariectomy (n = 60) or Sham operation (SHAM group, n = 12). Beginning at one week post-ovariectomy, the OVX rats were treated with vehicle (OVX group, n = 12), estradiol valerate (EV group, n = 12), or DG at three doses (DG-L, -M, -H group, n = 12, respectively). After a 12-week treatment, administration of EV or DG-H inhibited OVX-induced weight gain, and administration of EV or DG-H or DG-M had a significantly uterotrophic effect. Bone mineral density (BMD) and indices of bone histomorphometry of tibia were measured. Levels of protein and mRNA expression of osteoprotegerin (OPG) and receptor activator of nuclear factor kappa-B ligand (RANKL) in tibia were evaluated by immunohistochemistry and in situ hybridization. Our results show that DG at a high dose (DG-H) had a significant anti-osteoporotic effect compared to OVX control. DG-H treatment down-regulated expression of RANKL and up-regulated expression of OPG significantly in tibia from OVX rats compared to control, and thus lowered the RANKL/OPG ratio. This suggests that the anti-osteoporotic effect of DG might be associated with modulating the RANKL/OPG ratio and DG had potential to be developed as alternative therapeutic agents of osteoporosis induced by postmenopause.

Background To establish pharmacokinetic parameters and a radiomics model based on dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) for predicting sentinel lymph node (SLN) metastasis in patients with breast cancer. Methods A total of 164 breast cancer patients confirmed by pathology were prospectively enrolled from December 2017 to May 2018, and underwent DCE-MRI before surgery. Pharmacokinetic parameters and radiomics features were derived from DCE-MRI data. Least absolute shrinkage and selection operator (LASSO) regression method was used to select features, which were then utilized to construct three classification models, namely, the pharmacokinetic parameters model, the radiomics model, and the combined model. These models were built through the logistic regression method by using 10-fold cross validation strategy and were evaluated on the basis of the receiver operating characteristics (ROC) curve. An independent validation dataset was used to confirm the discriminatory power of the models. Results Seven radiomics features were selected by LASSO logistic regression. The radiomics model, the pharmacokinetic parameters model, and the combined model yielded area under the curve (AUC) values of 0.81 (95% confidence interval [CI]: 0.72 to 0.89), 0.77 (95% CI: 0.68 to 0.86), and 0.80 (95% CI: 0.72 to 0.89), respectively, for the training cohort and 0.74 (95% CI: 0.59 to 0.89), 0.74 (95% CI: 0.59 to 0.90), and 0.76 (95% CI: 0.61 to 0.91), respectively, for the validation cohort. The combined model showed the best performance for the preoperative evaluation of SLN metastasis in breast cancer. Conclusions The model incorporating radiomics features and pharmacokinetic parameters can be conveniently used for the individualized preoperative prediction of SLN metastasis in patients with breast cancer.

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