Zhanhao Mo’s research while affiliated with Jilin University and other places

In prior research, both miRNA‐125b and BLZ945 have shown potential in effectively inhibiting M2 macrophage polarization and producing antitumor effects. Nevertheless, their physicochemical characteristics present significant challenges for efficient in vivo delivery. Ionizable cationic lipid nanoparticles (LNPs), recognized for their superior biocompatibility and drug‐loading capacity, serve as a novel carrier for nucleic acid‐based therapeutics. In our study, we successfully encapsulated both agents within LNPs and conducted a thorough characterization. Subsequently, we investigated their potential to repolarize M2 macrophages in vitro and evaluated their in vivo distribution, biosafety, and antitumor efficacy. The findings revealed that the LNPs maintained excellent drug‐loading efficiency, consistent particle size, and stable zeta potential. All formulations effectively inhibited M2 macrophage polarization in vitro. Upon administration in vivo, the LNPs not only demonstrated favorable biosafety profiles but also accumulated efficiently in tumor tissues, substantially reducing tumor burden, particularly notable in co‐loaded LNPs. Our results affirm that LNPs are an effective carrier for miRNA‐125b and BLZ945, highlighting this encapsulation approach as promising for the treatment of solid tumors and meriting further investigation. Practitioner points : (i) Ionizable cationic nanoparticles provide high and stable encapsulation rates to efficiently load nucleic acid polymers into the LNP, avoiding the rapid accumulation of circulating macrophages, which can lead to reduced penetration of the LNP into target tissues. Therefore, it can be used as a novel drug delivery method to benefit clinical patients. (ii) miRNA‐125b LNP/BLZ945 LNP attenuated the depleting effect of BLZ945 on macrophages and significantly inhibited macrophage M2 polarization. It could be effectively distributed in tumors and showed good biosafety while exerting antitumor effects, bringing hope to clinical pancreatic tumor patients.

Purpose Magnetic resonance imaging (MRI) refers to one of the critical image modalities for diagnosis, whereas its long acquisition time limits its application. In this study, the aim was to investigate whether deep learning–based techniques are capable of using the common information in different MRI sequences to reduce the scan time of the most time‐consuming sequences while maintaining the image quality. Method Fully sampled T1‐FLAIR, T2‐FLAIR, and T2WI brain MRI raw data originated from 217 patients and 105 healthy subjects. The T1‐FLAIR and T2‐FLAIR sequences were subsampled using Cartesian masks based on four different acceleration factors. The fully sampled T1/T2‐FLAIR images were predicted from undersampled T1/T2‐FLAIR images and T2WI images through deep learning–based reconstruction. They were qualitatively assessed by two senior radiologists in accordance with the diagnosis decision and a four‐point scale image quality score. Furthermore, the images were quantitatively assessed based on regional signal‐to‐noise ratios (SNRs) and contrast‐to‐noise ratios (CNRs). The chi‐square test was performed, where p < 0.05 indicated a difference with statistical significance. Results The diagnosis decisions from two senior radiologists remained unchanged in accordance with the accelerated and fully sampled images. There were no significant differences in the regional SNRs and CNRs of most assessed regions (p > 0.05) between the accelerated and fully sampled images. Moreover, no significant difference was identified in the image quality assessed by two senior radiologists (p > 0.05). Conclusion Deep learning–based image reconstruction is capable of significantly expediting the brain MR imaging process and producing acceptable image quality without affecting diagnosis decisions.

Background Previous studies have confirmed the separate effect of arterial stiffness and obesity on type 2 diabetes; however, the joint effect of arterial stiffness and obesity on diabetes onset remains unclear. Objective This study aimed to propose the concept of arterial stiffness obesity phenotype and explore the risk stratification capacity for diabetes. Methods This longitudinal cohort study used baseline data of 12,298 participants from Beijing Xiaotangshan Examination Center between 2008 and 2013 and then annually followed them until incident diabetes or 2019. BMI (waist circumference) and brachial-ankle pulse wave velocity were measured to define arterial stiffness abdominal obesity phenotype. The Cox proportional hazard model was used to estimate the hazard ratio (HR) and 95% CI. Results Of the 12,298 participants, the mean baseline age was 51.2 (SD 13.6) years, and 8448 (68.7%) were male. After a median follow-up of 5.0 (IQR 2.0-8.0) years, 1240 (10.1%) participants developed diabetes. Compared with the ideal vascular function and nonobese group, the highest risk of diabetes was observed in the elevated arterial stiffness and obese group (HR 1.94, 95% CI 1.60-2.35). Those with exclusive arterial stiffness or obesity exhibited a similar risk of diabetes, and the adjusted HRs were 1.63 (95% CI 1.37-1.94) and 1.64 (95% CI 1.32-2.04), respectively. Consistent results were observed in multiple sensitivity analyses, among subgroups of age and fasting glucose level, and alternatively using arterial stiffness abdominal obesity phenotype. Conclusions This study proposed the concept of arterial stiffness abdominal obesity phenotype, which could improve the risk stratification and management of diabetes. The clinical significance of arterial stiffness abdominal obesity phenotype needs further validation for other cardiometabolic disorders.

Background The triglyceride-glucose (TyG) index is a predictor of cardiovascular diseases; however, to what extent the TyG index is associated with cardiovascular diseases through renal function is unclear. This study aimed to evaluate the complex association of the TyG index and renal function with cardiovascular diseases using a cohort design. Methods This study included participants from the China Health and Retirement Longitudinal Study (CHARLS) free of cardiovascular diseases at baseline. We performed adjusted regression analyses and mediation analyses using Cox models. The TyG index was calculated as Ln [fasting triglyceride (mg/dL) × fasting glucose (mg/dL)/2]. Renal function was defined by the estimated glomerular filtration rate (eGFR). Results A total of 6 496 participants were included in this study. The mean age of the participants was 59.6 ± 9.5 years, and 2996 (46.1%) were females. During a maximum follow-up of 7.0 years, 1 996 (30.7%) people developed cardiovascular diseases, including 1 541 (23.7%) cases of heart diseases and 651 (10.0%) cases of stroke. Both the TyG index and eGFR level were significantly associated with cardiovascular diseases. Compared with people with a lower TyG index (median level) and eGFR ≥ 60 ml/minute/1.73 m², those with a higher TyG index and decreased eGFR had the highest risk of cardiovascular diseases (HR, 1.870; 95% CI 1.131–3.069). Decreased eGFR significantly mediated 29.6% of the associations between the TyG index and cardiovascular diseases. Conclusions The combination of a higher TyG index and lower eGFR level was associated with the highest risk of cardiovascular diseases. Renal function could mediate the association between the TyG index and cardiovascular risk.

Background and aims Dyslipidemia is known to contribute to arterial stiffness, while the inverse association remains unknown. This study aimed to explore the association of baseline arterial stiffness and its changes, as determined by brachial-ankle pulse wave velocity (baPWV), with dyslipidemia onset in the general population. Methods This study enrolled participants from Beijing Health Management Cohort using measurements of the first visit from 2012 to 2013 as baseline, and followed until the dyslipidemia onset or the end of 2019. Unadjusted and adjusted Cox proportional regression models were used to evaluate the associations of baseline baPWV and baPWV transition (persistent low, onset, remitted and persistent high) with incident dyslipidemia. Results Of 4362 individuals (mean age: 55.5 years), 1490 (34.2%) developed dyslipidemia during a median follow-up of 5.9 years. After adjusting for potential confounders, participants with elevated arterial stiffness at baseline had an increased risk of dyslipidemia (HR, 1.194; 95% CI, 1.050-1.358). Compared with persistent low baPWV, new-onset and persistent high baPWV were associated with a 51.2% and 37.1% excess risk of dyslipidemia. Conclusion The findings indicated that arterial stiffness is an early risk factor of dyslipidemia, suggesting a bidirectional association between arterial stiffness and lipid metabolism.

Purpose Thyroid hormones sensitivity is a newly proposed clinical entity closely related with metabolic health. Prior studies have reported the cross-sectional relationship between thyroid hormones sensitivity and diabetes; however, the longitudinal association is unclear to date. We aimed to explore the relationship between impaired thyroid hormone sensitivity at baseline and diabetes onset using a cohort design. Methods This study enrolled 7283 euthyroid participants at the first visit between 2008 and 2009, and then annually followed until diabetes onset or 2019. Thyrotropin (TSH), free triiodothyronine (FT3) and free thyroxine (FT4) were measured to calculate thyroid hormone sensitivity by thyroid feedback quantile-based index (TFQI), Chinese-referenced parametric thyroid feedback quantile-based index (PTFQI), thyrotropin index (TSHI), thyrotroph thyroxine resistance index (TT4RI) and FT3/FT4 ratio. Cox proportional hazard model and cross-lagged panel analysis were used. Results The mean baseline age was 44.2 ± 11.9 years, including 4170 (57.3%) male. During a median follow-up of 5.2 years, 359 cases developed diabetes. There was no significant association between thyroid hormones sensitivity indices and diabetes onset, and adjusted hazard ratios per unit (95% CIs) were 0.89 (0.65-1.23) for TFQI, 0.91 (0.57-1.45) for PTFQI, 0.95 (0.70-1.29) for TSHI, 0.98 (0.70-1.01) for TT4RI and 2.12 (0.17-5.78) for FT3/FT4 ratio. Cross-lagged analysis supported the temporal association from fasting glucose to impaired thyroid hormones sensitivity indices. Conclusions Our findings could not demonstrate that thyroid hormones sensitivity status is a predictor of diabetes onset in the euthyroid population. Elevated fasting glucose (above 7.0 mmol/L) appeared to precede impaired sensitivity indices of thyroid hormones.

Objectives Our objective was to use deep learning models to identify underlying brain regions associated with depression symptom phenotypes in late-life depression (LLD). Participants Diagnosed with LLD (N = 116) and enrolled in a prospective treatment study. Design Cross-sectional. Measurements Structural magnetic resonance imaging (sMRI) was used to predict five depression symptom phenotypes from the Hamilton and MADRS depression scales previously derived from factor analysis: (1) Anhedonia, (2) Suicidality, (3) Appetite, (4) Sleep Disturbance, and (5) Anxiety. Our deep learning model was deployed to predict each factor score via learning deep feature representations from 3D sMRI patches in 34 a priori regions-of-interests (ROIs). ROI-level prediction accuracy was used to identify the most discriminative brain regions associated with prediction of factor scores representing each of the five symptom phenotypes. Results Factor-level results found significant predictive models for Anxiety and Suicidality factors. ROI-level results suggest the most LLD-associated discriminative regions in predicting all five symptom factors were located in the anterior cingulate and orbital frontal cortex. Conclusions We validated the effectiveness of using deep learning approaches on sMRI for predicting depression symptom phenotypes in LLD. We were able to identify deep embedded local morphological differences in symptom phenotypes in the brains of those with LLD, which is promising for symptom-targeted treatment of LLD. Future research with machine learning models integrating multimodal imaging and clinical data can provide additional discriminative information.

Fast and low-dose reconstructions of medical images are highly desired in clinical routines. We propose a hybrid deep-learning and iterative reconstruction (hybrid DL-IR) framework and apply it for fast magnetic resonance imaging (MRI), fast positron emission tomography (PET), and low-dose computed tomography (CT) image generation tasks. First, in a retrospective MRI study (6,066 cases), we demonstrate its capability of handling 3- to 10-fold under-sampled MR data, enabling organ-level coverage with only 10- to 100-s scan time; second, a low-dose CT study (142 cases) shows that our framework can successfully alleviate the noise and streak artifacts in scans performed with only 10% radiation dose (0.61 mGy); and last, a fast whole-body PET study (131 cases) allows us to faithfully reconstruct tumor-induced lesions, including small ones (<4 mm), from 2- to 4-fold-accelerated PET acquisition (30-60 s/bp). This study offers a promising avenue for accurate and high-quality image reconstruction with broad clinical value.