This study was to assess the chronic morbidity and metabolic disordelters in 262 patients with type 2 diabetes.Of all participants,64(24.4%)coexisted with peripheral neuropathy,34(13.0%)combined with peripheral vascular disease.41(15.6%)were diagnosed as diabetic retinopathy,and 46 (17.6%)had concurrent diabetic nephropathy.In comparison with diabetic patients without these complications,those with the chronic conditions generally had higher plasma glucose,blood pressure or body mass index.

Objective: To explore drug resistance of different viral loads, and investigate the relationship between drug resistance and CD4⁺T cell counts in patients with HIV antiretroviral therapy (ART) in China from 2003 to 2015. Methods: Data were extracted from the Chinese National HIVDR Surveillance database from 2003 to 2015. For this study, the data collected were as follows: having received ART for ≥12 months; 18 years or older; demographic characteristics, information of ART, CD4⁺T cell counts, viral load (VL) and HIV drug resistance of a total of 8 362 patients were collected. Multi-variables non-conditional logistic regression model was used to study the relationship between viral load, HIV drug resistance and CD4⁺T cell counts. Results: Participants with age of (41.8±10.5) years were enrolled in this study. Among them, 59.9% (5 009 cases) were men. The percentage of CD4⁺T cell counts <200 cells/μl in the total population was 17.9% (1 496 cases), the highest was in VL ≥1 000 copies/ml with drug resistance, which was 43.0% (397/923) , followed by VL 50-999 copies/ml with drug resistance, which was 31.1% (69/222), and the lowest was in VL 50-999 copies/ml without drug resistance 13.2% (273/2 068). Compared to VL 50-999 copies/ml without drug resistance, VL<50 copies/ml, VL 50-999 with drug resistance, VL≥1 000 copies/ml without drug resistance, and VL ≥1 000 copies/ml with drug resistance, the OR (95%CI) of CD4 <200 cells/μl were 0.9 (0.7-1.0), 3.2 (2.3-4.4), 2.6 (2.1-3.2), and 4.9 (4.0-5.9), respectively. Among 222 patients with VL 50-999 and HIVDR, the most frequent antiretroviral drugs were EFV and NVP, both of which were NNRTI, and whose percentage both were 94.1% (209 cases). The most frequent mutations were M184V/I (NNRTI), and the percentage was 26.1% (58 cases). The second one was K103N (NNRTI), and the percentage was 22.5% (50 cases). The percentage of V32L/E (PI) and V82A (PI) were lower, they were 0.9% (2 cases) and 0.5% (1 case) respectively. Conclusion Decreased CD4⁺T cell counts were associated with HIV drug resistance at low viraemia. In the case of low viral load, the most vulnerable were the NNRTI antiviral drugs such as EFV and NVP.

Objective:To perform harmonization based on the ComBat method for PET brain imaging scanned by different types of scanners from the same manufacturer and explored its effect on center effect.Methods:The three-dimensional (3D) Hoffman brain model was scanned by two different PET/CT instruments (Siemens Biograph64 TruePoint and Biograph128 mCT). Fourteen healthy subjects (8 males, 6 females, age: (57.7±9.5) years) underwent 18F-FDG PET/CT on Siemens Biograph64 TruePoint and 12 healthy subjects (9 males, 3 females, age: (55.8±10.5) years) underwent 18F-FDG PET/CT on Siemens Biograph128 mCT (all from Huashan Hospital, Fudan University; from November 2020 to March 2023). The whole brain was divided into 116 brain regions based on the anatomical automatic labeling (AAL) brain template. The ComBat method was applied to harmonized the PET data from brain model and healthy subjects. Mann-Whitney U test was performed on the radioactive counts and SUV ratios (SUVR) before and after homogenization acquired by both PET/CT instruments. Voxel-based statistical parametric mapping (SPM) independent-sample t test was also performed on data of healthy subjects. Results:In 3D Hoffman brain model, radioactivity counts (5 590.33(4 961.67, 6 102.95) vs 6 116.03(5 420.97, 6 660.66); z=-9.35, P<0.001) and SUVR (1.35(1.19, 1.47) vs 1.37(1.21, 1.49); z=-3.63, P<0.001) were significantly different between the two PET/CT scanners before harmonization and not after harmonization (radioactivity counts: 5 845.95(5 192.68, 6 378.63) vs 5 859.17(5 193.84, 6 380.52); SUVR: 1.35(1.20, 1.48) vs 1.36(1.20, 1.49); both z=-0.68, both P=0.498). In the healthy subjects, radioactive counts in 19 brain regions (12 422.78(11 181.60, 13 424.28)-18 166.40(15 882.80, 18 666.27); z values: from -3.24 to -2.06, all P<0.05) and SUVR in 40 brain regions (1.46(1.41, 1.52)-2.28(2.16, 2.36); z values: from -3.65 to -1.70, all P<0.05) were significantly different between the two scanners before harmonization, while after homogenization there were no statistical differences for all 116 brain regions (radioactivity counts: 9 243.55(8 502.38, 9 854.87)-20 419.60(19 931.51, 21 179.43); z values: from -0.72 to 0, all P>0.05; SUVR: 1.04(1.01, 1.09)-2.32(2.24, 2.40); z values: from -0.82 to 0, all P>0.05). SPM showed that significant differences of glucose metabolism in the cerebral cortex, basal ganglia, midbrain and cerebellum were found in healthy subjects between the two PET/CT scanners before homogenization, and brain regions with obvious differences reduced after homogenization. Conclusion:ComBat harmonization method is efficient at removing the center effect among different types of PET/CT scanners from the same manufacturer and may provide a simple and easy-to-implement homogenization for multicenter brain imaging studies.

Pharmacokinetics (PK) is the study of the absorption, distribution, metabolism, and excretion (ADME) processes of a drug. Understanding PK properties is essential for drug development and precision medication. In this review we provided an overview of recent research on PK with focus on the following aspects: (1) an update on drug-metabolizing enzymes and transporters in the determination of PK, as well as advances in xenobiotic receptors and noncoding RNAs (ncRNAs) in the modulation of PK, providing new understanding of the transcriptional and posttranscriptional regulatory mechanisms that result in inter-individual variations in pharmacotherapy; (2) current status and trends in assessing drug-drug interactions, especially interactions between drugs and herbs, between drugs and therapeutic biologics, and microbiota-mediated interactions; (3) advances in understanding the effects of diseases on PK, particularly changes in metabolizing enzymes and transporters with disease progression; (4) trends in mathematical modeling including physiologically-based PK modeling and novel animal models such as CRISPR/Cas9-based animal models for DMPK studies; (5) emerging non-classical xenobiotic metabolic pathways and the involvement of novel metabolic enzymes, especially non-P450s. Existing challenges and perspectives on future directions are discussed, and may stimulate the development of new research models, technologies, and strategies towards the development of better drugs and improved clinical practice.