The general models for intermediates quality analysis in the production process of Yaobitong capsule were established by near infrared spectroscopy (NIRS) combined with chemometrics, realizing the rapid determination of notoginsenoside R1, ginsenoside Rg1, ginsenoside Re, ginsenoside Rb1, ginsenoside Rd and moisture. The spray-dried fine powder and total mixed granule were selected as research objects. The contents of five saponins were determined by high performance liquid chromatography and the moisture content was determined by drying method. The measured contents were used as reference values. Meanwhile, NIR spectra were collected. After removing abnormal samples by Monte Carlo cross validation (MCCV), Monte Carlo uninformative variables elimination (MC-UVE) and competitive adaptive reweighted sampling (CARS) were used to select feature variables respectively. Based on the feature variables, quantitative models were established by partial least squares regression (PLSR), extreme learning machine (ELM) and ant lion optimization least squares support vector machine (ALO-LSSVM). The results showed that CARS-ALO-LSSVM model had the optimum effect. The correlation coefficients of the six index components were greater than 0.93, and the relative standard errors were controlled within 6%. ALO-LSSVM was more suitable for a large number of samples with rich information, and the prediction effect and stability of the model were significantly improved. The general models with good predicting effect can be used for the rapid quality determination of Yaobitong capsule intermediates.

Objective To evaluate the bioequivalence of oral sidenafil citrate tablets manufactured(100 mg)test preparations and reference preparations in healthy subjects under fasting and fed conditions.Methods Using a single-dose,randomized,open-lable,two-period,two-way crossover design,36 healthy subjects respectively for fasting and fed study were enrolled,and randomized into two groups to receive a single dose of test 100 mg with 7-day washout period.Plasma concentration of sidenafil and N-demethylsildenafil was determined by liquid chromatography-tandem mass spectrometry(LC-MS/MS)method.The pharmacokinetic parameters were calculated by Analyst 1.6.3(AB Scie)using non-compartmental model,and bioequivalence evaluation was performed for the two preparations.Relevant safety evaluations were performed during the trial.Results The main pharmacokinetic parameters of sidenafil after a single oral dose of sidenafil citrate tablets under fasting condition for test and reference were as follows:Cmax were(494.69±230.94)and(558.78±289.83)ng·mL-1,AUC0-t were(1 336.21±509.78)and(1 410.82±625.99)h·ng·mL-1,AUC0-were(1 366.49±512.16)and(1 441.84±628.04)h·ng·mL-1,respectively.The main pharmacokinetic parameters of sidenafil under fed condition for T and R were as follows:Cmax were(381.89±126.53)and(432.47±175.91)ng·mL-1,AUC0-t were(1 366.34±366.99)and(1 412.76±420.37)h·ng·mL-1,AUC0-were(1 403.28±375.32)and(1 454.13±429.87)h·ng·mL-1,respectively.The results demonstrated the bioequivalence of sidenafil citrate tablets between T and R.The incidence of adverse events in fasting and fed tests were 33.33％and 25.00％,respectively.No serious adverse event was reported.Conclusion The test and reference formulation of sidenafil citrate tablets were equivalent and was safe.

Gadopiclenol was used in adults and pediatric patients 2 years of age and older during magnetic resonance imaging(MRI)to detect and view lesions of the central nervous system(brain,spine,and associated tissues)and body(head and neck,chest,abdomen,pelvis,and musculoskeletal system)with abnormal vascular properties.Gadopiclenol is a new type of macrocyclic gadolinium-based contrast agent(GBCA).In this article,the molecular structure,principle of action,pharmacodynamics,pharmacokinetics,clinical studies,safety and other aspects of Gadopiclenol were reviewed,in order to introduce the current research status and existing achievements of Gadopiclenol.

On May 27,2021,the U.S.Food and Drug Administration(FDA)officially approved Lantheus'PYLARIFY?(Piflufolastat F 18,18 F-labeled imaging agent),which can be used for positron emission computed tomography(PET)of prostate-specific membrane antigen(PSMA)-positive lesions in prostate cancer patients to accurately identify prostate cancer with suspected metastasis or recurrence.Piflufolastat F 18 is approved by FDA for two indications.The first is the initial staging for suspected metastatic lesions in men with newly diagnosed prostate cancer.The second is restaging,with the goal of identifying lesions in the setting of biochem ical recurrence.

Objective China is among the 30 countries with a high burden of tuberculosis(TB)worldwide,and TB remains a public health concern.Kashgar Prefecture in the southern Xinjiang Autonomous Region is considered as one of the highest TB burden regions in China.However,molecular epidemiological studies of Kashgar are lacking. Methods A population-based retrospective study was conducted using whole-genome sequencing(WGS)to determine the characteristics of drug resistance and the transmission patterns. Results A total of 1,668 isolates collected in 2020 were classified into lineages 2(46.0%),3(27.5%),and 4(26.5%).The drug resistance rates revealed by WGS showed that the top three drugs in terms of the resistance rate were isoniazid(7.4%,124/1,668),streptomycin(6.0%,100/1,668),and rifampicin(3.3%,55/1,668).The rate of rifampicin resistance was 1.8%(23/1,290)in the new cases and 9.4%(32/340)in the previously treated cases.Known resistance mutations were detected more frequently in lineage 2 strains than in lineage 3 or 4 strains,respectively:18.6%vs.8.7 or 9%,P<0.001.The estimated proportion of recent transmissions was 25.9%(432/1,668).Multivariate logistic analyses indicated that sex,age,occupation,lineage,and drug resistance were the risk factors for recent transmission.Despite the low rate of drug resistance,drug-resistant strains had a higher risk of recent transmission than the susceptible strains(adjusted odds ratio,1.414;95%CI,1.023-1.954;P = 0.036).Among all patients with drug-resistant tuberculosis(DR-TB),78.4%(171/218)were attributed to the transmission of DR-TB strains. Conclusion Our results suggest that drug-resistant strains are more transmissible than susceptible strains and that transmission is the major driving force of the current DR-TB epidemic in Kashgar.

Objective:To investigate the effect of sleep on physical performance and the correlation between sleep quality and physical performance in the elderly.Methods:In this prospective multicenter case-control study, 472 elderly people aged 60-80 years were recruited from three regions in China, Beijing, Tianjin, and Hainan Province.Basic information of study participants was collected through face-to-face interviews, and physical performance of study participants was assessed by the time up and go(TUG)test on site, with 106 cases(22.5%)in the normal physical performance group and 366 cases(77.5%)in the abnormal group.The Pittsburgh Sleep Quality Index(PSQI)and the Epworth Sleepiness Scale(ESS)were applied to assess sleep quality of study subjects.Correlation analysis was performed to examine factors affecting subjects' physical performance.Results:Age, history of alcohol consumption, BMI, past medical history, the ESS score, daytime sleepiness, and some components of PSQI, such as sleep quality, sleep efficiency, sleep disturbances, use of sleeping drugs and daytime dysfunction, were influencing factors of the TUG score.Two components of PSQI, sleep duration and habitual sleep efficiency, and the ESS score were positively correlated with physical performance.Logistic regression analysis showed that risk factors for decreased physical performance in the elderly included increased age( OR=1.125, 95% CI: 1.083-1.168, P<0.01), history of alcohol consumption( OR=0.482, 95% CI: 0.384-0.605, P<0.001), abnormally high body mass index( OR=1.663, 95% CI: 1.340-2.063, P<0.01), hyperlipemia( OR=0.156, 95% CI: 0.077-0.318, P<0.01), digestive system diseases( OR=0.154, 95% CI: 0.044-0.532, P<0.01), use of sleeping drugs( OR=0.415, 95% CI: 0.202-0.854, P<0.05), daytime sleepiness( OR=4.234, 95% CI: 2.800-6.403, P<0.01), a high habitual sleep efficiency score of PSQI( OR=1.425, 95% CI: 1.214-1.672, P<0.01)and a high sleep disturbances score in PSQI( OR=3.356, 95% CI: 2.337-4.819, P<0.01). Conclusions:The incidence of physical performance decline is high in the elderly.There is a correlation between physical performance and sleep quality.

IgA nephropathy is a common primary glomerular disease,and autophagy plays an important role in maintaining the homeostasis of the internal environment.Dysfunction of cellular autophagy can affect the occurrence and development of IgA nephropathy.This article focused on the molecular mechanism of TCM regulating autophagy in renal intrinsic cells,and found that TCM extracts and formulas mainly regulate autophagy through PI3K/Akt/mTOR,TLR4/NF-κB,MAPK,Nrf2/HO-1,NLRP3 and other signaling pathways.Furthermore,it could intervene in pathological damage such as renal fibrosis,inflammation,and oxidative stress,delaying the progression of IgA nephropathy,in order to provide reference for the clinical treatment and new drug development of IgA nephropathy.

Sodium and water retention are key and controllable risk factors that contribute to the increased risk of morbidity and mortality in patients with heart failure,and long-term continuous and effective volume management is an important strategy to help patients achieve an individualized state of optimal volume balance.The gradual application of digital health technology in the field of cardiovascular disease has provided new possibilities for volume management in patients with heart failure,while the relevant domestic studies and reports remain fewer.This article reviews the application of digital health technology in the assessment of volume status,management of volume status,volume hsk waming,health education and follow-up in patients with heart failure,and analyzes the existing problems and countermeasures,to provide a reference for digital volume management in out-of-hospital heart failure patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.