Current understanding of subjective tinnitus in adults.

PURPOSE: An up-to-date overview of epidemiology, etiology and pathophysiological mechanisms, diagnostic and evaluation methods, current treatment status and future directions of subjective tinnitus in adults. METHODS: Review of current evidence-based literature on subjective tinnitus in adults. RESULTS: The prevalence of subjective tinnitus in the adult population is estimated to be around 14%, and it tends to increase with age. Subjective tinnitus is a complex condition with multiple factors contributing to its origin. However, the exact causes and underlying mechanisms remain unknown. Potential causes may include hearing loss, dysfunction in the somatosensory system, and auditory cortical dysfunction, although severe underlying pathology is rare. Currently, diagnosis primarily relies on patient self-reported medical history and physician-based clinical assessment due to the lack of objective testing. Various treatment and management options have been proposed, but their effectiveness varies, and there is no universally agreed-upon treatment option. CONCLUSIONS: Tinnitus is a complex and heterogeneous disease with a high incidence rate and a tendency to increase with age. A holistic perspective is needed to understand the generation, perception, and emotional responses to tinnitus. Diagnosis requires a comprehensive assessment based on medical history and relevant examinations, identification of concurrent psychosomatic comorbidities, and active pursuit of objective diagnostic methods. At the same time, on the basis of existing treatment plans and combining emerging technologies, we will develop new personalized, precise, and combined treatment plans.

Clinical study of different prediction models in predicting diabetic nephropathy in patients with type 2 diabetes mellitus.

BACKGROUND: Among older adults, type 2 diabetes mellitus (T2DM) is widely recognized as one of the most prevalent diseases. Diabetic nephropathy (DN) is a frequent complication of DM, mainly characterized by renal microvascular damage. Early detection, aggressive prevention, and cure of DN are key to improving prognosis. Establishing a diagnostic and predictive model for DN is crucial in auxiliary diagnosis. AIM: To investigate the factors that impact T2DM complicated with DN and utilize this information to develop a predictive model. METHODS: The clinical data of 210 patients diagnosed with T2DM and admitted to the First People's Hospital of Wenling between August 2019 and August 2022 were retrospectively analyzed. According to whether the patients had DN, they were divided into the DN group (complicated with DN) and the non-DN group (without DN). Multivariate logistic regression analysis was used to explore factors affecting DN in patients with T2DM. The data were randomly split into a training set (n = 147) and a test set (n = 63) in a 7:3 ratio using a random function. The training set was used to construct the nomogram, decision tree, and random forest models, and the test set was used to evaluate the prediction performance of the model by comparing the sensitivity, specificity, accuracy, recall, precision, and area under the receiver operating characteristic curve. RESULTS: Among the 210 patients with T2DM, 74 (35.34%) had DN. The validation dataset showed that the accuracies of the nomogram, decision tree, and random forest models in predicting DN in patients with T2DM were 0.746, 0.714, and 0.730, respectively. The sensitivities were 0.710, 0.710, and 0.806, respectively; the specificities were 0.844, 0.875, and 0.844, respectively; the area under the receiver operating characteristic curve (AUC) of the patients were 0.811, 0.735, and 0.850, respectively. The Delong test results revealed that the AUC values of the decision tree model were lower than those of the random forest and nomogram models (P < 0.05), whereas the difference in AUC values of the random forest and column-line graph models was not statistically significant (P > 0.05). CONCLUSION: Among the three prediction models, random forest performs best and can help identify patients with T2DM at high risk of DN.

Ecological drought monitoring of Inner Mongolia vegetation growing season based on kernel temperature vegetation drought index (kTVDI).

Ecological drought monitoring is important for regional status assessment and protection of water resources. In this study, we constructed a new ecological drought index, the kernel temperature vegetation drought index (kTVDI), by using the kernel normalized vegetation index (kNDVI) to improve the temperature vegetation drought index (TVDI) in Inner Mongolia. We further analyzed the spatial and temporal distribution of ecological drought in Inner Mongolia during 2000-2022 and the future trend of ecological drought by using segmented linear regression model, Theil-Sen median, Mann-Kendall test, and Hurst index. The results showed that kTVDI performed better in monitoring ecological drought than TVDI. From 2000 to 2022, kTVDI showed a decreasing trend in the growing season in Inner Mongolia, but the change was not significant, and a sudden change occurred in 2016, and the wetting trend after the sudden change was more obvious. During the study period, ecological drought in 23.6% of the areas of Inner Mongolia showed an aggravating trend, and ecological drought was alleviated in 46.5% of the area. In the future, ecological drought would be exacerbated in the eastern part but alleviated in the central and western parts of Inner Mongolia.

Glutamine metabolism in tumor metastasis: Genes, mechanisms and the therapeutic targets.

Cancer cells frequently change their metabolism from aerobic glycolysis to lipid metabolism and amino acid metabolism to adapt to the malignant biological behaviours of infinite proliferation and distant metastasis. The significance of metabolic substances and patterns in tumour cell metastasis is becoming increasingly prominent. Tumour metastasis involves a series of significant steps such as the shedding of cancer cells from a primary tumour, resistance to apoptosis, and colonisation of metastatic sites. However, the role of glutamine in these processes remains unclear. This review summarises the key enzymes and transporters involved in glutamine metabolism that are related to the pathogenesis of malignant tumour metastasis. We also list the roles of glutamine in resisting oxidative stress and promoting immune escape. Finally, the significance of targeting glutamine metabolism in inhibiting tumour metastasis was proposed, research in this field improving our understanding of amino acid metabolism rewiring and simultaneously bringing about new and exciting therapeutic prospects.

LINC00482 sponged miR-2467-3p to promote bone metastasis of prostate cancer through activating Wnt/ß-catenin signaling pathway.

This study was designed to investigate the biological functions of LINC00482 in prostate cancer (PCa) with bone metastasis. TCGA dataset of PCa was applied for LINC00482 expression analysis and real time PCR was used to verify the expression level of LINC00482 in PCa tissues as well as PCa bone metastatic tissues. To detect the biological functions of LINC00482 in vitro, various assays were used including CCK-8, EdU, colony formation and transwell assays. The biological functions of LINC00482 were also identified in vivo by inoculating PCa cells into the left cardiac ventricle of mice, followed by evaluating the osteolytic lesions and osteolytic score. In addition, Starbase and Lncbase databases were applied for predicting the potential target miRNA of LINC00482, while TargetScan and Starbase databases were used for predicting the potential target of miRNA. The luciferase reporter assay was utilized to determine the interactions among these molecules and western blotting was employed to verified the targeted proteins. Results showed that high expression level of LINC00482 was observed in bone metastatic PCa tissues and associated with PCa progression. Silencing of LINC00482 inhibited cell proliferation, migration and invasion in PCa. Furthermore, LINC00482 was proved to act as a competing endogenous RNA by sponging miR-2467-3p to activate Wnt/ß-catenin signaling pathway, which may be a promising therapeutic target for PCa with bone metastasis.

Predict the Polarizability and Order of Magnitude of Second Hyperpolarizability of Molecules by Machine Learning.

In order to determine the polarizability and hyperpolarizability of a molecule, several key parameters need to be known, including the excitation energy of the ground and excited states, the transition dipole moment, and the difference of dipole moment between the ground and excited states. In this study, a machine-learning model was developed and trained to predict the molecular polarizability and second-order hyperpolarizability on a subset of QM9 data set. The density of states was employed as input to the model. The results demonstrated that the machine-learning model effectively estimated both polarizability and the order of magnitude of second-order hyperpolarizability. However, the model was unable to predict the dipole moment and first-order hyperpolarizability, suggesting limitations in its ability to predict the difference of dipole moment between the ground and excited states. The computational efficiency of machine-learning models compared to traditional quantum mechanical calculations enables the possibility of large-scale screening of molecules that satisfy specific requirements using existing databases. This work presents a potential solution for the efficient exploration and analysis of molecules on a larger scale.

A Case Report of a Bilateral Carotid Body Tumor and a Review of Its Imaging Manifestations.

Bilateral carotid body tumors (CBTs) are extremely uncommon. In recent years, some clinical cases have been documented. A case of bilateral CBT is reported with a review of its imaging manifestations. When a unilateral CBT is discovered, assessment of the contralateral site is essential. The 'goblet' sign is a characteristic imaging feature of CBT. The preferred imaging modality is computed tomography angiography. Teaching Point: When a carotid body tumor is suspected on one side, careful examinationof the contralateral side is essential; The preferred imaging approach is CTA.

Bone metastasis risk and prognosis assessment models for kidney cancer based on machine learning.

Background: Bone metastasis is a common adverse event in kidney cancer, often resulting in poor survival. However, tools for predicting KCBM and assessing survival after KCBM have not performed well. Methods: The study uses machine learning to build models for assessing kidney cancer bone metastasis risk, prognosis, and performance evaluation. We selected 71,414 kidney cancer patients from SEER database between 2010 and 2016. Additionally, 963 patients with kidney cancer from an independent medical center were chosen to validate the performance. In the next step, eight different machine learning methods were applied to develop KCBM diagnosis and prognosis models while the risk factors were identified from univariate and multivariate logistic regression and the prognosis factors were analyzed through Kaplan-Meier survival curve and Cox proportional hazards regression. The performance of the models was compared with current models, including the logistic regression model and the AJCC TNM staging model, applying receiver operating characteristics, decision curve analysis, and the calculation of accuracy and sensitivity in both internal and independent external cohorts. Results: Our prognosis model achieved an AUC of 0.8269 (95%CI: 0.8083-0.8425) in the internal validation cohort and 0.9123 (95%CI: 0.8979-0.9261) in the external validation cohort. In addition, we tested the performance of the extreme gradient boosting model through decision curve analysis curve, Precision-Recall curve, and Brier score and two models exhibited excellent performance. Conclusion: Our developed models can accurately predict the risk and prognosis of KCBM and contribute to helping improve decision-making.

[Dissolution test and evaluation of Guizhi Fuling Capsules].

Because of the complex components, simple content determination can hardly reflect the overall quality of Guizhi Fuling Capsules. Therefore, it is necessary to carry out a multi-component dissolution test. The variability of quality among different batches of products from different manufacturers is a common problem of Chinese medicine solid preparations. To comprehensively control the quality of Guizhi Fuling Capsules, we studied the dissolution behaviors of 7 index components in the capsules under different conditions, and investigated the consistency of dissolution behaviors among different batches of products from the same manufacturer. The basket method of general rule 0931 in Chinese Pharmacopoeia was adopted, and the rotating speeds were set at 50, 75, and 100 r·min~(-1), respectively. The hydrochloric acid solution(pH 1.2), acetate buffer solution(pH 4.0), pure water, and phosphate buffer solution(pH 6.8) were used as the dissolution media. Automatic sampling was carried out at the time points of 5, 10, 20, 30, 45, and 60 min, respectively. The cumulative dissolution of 7 index components was measured through ultra-performance liquid chromatography(UPLC). The difference factor f_1 and similarity factor f_2 were calculated to comprehensively evaluate the similarity of the dissolution curves among 8 batches of Guizhi Fuling Capsules, and a variety of dissolution and release equations were fitted. The results showed that multiple components had faster dissolution rates at higher rotating speed and in hydrochloric acid medium. The 8 batches of Guizhi Fuling capsules showed the average f_1 value lower than 15 and the average f_2 value higher than 50, which indicated that different batches of products had similar dissolution behaviors. Most components had synchronous dissolution behaviors and similar release cha-racteristics. This study provides a reference for the quality consistency evaluation among batches, processing optimization, and dosage form improvement of Guizhi Fuling Capsules.

A visualized dynamic prediction model for survival of patients with geriatric thyroid cancer: A population-based study.

Objective: Thyroid cancer (TC) is a common malignancy with a poor prognosis with aging. However, no accurate predictive survival model exists for patients with geriatric TC.We aimed to establish prediction models of prognosis in elderly TC. Methods: We retrospectively reviewed the clinicopathology characteristics of patients with geriatric TC in the Surveillance, Epidemiology, and End Results database (SEER) from 2004 to 2018. The risk predictors used to build the nomograms were derived from the Cox proportional risk regression. These nomograms were used to predict 1-, 3-, and 5-year overall survival and cancer-specific survival in elderly patients with TC. The accuracy and discriminability of the new model were evaluated by the consistency index (C-index) and calibration curve. The clinical applicability value of the model was assessed using the decision curve analysis. Results: We used the SEER database to include 16475 patients with geriatric TC diagnosed from 2004 to 2018. The patients from 2004 to 2015 were randomly sorted out on a scale of 7:3. They were classified into a training group (n = 8623) and a validation group (n = 3669). Patients with TC diagnosed in 2016-2018 were classified into external validation groups (n = 4183). The overall survival nomogram consisted of 10 variables (age, gender, marital status, histologic type, grade, TNM stage, surgery status, and tumor size). A cancer-specific survival nomogram consisted of eight factors (age, tumor size, grade, histologic type, surgery, and TNM stage). The C-index values for the training, validation, and external validation groups were 0.775 (95% confidence interval [CI] 0.785-0.765), 0.776 (95% CI 0.792-0.760), and 0.895(95% CI 0.873-0.917), respectively. The overall survival was consistent with a nomogram based on the calibration curve. Besides, the decision curve analysis showed excellent clinical application value of the nomogram. Additionally, we found that surgery could improve the prognosis of patients with geriatric at high-risk (P < 0.001) but not those at low-risk (P = 0.069). Conclusion: This was the first study to construct predictive survival nomograms for patients with geriatric TC. The well-established nomograms and the actual results could guide follow-up management strategies.

Pharmacokinetics of Herb-Drug Interactions of Plumbagin and Tazemetostat in Rats by UPLC-MS/MS.

Objective: A sensitive and rapid UPLC-MS/MS method for determination of tazemetostat in rat plasma was developed, and the pharmacokinetics of herb-drug interactions (HDIs) of plumbagin (PLB) and tazemetostat was investigated. Methods: After the rat plasma samples were precipitated by acetonitrile, tazemetostat and verubecestat (ISTD) were detected. Gradient elution was performed with 0.1% formic acid and acetonitrile as mobile phases. The multi-reaction monitoring was used with ESI+ source, and the ion pairs for tazemetostat and ISTD were m/z 573.12→135.99 and m/z 410.10→124.00, respectively. 12 SD rats were randomly divided into the control group and the experimental group, 6 rats in each group. The rats in the experimental group were given PLB 100 mg/kg by gavage once a day for 7 consecutive days. The rats in the control group were given the same amount of 0.1% sodium carboxymethyl cellulose solution by gavage once a day for 7 consecutive days. At the seventh day, tazemetostat (80 mg/kg) was given and the blood was collected at different time points. The main parameters of pharmacokinetics were calculated and the herb-drug interactions (HDIs) were evaluated. Results: In the calibrated range of 1-1000 ng/mL, tazemetostat had a good linearity. The extraction recovery was more than 84%, and the RSD of intra-batch and inter-batch precision were both less than 15%. The Cmax of tazemetostat in the experimental group was 32.48% higher than that in the control group, and the AUC(0-t) and AUC(0-∞) of tazemetostat in the experimental group were 46.24% and 46.67% higher than that in the control group, respectively, and the t1/2 was prolonged from 10.56 h to 11.73 h. Conclusion: A simple, rapid and sensitive UPLC-MS/MS method for the determination of tazemetostat in rat plasma was established. PLB can inhibit the metabolism of tazemetostat and increase the plasma exposure of tazemetostat in rats.

Effect of Chaihu Shugan Pills on the Pharmacokinetics of Duloxetine and its Metabolite 4-Hydroxyduloxetine in Beagle Dogs: A Herb-Drug Interaction Study.

The effect of Chaihu Shugan pills (CHSG) on the pharmacokinetics of duloxetine and its metabolite 4-hydroxyduloxetine in beagle dogs was investigated by establishing an ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method to simultaneously measure the concentrations of duloxetine and 4-hydroxyduloxetine in beagle dog plasma. Duloxetine and 4-hydroxyduloxetine were separated on the UPLC-C18 column after acetonitrile precipitation and detected by mass spectrometry with multireaction detection mode (MRM). Six adult healthy beagle dogs (weighing 7-9 kg, male and female) were randomly selected and examined for a single-dose administration of duloxetine hydrochloride (2 mg/kg, control group) and oral administration of CHSG (0.3 g/kg) twice daily for 15 consecutive days followed by a single-dose administration of duloxetine hydrochloride (2 mg/kg, experimental group) using the self-control method. All plasma samples were treated in the same way, and then the concentrations of duloxetine and 4-hydroxyduloxetine were determined using the established UPLC-MS/MS method. The obtained data were subjected to DAS 2.0 software to calculate the pharmacokinetic parameters, and SPSS 20.0 software was used to compare the differences between the two groups. Duloxetine and 4-hydroxyduloxetine had a good linear relationship in the ranges of 1-1000 ng/ml and 0.1-100 ng/ml, and the lower limits of quantification (LLOQ) were 1 ng/mL and 0.1 ng/ml, respectively. The precision, accuracy, extraction recovery, matrix effect, and stability meet the requirements of the guiding principles. After combination with CHSG, C max and AUC0⟶t of duloxetine decreased by 49.33% and 13.08%, respectively, and t 1/2 was shortened to 10.17 h; C max and AUC0⟶t of 4-hydroxyduloxetine decreased by 71.47% and 48.78%, respectively, and t 1/2 was shortened to 7.97 h. The UPLC-MS/MS method was fully developed to simultaneously measure the plasma concentration of duloxetine and its metabolite 4-hydroxyduloxetine in beagle dogs. CHSG could slow down the absorption of duloxetine, induce the metabolism of duloxetine and 4-hydroxyduloxetine in beagle dogs, and reduce plasma exposure.

Osteoporosis, fracture and survival: Application of machine learning in breast cancer prediction models.

The risk of osteoporosis in breast cancer patients is higher than that in healthy populations. The fracture and death rates increase after patients are diagnosed with osteoporosis. We aimed to develop machine learning-based models to predict the risk of osteoporosis as well as the relative fracture occurrence and prognosis. We selected 749 breast cancer patients from two independent Chinese centers and applied six different methods of machine learning to develop osteoporosis, fracture and survival risk assessment models. The performance of the models was compared with that of current models, such as FRAX, OSTA and TNM, by applying ROC, DCA curve analysis, and the calculation of accuracy and sensitivity in both internal and independent external cohorts. Three models were developed. The XGB model demonstrated the best discriminatory performance among the models. Internal and external validation revealed that the AUCs of the osteoporosis model were 0.86 and 0.87, compared with the FRAX model (0.84 and 0.72)/OSTA model (0.77 and 0.66), respectively. The fracture model had high AUCs in the internal and external cohorts of 0.93 and 0.92, which were higher than those of the FRAX model (0.89 and 0.86). The survival model was also assessed and showed high reliability via internal and external validation (AUC of 0.96 and 0.95), which was better than that of the TNM model (AUCs of 0.87 and 0.87). Our models offer a solid approach to help improve decision making.

A Worldwide Bibliometric Analysis of Published Literature on Osteoporosis Vertebral Compression Fracture.

Background: With the aging population growth in the world, the prevalence of osteoporotic vertebral compression fracture (OVCF) is rapidly increasing and receiving widespread attention. Although there are numerous articles on the topic, the research status and hotspot analysis are unclear. Objective: The goal of this study is to identify trends in the OVCF field and to analyze the most highly cited original articles published in the Web of Science Index on OVCF using bibliometric analysis. Methods: All OVCF data were obtained from the Web of Science Core Collection, and the number of citations, institutions, journals, countries, and years of publication in this field were visually analyzed using VOSviewer, the bibliometrics online analysis platform, and Excel software. Simultaneously, the top 100 most cited articles were extracted and analyzed. Results: A total of 756 publications were related to OVCF were included from 1900 to 2022. In recent years, the number of articles on OVCF significantly increased. They are mainly from 41 countries/regions and 202 journals, led by China and the United States. Among all countries, China had the most significant contribution on OVCF (n = 363), and it also was cited most often (n = 3337). The institution with the most articles was Soochow University (n = 40). Osteoporosis International was the journal with most studies and has published 50 on this field. The journal of Spine was cited most often (n = 1968). The most productive periods were from 2016 to 2020, which received 294 articles and 4868 citations. After the analysis, the "vertebroplasty" and "kyphoplasty" of OVCF have been the most common research hotspots. Conclusion: This study represents an updated bibliometric analysis of OVCF. The aim is to identify current research hotspots and future trends to guide clinicians and researchers in this field.

Two Novel Nomograms Predicting the Risk and Prognosis of Pancreatic Cancer Patients With Lung Metastases: A Population-Based Study.

Background: Pancreatic cancer (PC) is one of the most common malignant types of cancer, with the lung being the frequent distant metastatic site. Currently, no population-based studies have been done on the risk and prognosis of pancreatic cancer with lung metastases (PCLM). As a result, we intend to create two novel nomograms to predict the risk and prognosis of PCLM. Methods: PC patients were selected from the Surveillance, Epidemiology, and End Results Program (SEER) database from 2010 to 2016. A multivariable logistic regression analysis was used to identify risk factors for PCLM at the time of diagnosis. The multivariate Cox regression analysis was carried out to assess PCLM patient's prognostic factors for overall survival (OS). Following that, we used area under curve (AUC), time-dependent receiver operating characteristics (ROC) curves, calibration plots, consistency index (C-index), time-dependent C-index, and decision curve analysis (DCA) to evaluate the effectiveness and accuracy of the two nomograms. Finally, we compared differences in survival outcomes using Kaplan-Meier curves. Results: A total of 803 (4.22%) out of 19,067 pathologically diagnosed PC patients with complete baseline information screened from SEER database had pulmonary metastasis at diagnosis. A multivariable logistic regression analysis revealed that age, histological subtype, primary site, N staging, surgery, radiotherapy, tumor size, bone metastasis, brain metastasis, and liver metastasis were risk factors for the occurrence of PCLM. According to multivariate Cox regression analysis, age, grade, tumor size, histological subtype, surgery, chemotherapy, liver metastasis, and bone metastasis were independent prognostic factors for PCLM patients' OS. Nomograms were constructed based on these factors to predict 6-, 12-, and 18-months OS of patients with PCLM. AUC, C-index, calibration curves, and DCA revealed that the two novel nomograms had good predictive power. Conclusion: We developed two reliable predictive models for clinical practice to assist clinicians in developing individualized treatment plans for patients.

Preparation and Visible-Light Response of Salicylate-Stabilized Heterobimetallic Pb-Ti-Oxo Clusters Initiated via Auxiliary Quaternary Ammonium Salts and a Solvent Effect.

In this study, with the assistance of quaternary ammonium salts we have successfully prepared a new family of salicylate-stabilized heterobimetallic Pb-Ti-oxo clusters, including H(TEA)[Pb2Ti6(µ2-O)2(µ3-O)2(OiPr)4(PA)2(Sal)6(NO3)2] (PTC-321; TEA = tetraethylammonium; HOiPr = isopropanol; H2PA = phenylphosphonic acid; H2Sal = salicylic acid), {PbTi3(µ2-O)(µ3-O)(OiPr)2(PA)(Sal)3(DMF)·CH3CN}n (PTC-322; DMF = dimethylformamide), {PbTi5(µ3-O)6(Sal)3(CH3COO)2(DMF)(OiPr)2}n (PTC-323), [Pb2Ti4(Sal)6(EtO)2(OiPr)6(HOiPr)2]·CH3NH2 (PTC-324; EtOH = CH3CH2OH), H[Pb4Ti9(µ2-O)2(µ3-O)(µ4-O)6(Sal)7(OiPr)13] (PTC-325), and Pb2Ti12(µ2-O)3(µ3-O)3(µ4-O)4(Sal)4(OEt)24 (PTC-326). Single-crystal X-ray diffraction studies demonstrate that the {Ti3Pb(Sal)3} unit acts as the building block to constitute the diverse assembly of PTC-321-PTC-323. Thereinto, the clusters in PTC-322 and PTC-323 are connected into infinite one-dimensional chains. Furthermore, the solvent effects have facilitated the heterobimetallic Pb-Ti-oxo clusters into various configurations in PTC-323-PTC-326. Solid-state ultraviolet-visible spectroscopy analysis indicates that the optical absorption bands of these compounds shift effectively toward the visible-light region, and they were also employed as electrode precursors to investigate their visible-light-driven photocurrent response.

Application Progress of Gd-EOB-DTPA-Enhanced MRI T1 Mapping in Hepatic Diffuse Diseases.

BACKGROUND: In recent years, T1 mapping imaging based on Gd-EOB-DTPA-enhanced magnetic resonance imaging (MRI) has resulted in new research and clinical applications in hepatic diseases. OBJECTIVE: The objective of the study is to analyze, prospect, and summarize the Gd-EOB-DTPA- enhanced MRI T1 mapping technology in hepatic diseases in recent years. MAIN FINDINGS: Gd-EOB-DTPA-enhanced T1 mapping has been used more frequently in liver diseases regardless of 1.5T or 3.0T MRI equipment. Volume interpolated body examination (VIBE) mapping sequence seems to be the recommended MRI scan sequence. In the evaluation of T1 value on liver function, the hepatobiliary phase 10 minutes after enhancement is the recommended time point. The fat fraction and hepatic steatosis grade based on MRI-derived biomarkers are easier to implement and popularize than a liver biopsy. Gd-EOB-DTPA-enhanced MRI T1 mapping can not only be used to evaluate the degree of liver injury, the stage of liver fibrosis, and the liver reserve function of patients with liver cirrhosis but also to distinguish focal liver lesions and predict the differentiation degree of hepatocellular carcinoma. At the same time, it has some value in predicting tumor immunohistochemical indexes, such as Ki67, CD34. CONCLUSION: Gd-EOB-DTPA-enhanced MRI T1 mapping has great potential in the application of diffuse and focal liver lesions. It is a quantitative study, trying to select homogeneous research objects and try to use the same standards in scanning sequence and scanning time, especially for the study of liver function, which is a focus of future research. The research on the relationship between T1 value and tumor immunohistochemical indexes is worth consideration.

Infrared Imaging Meibomian Gland Segmentation System Based on Deep Learning / 中国医疗器械杂志

In order to better assist doctors in the diagnosis of dry eye and improve the ability of ophthalmologists to recognize the condition of meibomian gland, a meibomian gland image segmentation and enhancement method based on Mobile-U-Net network was proposed. Firstly, Mobile-Net is used as the coding part of U-Net for down sampling, and then features are extracted and fused with the features in decoder to guide image segmentation. Secondly, the segmentation of meibomian gland region is enhanced to assist doctors to judge the condition. Thirdly, a large number of meibomian gland images are collected to train and verify the semantic segmentation network, and the clarity evaluation index is used to verify the meibomian gland enhancement effect. The experimental results show that the similarity coefficient of the proposed method is stable at 92.71%, and the image clarity index is better than the similar dry eye detection instruments on the market.

Application of temperature vegetation dryness index for drought monitoring in Mongolian Plateau.

Taking the Mongolian Plateau as the study area, the MODIS normalized difference vegetation index (NDVI) and the land surface temperature (LST) in the growing season from 2000 to 2019 were used to construct the NDVI-LST feature space, and based on which the temperature vege-tation dryness index (TVDI) of the Mongolian Plateau was calculated. We used Theil-Sen Median trend analysis, Mann-Kendall test, and Hurst index method to analyze the spatial and temporal varia-tions and future trends of TVDI on the Mongolian Plateau. Furthermore, we examined the relationship between meteorological factors and TVDI on the Mongolian Plateau using partial correlation analysis. The results showed that the TVDI of the Mongolian Plateau during 2000-2019 showed an increasing trend with a rate of 0.0001·a-1, indicating that the Mongolian Plateau's drought condition became heavier slightly in the last 20 years. The drought condition in meadow steppe and typical steppe gradually decreased, and that in desert steppe and alpine grassland was increased. The average Hurst index of TVDI in the growing season was 0.45, and the area with TVDI less than 0.5 accounted for 71.5% of the total area, which indicated that the TVDI during 2000-2019 in most areas turned opposite to the past. In the future, the drought condition in the central desert steppe area and the eastern meadow steppe area might increase, and that in most of the typical steppe and the desert steppe in Inner Mongolia tended to decrease. The drought change in the alpine grassland area was uncertain. There was a significant positive correlation between the TVDI and temperature in 33.6% area of the Mongolian Plateau and a significant negative correlation between the TVDI and precipitation in 34.8% of the area. Moreover, the meteorological factors heavily affected the typical steppe.

Bibliometric Analysis of the Scientific Literature on Adolescent Idiopathic Scoliosis.

BACKGROUND: In recent years, there has been increasing research on adolescent idiopathic scoliosis (AIS), leading to many publications on this topic. To our knowledge, no study has focused on the research trends in this field. METHODS: Using the Web of Science Core Collection database for all articles on AIS, the number of citations, authorship, year of publication, journal of publication, country and institution of origin, and keywords were subjected to co-occurrence analysis using VOSviewer software. The top 100 most-cited articles on AIS were analyzed. RESULTS: Between 1985 and 2020, 2266 articles related to AIS were identified. The frequency of publication on AIS has increased substantially over time. Among all countries, the United States has contributed the most articles on AIS (n = 671). The most productive institution has been Nanjing University (n = 154). Spine topped the list of journals and has published 569 AIS-related articles, which received 19,862 total citations. The clinical description of AIS has been the most common research focus in the AIS literature. CONCLUSIONS: The scientific literature on AIS has rapidly expanded in recent years. This study represents the updated bibliometric analysis of scientific articles on AIS and provides a research trend for the first time, which aims to give a unique insight into the development of AIS research focus and serve as a useful guide to clinicians and researchers in the field.