Mathematical modeling for local trans-round window membrane drug transport in the inner ear

<p>The structure and composition of the round window membrane (RWM) make it a particularly effective pathway for drug delivery to the inner ear. Therefore, predicting the efficiency of RWM transport would provide useful information for enhancing local application. In the present study, a mathematical model was established to achieve this goal. A series of drugs with different physicochemical properties were introduced in the inner ear cavity of guinea pigs via RWM by intratympanic application. The perilymphatic drug concentration (C) data were used to calculate the permeability coefficient (<i>K_p</i>) of different drugs diffusing through the RWM. The experimental data were fitted using the Matlab software to set up the numerical model based on Fick’s diffusion law and the single-compartment model following extravascular administration, which facilitated the prediction of the permeation profiles of different drugs while trans-RWM. In summary, this mathematical model is a contribution toward developing potentially useful RWM administration simulating tools.</p

Cu-Catalyzed Enantioselective Boron Addition to <i>N</i>‑Heteroaryl-Substituted Alkenes

Catalytic enantioselective Cu–B­(pin) (pin = pinacolato) addition to <i>N</i>-heteroaryl-substituted alkenes followed by protonation promoted by phosphine–Cu complexes is presented. The resulting alkylboron products that contain a <i>N</i>-heteroaryl moiety are afforded in up to 97% yield and 99:1 enantiomeric ratio. The highly versatile C–B­(pin) bond can be converted to a range of useful functional groups, delivering a variety of enantiomerically enriched building blocks that are otherwise difficult to access. The utility of this method is further demonstrated by application to a fragment synthesis of biologically active molecule U-75302. Preliminary mechanistic studies revealed that the adjacent N atom of the heterocycles plays a unique role in high reactivity and enantioselectivity

DataSheet_1_Evaluation and driving factors of ecological integrity in the Alxa League from 1990 to 2020.pdf

Ecological integrity can satisfactorily reflect the comprehensive quality of ecosystems and has become a useful tool for evaluating the ecological environment. Ecological integrity evaluation has been widely applied in various ecosystems. Conducted in the Alxa League, the study established an ecological integrity index based on ecosystem structure, function and resilience and evaluated the ecological integrity of the study area in 1990, 2000, 2010 and 2020. Using hotspots spatial analyses, we analyzed the temporal and spatial variation of ecological integrity index during the study period. The main contributing factors affecting ecological integrity were identified with the help of the geographical detector model. Our results showed that: (1) Ecosystem structure, function and resilience in the Alxa League had obvious spatial heterogeneity and barely changed from 1990 to 2020. (2) Half of the area had a poor ecological integrity index, and the decrease in ecological integrity mainly occurred in the Alxa Left Banner. (3) Among the factors affecting the ecological integrity index, land use intensity was the major driving factor, and desertification was a key reason leading to the decrease. Ecological integrity evaluation can increase public awareness of desert conditions and guide policy makers to make reasonable and sustainable policies or strategies to protect and restore desert ecosystems.</p

DataSheet_1_Comparison of preoperative CT- and MRI-based multiparametric radiomics in the prediction of lymph node metastasis in rectal cancer.docx

ObjectiveTo compare computed tomography (CT)- and magnetic resonance imaging (MRI)-based multiparametric radiomics models and validate a multi-modality, multiparametric clinical-radiomics nomogram for individual preoperative prediction of lymph node metastasis (LNM) in rectal cancer (RC) patients.Methods234 rectal adenocarcinoma patients from our retrospective study cohort were randomly selected as the training (n = 164) and testing (n = 70) cohorts. The radiomics features of the primary tumor were extracted from the non-contrast enhanced computed tomography (NCE-CT), the enhanced computed tomography (CE-CT), the T2-weighted imaging (T2WI) and the gadolinium contrast-enhanced T1-weighted imaging (CE-TIWI) of each patient. Three kinds of models were constructed based on training cohort, including the Clinical model (based on the clinical features), the radiomics models (based on NCE-CT, CE-CT, T2WI, CE-T1WI, CT, MRI, CT combing with MRI) and the clinical-radiomics models (based on CT or MRI radiomics model combing with clinical data) and Clinical-IMG model (based on CT and MRI radiomics model combing with clinical data). The performances of the 11 models were evaluated via the area under the receiver operator characteristic curve (AUC), accuracy, sensitivity, and specificity in the training and validation cohort. Differences in the AUCs among the 11 models were compared using DeLong’s test. Finally, the optimal model (Clinical-IMG model) was selected to create a radiomics nomogram. The performance of the nomogram to evaluate clinical efficacy was verified by ROC curves and decision curve analysis (DCA).ResultsThe MRI radiomics model in the validation cohort significantly outperformed than CT radiomics model (AUC, 0.785 vs. 0.721, pConclusionMRI radiomics model performed better than both CT radiomics model and Clinical model in predicting LNM of RC. The clinical-radiomics nomogram that combines the radiomics features obtained from both CT and MRI along with preoperative clinical characteristics exhibits the best diagnostic performance.</p

Characteristics of all subjects.

<p>The data are presented as mean±S.E.</p><p>*<i>P</i> < 0.05</p><p>***<i>P</i> < 0.001 versus normal healthy controls.</p><p>BMI: body mass index; HbA1c: hemoglobin A1c; TG: triglyceride; Cr: creatinine; BUN: blood urea nitrogen; ACR, albumin to creatinine ratio; H-CRP: high-sensitive C-reactive protein. ACEI: angiotensin-converting enzyme inhibitor; ARB: angiotensin Ⅱ receptor antagonist</p><p>Characteristics of all subjects.</p

The Density of Soil Organic Carbon and the Economic Loss in Different Degradation Grasslands.

<p>Note: ND, LD, MD, HD, SD represent non-degradation, light degradation, moderate degradation, heavy degradation and severely degradation, respectively.</p

The Aboveground Biomass and Economic Loss of Different Degradation Grasslands.

<p>Note: ND, LD, MD, HD, SD represent non-degradation, light degradation, moderate degradation, heavy degradation and severely degradation, respectively.</p

Characteristics of all subjects.

<p>The data are presented as mean±S.E.</p><p>*<i>P</i> < 0.05</p><p>***<i>P</i> < 0.001 versus normal healthy controls.</p><p>BMI: body mass index; HbA1c: hemoglobin A1c; TG: triglyceride; Cr: creatinine; BUN: blood urea nitrogen; ACR, albumin to creatinine ratio; H-CRP: high-sensitive C-reactive protein. ACEI: angiotensin-converting enzyme inhibitor; ARB: angiotensin Ⅱ receptor antagonist</p><p>Characteristics of all subjects.</p

Protein contents of IL-6, IL-18, and TTP in urine and serum samples.

<p>Samples from urine (A) and serum (B) of patients from the normal (n = 41), diabetic without proteinuria (n = 33), diabetic with microalbuminuria (n = 29), and diabetic with clinical proteinuria (n = 25) groups were analyzed by ELISA. The data are presented as mean±S.E. *<i>P</i>< 0.05, **<i>P</i> < 0.01 compared with normal healthy controls, ^## P<0.01 compared with diabetic without proteinuria, and ^△△P<0.01 compared with diabetic with microalbuminuria.</p

The Plant Diversity of Different Degradation Grasslands.

<p>Note: ND, LD, MD, HD, SD represent non-degradation, light degradation, moderate degradation, heavy degradation and severely degradation, respectively.</p