Correlation Between Serum 1,25(OH)2D3, Serum Phosphorus, and Parathyroid Hormone and Parathyroid Function After Central Lymph Node Dissection in Patients with Papillary Thyroid Carcinoma.

This work was to demonstrate the relationship between serum 1,25-dihydroxyvitamin D3 (1,25(OH)2D3), serum phosphorus (SP), and parathyroid hormone (PTH) and parathyroid function after central lymph node dissection (CLND) in patients with papillary thyroid carcinoma (PTC). 200 PTC patients after CLND were included, who were rolled into a control group (CG) (n = 89 cases without hypoparathyroidism) and an observation group (OG) (n = 111 cases with complicated hypoparathyroidism). The 1,25(OH)2D3, SP, and PTH levels were detected, and the diagnostic effect of these indicators was assessed. The serum PTH levels of patients in CG after surgery were normal relative to those before surgery, while the serum PTH of patients in OG was relatively lower. 1,25(OH)2D3 concentration of patients in OG was also inferior to CG, while the SP level was superior (P < 0.05). Hypoparathyroidism was positively correlated with serum PTH (r = 0.382) and 1,25(OH)2D3 (r = 0.321) and negatively correlated with SP (r = - 0.211). The area under the curve (AUC) (0.893), sensitivity (90.83%), and specificity (94.77%) of the joint diagnosis of 1,25(OH)2D3 + SP + PTH were greatly superior to those of the single diagnosis and the pairwise diagnosis with the three indicators (P < 0.05). Hypoparathyroidism in patients with PTC after CLND surgery was positively correlated with 1,25(OH)2D3 and PTH and negatively correlated with SP concentration. In addition, the combination diagnosis of 1,25(OH)2D3, PTH, and SP worked well.

Efficient Electrosynthesis of Hydrogen Peroxide Using Oxygen-Doped Porous Carbon Catalysts at Industrial Current Densities.

Metal-free carbon catalysts (MFCCs) are one of the commonly used catalysts for electrocatalytic two-electron oxygen reduction (2e- ORR) synthesis of hydrogen peroxide (H2O2). Oxygen doping is an effective means to improve the performance of MFCCs, but the performance of oxygen-doped carbon catalysts is still not high enough, and the contribution of different oxygen functional groups (OFGs) to the catalytic performance is still inconclusive. In this paper, carbon-based catalysts with different oxygen contents and ratios of OFGs were prepared, and the high 2e- ORR activity of COOH + C-OH was demonstrated by combining the results of experiments and theoretical calculations. The prepared oxygen-doped carbon-based catalyst C-0.1M80 achieved an onset potential of 0.795 V (vs RHE), a selectivity of up to 98.2% (0.6 V vs RHE), and a H2O2 oxidation current of 1.33 mA cm-2 (0.5 V vs RHE) in a rotating ring-disk electrode test (0.1 M KOH solution), which was an outstanding performance in MFCCs. In a solid electrolyte flow cell, C-0.1M80 achieved a Faraday efficiency of 97.5% at 200 mA cm-2 with a corresponding H2O2 production rate of 123.7 mg cm-2 h-1. In addition, a flow cell stability test was performed at an industrial current density (100 mA cm-2) with an astounding 200 h of uninterrupted operation, also achieving an outstanding average Faradaic efficiency (95.8%).

UV-Triggered Hydrogel Coating of the Double Network Polyelectrolytes for Enhanced Endothelialization.

The left atrial appendage (LAA) occluder is an important medical device for closing the LAA and preventing stroke. The device-related thrombus (DRT) prevents the implantation of the occluder in exerting the desired therapeutic effect, which is primarily caused by the delayed endothelialization of the occluder. Functional coatings are an effective strategy for accelerating the endothelialization of occluders. However, the occluder surface area is particularly large and structurally complex, and the device is subjected to a large shear friction in the sheath during implantation, which poses a significant challenge to the coating. Herein, a hydrogel coating by the in situ UV-triggered polymerization of double-network polyelectrolytes is reported. The findings reveal that the double network and electrostatic interactions between the networks resulted in excellent mechanical properties of the hydrogel coating. The sulfonate and Arg-Gly-Asp (RGD) groups in the coating promoted hemocompatibility and endothelial growth of the occluder, respectively. The coating significantly accelerated the endothelialization of the LAA occluder in a canine model is further demonstrated. This study has potential clinical benefits in reducing both the incidence of DRT and the postoperative anticoagulant course for LAA closure.

The impact of cognitive behavioural therapy-based psychological intervention on emotional improvement in elderly patients with extensive burns.

To evaluate the efficacy of cognitive behavioural therapy (CBT) as a psychological intervention for elderly patients with extensive burns, focusing on its impact on emotional well-being, self-efficacy and quality of life. A prospective, randomized study involving 200 elderly burn patients was conducted from November 2021 to January 2023. The patients were randomly assigned to receive either standard care (control group) or burn care based on cognitive behavioural therapy (CBT-B) (study group), with 100 patients in each group. Outcome measures included the Visual Analog Scale (VAS) for pain assessment, 36-item Short Form Survey (SF-36) for quality of life, General Self-Efficacy Scale (GSES) and Rosenberg Self-Esteem Scale (RSES). The study revealed that CBT-based intervention significantly reduced anxiety and depression scores compared with standard care (p < 0.05). Additionally, patients in the CBT group exhibited improved self-efficacy, self-esteem and quality of life (p < 0.05). CBT proves to be a valuable intervention for elderly burn patients, effectively addressing emotional distress and enhancing their psychological well-being. By modifying negative cognitive patterns, providing coping mechanisms and fostering problem-solving skills, CBT-based care contributes to a more positive recovery experience and improved quality of life.

Advanced Cognitive Patterns in Multiple System Atrophy Compared to Parkinson's Disease: An Individual Diffusion Tensor Imaging Study.

RATIONALE AND OBJECTIVES: Although both Multiple system atrophy (MSA) and Parkinson's disease (PD) belong to alpha-synucleinopathy, they have divergent clinical courses and prognoses. The degeneration of white matter has a considerable impact on cognitive performance, yet it is uncertain how PD and MSA affect its functioning in a similar or different manner. METHODS: In this study, a total of 116 individuals (37 PD with mild cognitive impairment (PD-MCI), 37 MSA (parkinsonian variant) with mild cognitive impairment (MSA-MCI), and 42 healthy controls) underwent diffusion tensor imaging (DTI) and cognitive assessment. Utilizing probabilistic fiber tracking, association fibers, projection fibers, and thalamic fibers were reconstructed. Subsequently, regression, support vector machine, and SHAP (Shapley Addictive exPlanations) analyzes were conducted to evaluate the association between microstructural diffusion metrics and multiple cognitive domains, thus determining the white matter predictors of MCI. RESULTS: MSA-MCI patients exhibited distinct white matter impairment extending to the middle cerebellar peduncle, corticospinal tract, and cingulum bundle. Furthermore, the fractional anisotropy (FA) and mean diffusivity (MD)values of the right anterior thalamic radiation were significantly associated with global efficiency (FA: B = 0.69, P < 0.001, VIF = 1.31; MD: B = -0.53, P = 0.02, VIF = 2.50). The diffusion metrics of white matter between PD-MCI and MSA-MCI proved to be an effective predictor of the MCI, with an accuracy of 0.73 (P < 0.01), and the most predictive factor being the MD of the anterior thalamic radiation. CONCLUSIONS: Our results demonstrated that MSA-MCI had a more noticeable deterioration in white matter, which potentially linked to various cognitive domain connections. Diffusion MRI could be a useful tool in comprehending the neurological basis of cognitive impairment in Parkinsonian disorders.

Recent advances and clinical translation of liposomal delivery systems in cancer therapy.

The limitations of conventional cancer treatment are driving the emergence and development of nanomedicines. Research in liposomal nanomedicine for cancer therapy is rapidly increasing, opening up new horizons for cancer treatment. Liposomal nanomedicine, which focuses on targeted drug delivery to improve the therapeutic effect of cancer while reducing damage to normal tissues and cells, has great potential in the field of cancer therapy. This review aims to clarify the advantages of liposomal delivery systems in cancer therapy. We describe the recent understanding of spatiotemporal fate of liposomes in the organism after different routes of drug administration. Meanwhile, various types of liposome-based drug delivery systems that exert their respective advantages in cancer therapy while reducing side effects were discussed. Moreover, the combination of liposomal agents with other therapies (such as photodynamic therapy and photothermal therapy) has demonstrated enhanced tumor-targeting efficiency and therapeutic efficacy. Finally, the opportunities and challenges faced by the field of liposome nanoformulations for entering the clinical treatment of cancer are highlighted.

Model-based analysis of the incidence trends and transmission dynamics of COVID-19 associated with the Omicron variant in representative cities in China.

BACKGROUND: In 2022, Omicron outbreaks occurred at multiple sites in China. It is of great importance to track the incidence trends and transmission dynamics of coronavirus disease 2019 (COVID-19) to guide further interventions. METHODS: Given the population size, economic level and transport level similarities, two groups of outbreaks (Shanghai vs. Chengdu and Sanya vs. Beihai) were selected for analysis. We developed the SEAIQRD, ARIMA, and LSTM models to seek optimal modeling techniques for waves associated with the Omicron variant regarding data predictive performance and mechanism transmission dynamics, respectively. In addition, we quantitatively modeled the impacts of different combinations of more stringent interventions on the course of the epidemic through scenario analyses. RESULTS: The best-performing LSTM model showed better prediction accuracy than the best-performing SEAIQRD and ARIMA models in most cases studied. The SEAIQRD model had an absolute advantage in exploring the transmission dynamics of the outbreaks. Regardless of the time to inflection point or the time to Rt curve below 1.0, Shanghai was later than Chengdu (day 46 vs. day 12/day 54 vs. day 14), and Sanya was later than Beihai (day 16 vs. day 12/day 20 vs. day 16). Regardless of the number of peak cases or the cumulative number of infections, Shanghai was higher than Chengdu (34,350 vs. 188/623,870 vs. 2,181), and Sanya was higher than Beihai (1,105 vs. 203/16,289 vs. 3,184). Scenario analyses suggested that upgrading control level in advance, while increasing the index decline rate and quarantine rate, were of great significance for shortening the time to peak and Rt below 1.0, as well as reducing the number of peak cases and final affected population. CONCLUSIONS: The LSTM model has great potential for predicting the prevalence of Omicron outbreaks, whereas the SEAIQRD model is highly effective in revealing their internal transmission mechanisms. We recommended the use of joint interventions to contain the spread of the virus.

Multi-parametric radiomics of conventional T1 weighted and susceptibility-weighted imaging for differential diagnosis of idiopathic Parkinson's disease and multiple system atrophy.

OBJECTIVES: This study aims to investigate the potential of radiomics with multiple parameters from conventional T1 weighted imaging (T1WI) and susceptibility weighted imaging (SWI) in distinguishing between idiopathic Parkinson's disease (PD) and multiple system atrophy (MSA). METHODS: A total of 201 participants, including 57 patients with PD, 74 with MSA, and 70 healthy control (HCs) individuals, underwent T1WI and SWI scans. From the 12 subcortical nuclei (e.g. red nucleus, substantia nigra, subthalamic nucleus, putamen, globus pallidus, and caudate nucleus), 2640 radiomic features were extracted from both T1WI and SWI scans. Three classification models - logistic regression (LR), support vector machine (SVM), and light gradient boosting machine (LGBM) - were used to distinguish between MSA and PD, as well as among MSA, PD, and HC. These classifications were based on features extracted from T1WI, SWI, and a combination of T1WI and SWI. Five-fold cross-validation was used to evaluate the performance of the models with metrics such as sensitivity, specificity, accuracy, and area under the receiver operating curve (AUC). During each fold, the ANOVA and least absolute shrinkage and selection operator (LASSO) methods were used to identify the most relevant subset of features for the model training process. RESULTS: The LGBM model trained by the features combination of T1WI and SWI exhibited the most outstanding differential performance in both the three-class classification task of MSA vs. PD vs. HC and the binary classification task of MSA vs. PD, with an accuracy of 0.814 and 0.854, and an AUC of 0.904 and 0.881, respectively. The texture-based differences (GLCM) of the SN and the shape-based differences of the GP were highly effective in discriminating between the three classes and two classes, respectively. CONCLUSIONS: Radiomic features combining T1WI and SWI can achieve a satisfactory differential diagnosis for PD, MSA, and HC groups, as well as for PD and MSA groups, thus providing a useful tool for clinical decision-making based on routine MRI sequences.

Prediction of Parkinson's Disease Using Machine Learning Methods.

The detection of Parkinson's disease (PD) in its early stages is of great importance for its treatment and management, but consensus is lacking on what information is necessary and what models should be used to best predict PD risk. In our study, we first grouped PD-associated factors based on their cost and accessibility, and then gradually incorporated them into risk predictions, which were built using eight commonly used machine learning models to allow for comprehensive assessment. Finally, the Shapley Additive Explanations (SHAP) method was used to investigate the contributions of each factor. We found that models built with demographic variables, hospital admission examinations, clinical assessment, and polygenic risk score achieved the best prediction performance, and the inclusion of invasive biomarkers could not further enhance its accuracy. Among the eight machine learning models considered, penalized logistic regression and XGBoost were the most accurate algorithms for assessing PD risk, with penalized logistic regression achieving an area under the curve of 0.94 and a Brier score of 0.08. Olfactory function and polygenic risk scores were the most important predictors for PD risk. Our research has offered a practical framework for PD risk assessment, where necessary information and efficient machine learning tools were highlighted.

Effects of total flavones of Abelmoschus manihot (L.) on the treatment of diabetic nephropathy via the activation of solute carriers in renal tubular epithelial cells.

As a traditional Chinese medicine, Huangkui capsule (HKC) has been used to treat patients with kidney diseases, including diabetic nephropathy (DN). We have recently demonstrated that HKC could re-regulate the activities of solute carriers (SLC)s in proximal and distal convoluted tubules of kidneys in regression of the development of DN. The main active chemical constituents of HKC are the flavones of Abelmoschus manihot (L.). The current study aims to further evaluate the efficacy of total flavones of A. manihot (TFA) in the regression of DN by analyzing SLC activities in proximal and distal convoluted tubules of kidneys. TFA (0.076 g/kg/d) or vehicle was administered in db/db mice, the animal model of type 2 diabetes and DN, daily via oral gavage for four weeks. Blood glucose levels and urinary albumin-to-creatinine ratio (UACR) were measured and used for the determination of T2D and DN. Ten SLCs, including slc2a2, slc4A1, slc5a2, slc5A3, slc5a8, slc6a20, slc27a2, slc12a3, slc34a1 and slc38a2 were highly expressed in proximal and distinct convoluted tubules of kidneys. Their expression at mRNA and protein levels before and after TFA treatment were analyzed with real-time RT-PCR and immunohistochemistry. Data showed that UACR in the db/db mice after TFA treatment was significantly decreased. Compared with the group of non-diabetic control, slc2a2, slc4A1, slc5a2, slc5A3, slc5a8, slc6a20, slc27a2, slc12a3, slc34a1 and slc38a2 in the group of DN were down-regulated but up-regulated after TFA treatment. Further analyses of whole kidney sections indicated that the numbers and structures of the nephron in db/db mice was increased and improved after TFA treatment. Thereby, the current study provides further evidence that the flavones in A. manihot have pharmacological effects on the treatment of DN by improving the biological function of SLCs in kidneys.

Public responses to urban heat and payment for heat-resilient infrastructure: implications for heat action plan formulation.

Heat action plans should be urgently formulated to enable urban managers, planners, and designers to take appropriate actions for mitigation and adaptation. However, few studies have been conducted to investigate the societal needs and knowledge gaps regarding heat mitigation and adaptation. To address such research gaps, this paper presents an empirical study of 574 questionnaires in Chengdu, China, to explore heat-related impacts, public responses, and driving mechanisms. The results indicated that outdoor activities and work/study were more sensitive to extreme heat than transportation, sleep/rest, and diet. Heat-related impacts on physiological health were at the same level as those on psychological health, where digestive system illness and emotional irritability were the most prevalent physiological and psychological symptoms. Respondents' knowledge of heat-related threats, adaptation awareness, and adaptation knowledge were insufficient, compared with heat severity. The payment willingness among the respondents was not strong and payment amount was not high. Poorer, healthier, and the less affected in outdoor activities were positive groups in payment willingness, while the group which experienced heat-related impacts on outdoor activities could pay more compared with other groups. Overall, these results help to shape the main contents of heat action plans.

Cognitive status and its risk factors in patients with hypertension and diabetes in a low-income rural area of China: A cross-sectional study.

OBJECTIVES: The proportion of older people with dementia in China is gradually increasing with the increase in the aging population over recent years. Hypertension and diabetes are common non-communicable diseases among rural populations in China. However, it remains unclear whether these conditions affect the occurrence and development of cognitive impairment as there is limited research on cognitive status and its risk factors among residents of rural areas. METHODS: A multi-stage stratified cluster random sampling method was used to select 5400 participants from rural permanent residents. A self-designed structured questionnaire was used to investigate demographic data of the participants. Cognitive function was assessed using the Montreal Cognitive Function Assessment Scale (MoCA). The results were analyzed using chi-square test, ANOVA and multiple linear regression analysis. RESULTS: A total of 5028 participants returned the survey, giving a response rate of 93.1%. Higher education (odds ratio (OR) = 3.2, 95% confidence interval (CI) 2.87-3.54, p < 0.001), higher income (OR = 1.61, 95% CI 1.16-2.07, p < 0.001), and dietary control (OR = 0.66, 95%CI 0.34-0.98, p < 0.001) were protective factors. A visual representation of the relationship between annual income and MoCA score showed an inverted U-curve, the group with an annual income of 6000-7999 RMB had a maximum OR of 1.93 (95%CI 0.12-2.74, p < 0.001). While difficulty in maintaining sleep were risk factors for cognitive impairment (OR = -2.28, 95% CI-4.18-0.39, p = 0.018). CONCLUSIONS: Participants with middle incomes had better cognitive status than those with the highest incomes. Higher education, proper diet control and good sleep are beneficial to the cognitive status of residents in rural areas.

Enhancing Solubility and Dissolution Rate of Antifungal Drug Ketoconazole through Crystal Engineering.

To improve the solubility and dissolution rate of the BCS class II drug ketoconazole, five novel solid forms in 1:1 stoichiometry were obtained upon liquid-assisted grinding, slurry, and slow evaporation methods in the presence of coformers, namely, glutaric, vanillic, 2,6-dihydroxybenzoic, protocatechuic, and 3,5-dinitrobenzoic acids. Single-crystal X-ray diffraction analysis revealed that the hydroxyl/carboxylic acid. . .N-imidazole motif acts as the dominant supramolecular interaction in the obtained solid forms. The solubility of ketoconazole in distilled water significantly increased from 1.2 to 2165.6, 321.6, 139.1, 386.3, and 191.7 µg mL-1 in the synthesized multi-component forms with glutaric, vanillic, 2,6-dihydroxybenzoic, protocatechuic, and 3,5-dinitrobenzoic acid, respectively. In particular, the cocrystal form with glutaric acid showed an 1800-fold solubility increase in water concerning ketoconazole. Our study provides an alternative approach to improve the solubility and modify the release profile of poorly water-soluble drugs such as ketoconazole.

Structural and Functional Alterations of Motor-Thalamus in Different Motor Subtype of Parkinson's Disease: An Individual Study.

RATIONALE AND OBJECTIVES: This study aimed to investigate the structural and functional alterations occurring within bilateral premotor thalamus (mPMtha) in motor subtypes of Parkinson's disease (PD). MATERIALS AND METHODS: Sixty-one individuals with instability and gait difficulty (PIGD) subtype, 60 individuals with tremor-dominant (TD) subtype and 66 healthy controls (HCs) participated in the study. All participants underwent resting-state functional magnetic resonance imaging (rs-fMRI) and 3D T1-weighted (3DT1) scans. Functional connectivity (FC) analysis and Voxel-based morphometry (VBM) analysis were performed to evaluate the function and volume of mPMtha. Additionally, correlations between motor performance and FC values, volumes were examined separately. Support vector machine (SVM) model based on FC values and thalamic volumes was conducted to assist in the clinical diagnosis of PD motor subtype. RESULTS: Compared to HCs and PIGD, TD subtype showed increased FC between the bilateral mPMtha and left middle occipital gyrus, left inferior parietal lobule (IPL). While PIGD subtype demonstrated decreased FC between right mPMtha and precentral gyrus (PreCG), supramarginal, IPL and superior parietal lobule. FC of bilateral mPMtha with the identified regions were significantly correlated with motor performance scores in PD patients. The SVM classification based on FC values demonstrated a high level of efficiency (AUC=0.874). The volumes of the bilateral mPMtha were indifferent among three groups. CONCLUSION: We noted distinct FC alterations of mPMtha in TD and PIGD subtypes, and these changes were correlated with motor performance. Furthermore, the machine learning based on statistically significant FC might be served as an alternative approach for automatically classifying PD motor subtypes individually.

Screening strategies and dynamic risk prediction models for Alzheimer's disease.

BACKGROUND: Characterizing the progression from Mild cognitive impairment (MCI) to Alzheimer's disease (AD) is essential for early AD prevention and targeted intervention. Our goal was to construct precise screening schemes for individuals with different risk of AD and to establish prognosis models for them. METHODS: We constructed a retrospective cohort by reviewing individuals with baseline diagnosis of MCI and at least one follow-up visits between November 2005 and May 2021. They were stratified into high-risk and low-risk groups with longitudinal cognitive trajectory. Then, we established a screening framework and obtained optimal screening strategies for two risk groups. Cox and random survival forest (RSF) models were developed for dynamic prognosis prediction. RESULTS: In terms of screening strategies, the combination of Clinical Dementia Rating Sum of Boxes (CDRSB) and hippocampus volume was recommended for the high-risk MCI group, while the combination of Alzheimer's Disease Assessment Scale Cognitive 13 items (ADAS13) and FAQ was recommended for low-risk MCI group. The concordance index (C-index) of the Cox model for the high-risk group was 0.844 (95% CI: 0.815-0.873) and adjustments for demographic information and APOE ε4. The RSF model incorporating longitudinal ADAS13, FAQ, and demographic information and APOE ε4 performed for the low-risk group. CONCLUSION: This precise screening scheme will optimize allocation of medical resources and reduce the economic burden on individuals with low risk of MCI. Moreover, dynamic prognosis models may be helpful for early identification of individuals at risk and clinical decisions, which will promote the secondary prevention of AD.

Differential Diagnosis of Intracranial Malignant Tumors Using MRI Based on Morphological Features and Signal Intensity Ratio of Lesions.

Objective: To investigate the clinical value of conventional MRI morphological features and signal intensity ratio in the differential diagnosis of intracranial malignant tumors (high-grade glioma (HGG), primary central nervous system Lymphoma (PCNSL) and single brain metastasis (BM). Methods: Retrospective analysis of 92 cases of HGG, 27 cases of PCNSL, and 35 cases of BM. MRI data in The General Hospital of Western Theater Command from August 2014 to December 2021, comparative analysis of morphological characteristics of tumors and lesion/normal brain parenchyma signal ratio (lesiontonormal parenchymaratio, LNR), five indexes were included T1WI signal ratio (LNRT1), T2WI signal intensity ratio (LNRT2), T2WI/T1WI signal ratio (LNRT2/T1), T1WI enhanced signal ratio (LNRT1CE) and contrast enhancement ratio (CER). The differential diagnostic performance was also assessed by subject operating characteristic (ROC) curves. Results: HGG, PCNSL, and BM were all seen more frequently in the supratentorial region, More than 50% of HGG mainly showed irregular morphology, intratumoral necrosis, cystic degeneration, peritumoral severe edema, cyclic uneven enhancement after enhancement, PCNSL significantly enhanced the main uniformity, necrosis cyst became rare, BM group showed uneven enhancement, no obvious specificity, and the differences in tumor morphology, peritumor edema, intratumor hemorrhage, necrotic cystic lesions, and enhancement patterns were statistically significant among the three (P < .05). PCNSL LNRT1 and its LNRT1CE (LNRT1: 0.558 ± 0.050, LNRT1CE: 1.637 ± 0.125) were significantly higher than those of HGG (LNRT1: 0.480 ± 0.077, LNRT1CE: 1.425 ± 0.160) and BM (LNRT1: 0.514 ± 0.120, LNRT1CE: 1.375 ± 0.122), while LNRT2 and LNRT2/T1 (LNRT2: 1.389 ± 0.086, LNRT2/T1: 2.511 ± 0.295) were significantly lower than those of HGG (LNRT2: 1.527 ± 0.191, LNRT2/T1: 3.263 ± 0.657), and BM (LNRT2: 1.504 ± 0.089, LNRT2/T1: 3.103 ± 0.830). There was no significant difference in CER among the three groups (P > .05). ROC curve analysis of LNRT1, LNRT2, LNRT1CE, and LNRT2/T1 could be used to discriminate PCNSL from HGG and BM, with LNRT1CE having the largest area under the curve of 0.873, sensitivity of 0.963 and specificity of 0.669. Conclusion: MRI lesion morphological features and signal intensity ratio are important for discriminating HGG from PCNSL and BM. As a quantitative parameter, tumor signal intensity ratio can provide an important supplement for subjective judgment, to improve the accuracy of tumor qualitative diagnosis and differential diagnosis.

Segmental disturbance of white matter microstructure in predicting mild cognitive impairment in idiopathic Parkinson's disease: An individualized study based on automated fiber quantification tractography.

PURPOSE: The neurobiological mechanisms and an early identification of MCI in idiopathic Parkinson's disease (IPD) remain unclear. To investigate the abnormalities of types of white matter (WM) fiber tracts segmentally and establish reliable indicator in IPD-MCI. METHODS: Forty IPD with normal cognition (IPD-NCI), thirty IPD-MCI, and thirty healthy controls were included. Automated fiber quantification was applied to extract the fractional anisotropy (FA) and mean diffusivity (MD) values at 100 locations along the major fibers. Partial correlation was performed between diffusion values and cognitive performance. Furthermore, machine learning analyses were conducted to determine the imaging biomarker of MCI. Permutation tests were performed to evaluate the pointwise differences under the FWE correction. RESULTS: IPD-MCI had similar but more severe and widespread WM degeneration in the association, projection, and commissural fibers compared with IPD-NCI. Meanwhile, IPD-MCI showed distinct degeneration pattern in the association fibers. The FA of the anterior segment of right inferior fronto-occipital fasciculus (IFOF) was positively correlated with MoCA (P < 0.05) and executive function (P < 0.05). The MD of the middle and posterior segment of left superior longitudinal fasciculus (SLF) was negatively correlated with MoCA P < 0.05), executive (P < 0.05), visuospatial function (P < 0.05). Furthermore, the AUC of support vector machine model was 0.80 in the validation dataset. The FA of anterior segment of right IFOF contribute the most. CONCLUSION: This study demonstrated that regional tract-specific microstructural degeneration, especially the association fibers, can be used to predict MCI in IPD. Especially, the right IFOF may be a significant imaging biomarker in predicting IPD with MCI.

Evaluation of the efficacy of Abelmoschus manihot (L.) on diabetic nephropathy by analyzing biomarkers in the glomeruli and proximal and distal convoluted tubules of the kidneys.

Introduction: As a traditional Chinese medicine, Abelmoschus manihot (L.) in the form of Huangkui (HK) capsule has been used as a medication for kidney diseases, including diabetic nephropathy (DN), in China. The most significant effect of HK capsule treatment in kidney diseases is the reduction of albuminuria and proteinuria. To evaluate the efficacy of HK capsule in the regression of DN, in the current study, we analyzed the biomarkers in the glomerulus and proximal and distal convoluted tubules in the kidneys of db/db mice, the animal model for type 2 diabetes and DN. Methods: Huangkui capsules (0.84 g/kg/d) or vehicle were administered daily via oral gavage for 4 weeks in db/db mice. Urinary albumin-to-creatinine ratio and blood glucose levels were measured during the whole experimental period. Five biomarkers in the glomerulus and proximal and distal convoluted tubules in the kidneys were selected, namely, col4a3, slc5a2, slc34a1, slc12a3, and slc4a1, and their activities at mRNA and protein levels before and after HK capsule treatment were analyzed by real-time RT-PCR and immunohistochemistry. Result and discussion: After HK capsule treatment for 4 weeks, the urinary albumin-to-creatinine ratio in db/db mice was found to be significantly decreased. The activities of col4a3, slc5a2, slc34a1, slc12a3, and slc4a1 in the kidneys were upregulated in db/db mice prior to the treatment but downregulated after HK capsule treatment. Further analyses of the fields of whole kidney tissue sections demonstrated that the number of nephrons in the kidneys of db/db mice with HK capsule treatment was higher than that in the kidneys of db/db mice without HK capsule treatment. Thereby, the current study provides experimental evidence confirming the medical efficacy of A. manihot in the reduction of albuminuria and proteinuria, suggesting that A. manihot may have pharmacological efficacy in the regression of the development of type 2 diabetes-DN.

Shared genetics and causal relationships between major depressive disorder and COVID-19 related traits: a large-scale genome-wide cross-trait meta-analysis.

Introduction: The comorbidity between major depressive disorder (MDD) and coronavirus disease of 2019 (COVID-19) related traits have long been identified in clinical settings, but their shared genetic foundation and causal relationships are unknown. Here, we investigated the genetic mechanisms behind COVID-19 related traits and MDD using the cross-trait meta-analysis, and evaluated the underlying causal relationships between MDD and 3 different COVID-19 outcomes (severe COVID-19, hospitalized COVID-19, and COVID-19 infection). Methods: In this study, we conducted a comprehensive analysis using the most up-to-date and publicly available GWAS summary statistics to explore shared genetic etiology and the causality between MDD and COVID-19 outcomes. We first used genome-wide cross-trait meta-analysis to identify the pleiotropic genomic SNPs and the genes shared by MDD and COVID-19 outcomes, and then explore the potential bidirectional causal relationships between MDD and COVID-19 outcomes by implementing a bidirectional MR study design. We further conducted functional annotations analyses to obtain biological insight for shared genes from the results of cross-trait meta-analysis. Results: We have identified 71 SNPs located on 25 different genes are shared between MDD and COVID-19 outcomes. We have also found that genetic liability to MDD is a causal factor for COVID-19 outcomes. In particular, we found that MDD has causal effect on severe COVID-19 (OR = 1.832, 95% CI = 1.037-3.236) and hospitalized COVID-19 (OR = 1.412, 95% CI = 1.021-1.953). Functional analysis suggested that the shared genes are enriched in Cushing syndrome, neuroactive ligand-receptor interaction. Discussion: Our findings provide convincing evidence on shared genetic etiology and causal relationships between MDD and COVID-19 outcomes, which is crucial to prevention, and therapeutic treatment of MDD and COVID-19.