DataSheet_1_The impact of COVID lockdown on glycaemic control in paediatric patients with type 1 diabetes: A systematic review and meta-analysis of 22 observational studies.pdf

IntroductionThe COVID lockdown has posted a great challenge to paediatric patients with type 1 diabetes (T1D) and their caregivers on the disease management. This systematic review and meta-analysis sought to compare the glycaemic control among paediatric patients with T1D (aged under 18 years) pre- during, and post-lockdown period.Methods and materialsWe did a systematic search of three databases (PubMed, Embase, and the WHO COVID‐19 Global literature) for the literature published between 1 Jan 2019 to 10 Sep 2022. Studies meeting the following inclusion criteria were eligible for this study: (1) a COVID-19 related study; (2) inclusion of children aged 18 years old or under with established T1D; (3) comparing the outcomes of interest during or after the COVID lockdown with that before the lockdown. Study endpoints included mean difference (MD) in HbA1c, blood glucose, time in range (TIR, 70-180 mg/dl), time above range (TAR, >180mg/dl), time below range (TBR,ResultsInitial search identified 4488 records and 22 studies with 2106 paediatric patients with T1D were included in the final analysis. Compared with pre-lockdown period, blood glucose was significantly decreased by 0.11 mmol/L (95%CI: -0.18, -0.04) during lockdown period and by 0.42 mmol/L (95%CI: -0.73, -0.11) after lockdown. The improvement was also found for TIR, TAR, TBR, and CV during and post-lockdown (all p valuesConclusionCompared with pre-lockdown period, there was significant improvement in T1D paediatric patients’ glucose metrics during and post-lockdown. The underlying reasons for this positive impact warrant further investigation to inform future paediatric diabetes management.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022359213.</p

Image_3_Survival nomogram for different grades of gastric cancer patients based on SEER database and external validation cohort.tif

BackgroundInfluencing factors varied among gastric cancer (GC) for different differentiation grades which affect the prognosis accordingly. This study aimed to develop a nomogram to effectively identify the overall survival (OS).MethodsTotally, 9,568 patients with GC were obtained from the SEER database as the training cohort and internal validation cohort. We then retrospectively enrolled patients diagnosed with GC to construct the external validation cohort from the First Affiliated Hospital of Anhui Medical University. The prognostic factors were integrated into the multivariate Cox regression to construct a nomogram. To test the accuracy of the model, we used the calibration curves, receiver operating characteristics (ROC) curves, C-index, and decision curve analysis (DCA).ResultsRace chemotherapy, tumor size, and other four factors were significantly associated with the prognosis of Grade III GC Patients. On this basis, we developed a nomogram. The discrimination of the nomogram revealed good prognostic accuracy The results of the area under the curve (AUC) calculated by ROC for five-year survival were 0.828 and 0.758 in the training set and external validation cohort, higher than that of the TNM staging system. The calibration plot revealed that the estimated risk was close to the actual risk. DCA also suggested an excellent predictive value of the nomogram. Similar results were obtained in Grade-I and Grade-II GC patients.ConclusionsThe nomogram developed in this study and other findings could help individualize the treatment of GC patients and assist clinicians in their shared decision-making with patients.</p

Table_1_Survival nomogram for different grades of gastric cancer patients based on SEER database and external validation cohort.docx

BackgroundInfluencing factors varied among gastric cancer (GC) for different differentiation grades which affect the prognosis accordingly. This study aimed to develop a nomogram to effectively identify the overall survival (OS).MethodsTotally, 9,568 patients with GC were obtained from the SEER database as the training cohort and internal validation cohort. We then retrospectively enrolled patients diagnosed with GC to construct the external validation cohort from the First Affiliated Hospital of Anhui Medical University. The prognostic factors were integrated into the multivariate Cox regression to construct a nomogram. To test the accuracy of the model, we used the calibration curves, receiver operating characteristics (ROC) curves, C-index, and decision curve analysis (DCA).ResultsRace chemotherapy, tumor size, and other four factors were significantly associated with the prognosis of Grade III GC Patients. On this basis, we developed a nomogram. The discrimination of the nomogram revealed good prognostic accuracy The results of the area under the curve (AUC) calculated by ROC for five-year survival were 0.828 and 0.758 in the training set and external validation cohort, higher than that of the TNM staging system. The calibration plot revealed that the estimated risk was close to the actual risk. DCA also suggested an excellent predictive value of the nomogram. Similar results were obtained in Grade-I and Grade-II GC patients.ConclusionsThe nomogram developed in this study and other findings could help individualize the treatment of GC patients and assist clinicians in their shared decision-making with patients.</p

Image_1_Survival nomogram for different grades of gastric cancer patients based on SEER database and external validation cohort.tif

BackgroundInfluencing factors varied among gastric cancer (GC) for different differentiation grades which affect the prognosis accordingly. This study aimed to develop a nomogram to effectively identify the overall survival (OS).MethodsTotally, 9,568 patients with GC were obtained from the SEER database as the training cohort and internal validation cohort. We then retrospectively enrolled patients diagnosed with GC to construct the external validation cohort from the First Affiliated Hospital of Anhui Medical University. The prognostic factors were integrated into the multivariate Cox regression to construct a nomogram. To test the accuracy of the model, we used the calibration curves, receiver operating characteristics (ROC) curves, C-index, and decision curve analysis (DCA).ResultsRace chemotherapy, tumor size, and other four factors were significantly associated with the prognosis of Grade III GC Patients. On this basis, we developed a nomogram. The discrimination of the nomogram revealed good prognostic accuracy The results of the area under the curve (AUC) calculated by ROC for five-year survival were 0.828 and 0.758 in the training set and external validation cohort, higher than that of the TNM staging system. The calibration plot revealed that the estimated risk was close to the actual risk. DCA also suggested an excellent predictive value of the nomogram. Similar results were obtained in Grade-I and Grade-II GC patients.ConclusionsThe nomogram developed in this study and other findings could help individualize the treatment of GC patients and assist clinicians in their shared decision-making with patients.</p

Image_2_Survival nomogram for different grades of gastric cancer patients based on SEER database and external validation cohort.tif

BackgroundInfluencing factors varied among gastric cancer (GC) for different differentiation grades which affect the prognosis accordingly. This study aimed to develop a nomogram to effectively identify the overall survival (OS).MethodsTotally, 9,568 patients with GC were obtained from the SEER database as the training cohort and internal validation cohort. We then retrospectively enrolled patients diagnosed with GC to construct the external validation cohort from the First Affiliated Hospital of Anhui Medical University. The prognostic factors were integrated into the multivariate Cox regression to construct a nomogram. To test the accuracy of the model, we used the calibration curves, receiver operating characteristics (ROC) curves, C-index, and decision curve analysis (DCA).ResultsRace chemotherapy, tumor size, and other four factors were significantly associated with the prognosis of Grade III GC Patients. On this basis, we developed a nomogram. The discrimination of the nomogram revealed good prognostic accuracy The results of the area under the curve (AUC) calculated by ROC for five-year survival were 0.828 and 0.758 in the training set and external validation cohort, higher than that of the TNM staging system. The calibration plot revealed that the estimated risk was close to the actual risk. DCA also suggested an excellent predictive value of the nomogram. Similar results were obtained in Grade-I and Grade-II GC patients.ConclusionsThe nomogram developed in this study and other findings could help individualize the treatment of GC patients and assist clinicians in their shared decision-making with patients.</p

The complete mitochondrial genome of <i>Bauhinia variegata</i> (Leguminosae)

The mitogenome of Bauhinia variegate was assembled and characterized in this study. The mitogenome size was 437,271 bp, and its GC content was 45.5%. 36 protein-coding genes, 17 tRNAs and 3 rRNAs were annotated in the mitogenome. A total of 12 MTPTs, ranging from 71 bp to 3562 bp, were identified in the mitogenome and covered 1.46% (6373 bp) of the mitogenome. Phylogenetic analysis of 15 species of Leguminosae based on 23 core protein-coding genes showed that B. variegata was sister to Tylosema esculentum, another member from the subfamily Cercidoideae. The mitogenome of B. variegata provides a valuable genetic resource for further phylogenetic studies of this family.</p

Table_1_AMF colonization affects allelopathic effects of Zea mays L. root exudates and community structure of rhizosphere bacteria.xlsx

Arbuscular mycorrhizal fungi (AMF) widely exist in the soil ecosystem. It has been confirmed that AMF can affect the root exudates of the host, but the chain reaction effect of changes in the root exudates has not been reported much. The change of soil microorganisms and soil enzyme vigor is a direct response to the change in the soil environment. Root exudates are an important carbon source for soil microorganisms. AMF colonization affects root exudates, which is bound to have a certain impact on soil microorganisms. This manuscript measured and analyzed the changes in root exudates and allelopathic effects of root exudates of maize after AMF colonization, as well as the enzymatic vigor and bacterial diversity of maize rhizosphere soil. The results showed that after AMF colonization, the contents of 35 compounds in maize root exudates were significantly different. The root exudates of maize can inhibit the seed germination and seedling growth of recipient plants, and AMF colonization can alleviate this situation. After AMF colonization, the comprehensive allelopathy indexes of maize root exudates on the growth of radish, cucumber, lettuce, pepper, and ryegrass seedlings decreased by 60.99%, 70.19%, 80.83%, 36.26% and 57.15% respectively. The root exudates of maize inhibited the growth of the mycelia of the pathogens of soil-borne diseases, and AMF colonization can strengthen this situation. After AMF colonization, the activities of dehydrogenase, sucrase, cellulase, polyphenol oxidase and neutral protein in maize rhizosphere soil increased significantly, while the bacterial diversity decreased but the bacterial abundance increased. This research can provide a theoretical basis for AMF to improve the stubble of maize and the intercropping mode between maize and other plants, and can also provide a reference for AMF to prevent soil-borne diseases in maize.</p

Table_2_AMF colonization affects allelopathic effects of Zea mays L. root exudates and community structure of rhizosphere bacteria.xlsx

Arbuscular mycorrhizal fungi (AMF) widely exist in the soil ecosystem. It has been confirmed that AMF can affect the root exudates of the host, but the chain reaction effect of changes in the root exudates has not been reported much. The change of soil microorganisms and soil enzyme vigor is a direct response to the change in the soil environment. Root exudates are an important carbon source for soil microorganisms. AMF colonization affects root exudates, which is bound to have a certain impact on soil microorganisms. This manuscript measured and analyzed the changes in root exudates and allelopathic effects of root exudates of maize after AMF colonization, as well as the enzymatic vigor and bacterial diversity of maize rhizosphere soil. The results showed that after AMF colonization, the contents of 35 compounds in maize root exudates were significantly different. The root exudates of maize can inhibit the seed germination and seedling growth of recipient plants, and AMF colonization can alleviate this situation. After AMF colonization, the comprehensive allelopathy indexes of maize root exudates on the growth of radish, cucumber, lettuce, pepper, and ryegrass seedlings decreased by 60.99%, 70.19%, 80.83%, 36.26% and 57.15% respectively. The root exudates of maize inhibited the growth of the mycelia of the pathogens of soil-borne diseases, and AMF colonization can strengthen this situation. After AMF colonization, the activities of dehydrogenase, sucrase, cellulase, polyphenol oxidase and neutral protein in maize rhizosphere soil increased significantly, while the bacterial diversity decreased but the bacterial abundance increased. This research can provide a theoretical basis for AMF to improve the stubble of maize and the intercropping mode between maize and other plants, and can also provide a reference for AMF to prevent soil-borne diseases in maize.</p

Image_1_Incorporation of a machine learning pathological diagnosis algorithm into the thyroid ultrasound imaging data improves the diagnosis risk of malignant thyroid nodules.tif

ObjectiveThis study aimed at establishing a new model to predict malignant thyroid nodules using machine learning algorithms.MethodsA retrospective study was performed on 274 patients with thyroid nodules who underwent fine-needle aspiration (FNA) cytology or surgery from October 2018 to 2020 in Xianyang Central Hospital. The least absolute shrinkage and selection operator (lasso) regression analysis and logistic analysis were applied to screen and identified variables. Six machine learning algorithms, including Decision Tree (DT), Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Naive Bayes Classifier (NBC), Random Forest (RF), and Logistic Regression (LR), were employed and compared in constructing the predictive model, coupled with preoperative clinical characteristics and ultrasound features. Internal validation was performed by using 10-fold cross-validation. The performance of the model was measured by the area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, F1 score, Shapley additive explanations (SHAP) plot, feature importance, and correlation of features. The best cutoff value for risk stratification was identified by probability density function (PDF) and clinical utility curve (CUC).ResultsThe malignant rate of thyroid nodules in the study cohort was 53.2%. The predictive models are constructed by age, margin, shape, echogenic foci, echogenicity, and lymph nodes. The XGBoost model was significantly superior to any one of the machine learning models, with an AUC value of 0.829. According to the PDF and CUC, we recommended that 51% probability be used as a threshold for determining the risk stratification of malignant nodules, where about 85.6% of patients with malignant nodules could be detected. Meanwhile, approximately 89.8% of unnecessary biopsy procedures would be saved. Finally, an online web risk calculator has been built to estimate the personal likelihood of malignant thyroid nodules based on the best-performing ML-ed model of XGBoost.ConclusionsCombining clinical characteristics and features of ultrasound images, ML algorithms can achieve reliable prediction of malignant thyroid nodules. The online web risk calculator based on the XGBoost model can easily identify in real-time the probability of malignant thyroid nodules, which can assist clinicians to formulate individualized management strategies for patients.</p

Video3_Exercise improves choroid plexus epithelial cells metabolism to prevent glial cell-associated neurodegeneration.MP4

Recent studies have shown that physical activities can prevent aging-related neurodegeneration. Exercise improves the metabolic landscape of the body. However, the role of these differential metabolites in preventing neurovascular unit degeneration (NVU) is still unclear. Here, we performed single-cell analysis of brain tissue from young and old mice. Normalized mutual information (NMI) was used to measure heterogeneity between each pair of cells using the non-negative Matrix Factorization (NMF) method. Astrocytes and choroid plexus epithelial cells (CPC), two types of CNS glial cells, differed significantly in heterogeneity depending on their aging status and intercellular interactions. The MetaboAnalyst 5.0 database and the scMetabolism package were used to analyze and calculate the differential metabolic pathways associated with aging in the CPC. These mRNAs and corresponding proteins were involved in the metabolites (R)-3-Hydroxybutyric acid, 2-Hydroxyglutarate, 2-Ketobutyric acid, 3-Hydroxyanthranilic acid, Fumaric acid, L-Leucine, and Oxidized glutathione pathways in CPC. Our results showed that CPC age heterogeneity-associated proteins (ECHS1, GSTT1, HSD17B10, LDHA, and LDHB) might be directly targeted by the metabolite of oxidized glutathione (GSSG). Further molecular dynamics and free-energy simulations confirmed the insight into GSSG’s targeting function and free-energy barrier on these CPC age heterogeneity-associated proteins. By inhibiting these proteins in CPC, GSSG inhibits brain energy metabolism, whereas exercise improves the metabolic pathway activity of CPC in NVU by regulating GSSG homeostasis. In order to develop drugs targeting neurodegenerative diseases, further studies are needed to understand how physical exercise enhances NVU function and metabolism by modulating CPC-glial cell interactions.</p