Machine-Learning and Radiomics-Based Preoperative Prediction of Ki-67 Expression in Glioma Using MRI Data.

BACKGROUND: Gliomas are the most common primary brain tumours and constitute approximately half of all malignant glioblastomas. Unfortunately, patients diagnosed with malignant glioblastomas typically survive for less than a year. In light of this circumstance, genotyping is an effective means of categorising gliomas. The Ki67 proliferation index, a widely used marker of cellular proliferation in clinical contexts, has demonstrated potential for predicting tumour classification and prognosis. In particular, magnetic resonance imaging (MRI) plays a vital role in the diagnosis of brain tumours. Using MRI to extract glioma-related features and construct a machine learning model offers a viable avenue to classify and predict the level of Ki67 expression. METHODS: This study retrospectively collected MRI data and postoperative immunohistochemical results from 613 glioma patients from the First Affliated Hospital of Nanjing Medical University. Subsequently, we performed registration and skull stripping on the four MRI modalities: T1-weighted (T1), T2-weighted (T2), T1-weighted with contrast enhancement (T1CE), and Fluid Attenuated Inversion Recovery (FLAIR). Each modality's segmentation yielded three distinct tumour regions. Following segmentation, a comprehensive set of features encompassing texture, first-order, and shape attributes were extracted from these delineated regions. Feature selection was conducted using the least absolute shrinkage and selection operator (LASSO) algorithm with subsequent sorting to identify the most important features. These selected features were further analysed using correlation analysis to finalise the selection for machine learning model development. Eight models: logistic regression (LR), naive bayes, decision tree, gradient boosting tree, and support vector classification (SVM), random forest (RF), XGBoost, and LightGBM were used to objectively classify Ki67 expression. RESULTS: In total, 613 patients were enroled in the study, and 24,455 radiomic features were extracted from each patient's MRI. These features were eventually reduced to 36 after LASSO screening, RF importance ranking, and correlation analysis. Among all the tested machine learning models, LR and linear SVM exhibited superior performance. LR achieved the highest area under the curve score of 0.912 ± 0.036, while linear SVM obtained the top accuracy with a score of 0.884 ± 0.031. CONCLUSION: This study introduced a novel approach for classifying Ki67 expression levels using MRI, which has been proven to be highly effective. With the LR model at its core, our method demonstrated its potential in signalling a promising avenue for future research. This innovative approach of predicting Ki67 expression based on MRI features not only enhances our understanding of cell activity but also represents a significant leap forward in brain glioma research. This underscores the potential of integrating machine learning with medical imaging to aid in the diagnosis and prognosis of complex diseases.

Association of sudomotor dysfunction with risk of diabetic retinopathy in patients with type 2 diabetes.

PURPOSE: Sudomotor dysfunction is considered as one of the earliest manifestations of diabetic peripheral neuropathy. We aimed to investigate the association between sudomotor dysfunction non-invasively detected by the SUDOSCAN device and diabetic retinopathy (DR) in patients with type 2 diabetes. METHODS: A total of 2010 patients admitted to a tertiary hospital located in Shanghai were included from March 2020 to September 2023. Sudomotor function was assessed by the SUDOSCAN device, and sudomotor dysfunction was defined as feet electrochemical skin conductance (FESC) <60 µs. Fundus radiography was used for DR assessment, which was graded according to the severity, specifically: (1) non-DR; (2) mild nonproliferative DR (NPDR); (3) moderate NPDR/vision-threatening DR (VTDR). RESULTS: Among the enrolled 2010 patients, 525 patients had sudomotor dysfunction; 648 were diagnosed with DR, which was equivalent to 32.2% of all patients. Patients with sudomotor dysfunction had a significantly higher prevalence of DR, compared to those with normal sudomotor function (40.8% vs. 29.2%, P < 0.05). After controlling for confounding factors including HbA1c, sudomotor dysfunction was significantly associated with any DR (odd ratio [OR] = 1.57, 95% CI 1.26-1.96). When FESC was considered as a continuous variable, the multivariable-adjusted OR of DR was 1.29 (95% CI 1.17-1.42) for per 1-SD decrease in FESC. Furthermore, multinomial logistic regression revealed significant associations between sudomotor dysfunction and all stages of DR (mild NPDR: OR = 1.40, 95% CI 1.11-1.78; moderate NPDR/VTDR: OR = 2.35, 95% CI 1.60-3.46). CONCLUSIONS: Sudomotor dysfunction was significantly associated with DR in patients with type 2 diabetes.

Electrostatically Mediated In Situ Polymerization for Enzyme Immobilization and Activation.

Enzyme immobilization in nanoparticles is of interest for boosting their catalytic applications, yet rational approaches to designs achieving both high enzyme loading and activation remain a challenge. Herein, we report an electrostatically mediated in situ polymerization strategy that simultaneously realizes enzyme immobilization and activation. This was achieved by copolymerizing cationic monomers with a cross-linker in the presence of the enzyme lipase (anionic) as the template, which produces enzyme-loaded nanogels. The effects of different control factors such as pH, lipase dosage, and cross-linker fraction on nanogel formation are investigated systematically, and optimal conditions for enzyme loading and activation have been determined. A central finding is that the cationic polymer network of the nanogel creates a favorable environment that not only protects the enzyme but also boosts enzymatic activity nearly 2-fold as compared to free lipase. The nanogels improve the stability of the lipase to tolerate a broader working range of pH (5.5-8.5) and temperature (25-70 °C) and allow recycling such that after six cycles of reaction, 70% of the initial activity is conserved. The established fabrication strategy can be applied generally to different cationic monomers, and most of these nanogels exhibit adequate immobilization and activation of lipase. Our study confirms that in situ polymerization based on electrostatic interaction provides a facile and robust strategy for enzyme immobilization and activation. The wide variety of ionic monomers, therefore, features great potential for developing functional platforms toward satisfying enzyme immobilization and demanding applications.

Sleep Disturbances in Early Gestation and the Risks of Hypertensive Disorders of Pregnancy: A Prospective Cohort Study.

Maternal poor sleep quality may increase blood pressure during pregnancy, but sound evidence is still limited and inconsistent. To evaluate whether sleep disturbances in early gestation are risk factors for the development of hypertensive disorders of pregnancy, we conducted the Early Life Plan Project from June 2016 to December 2019. Maternal sleep patterns were assessed at 12-16 weeks of gestation by using the Pittsburgh Sleep Quality Index questionnaire. For gestational hypertension and preeclampsia, we estimated adjusted odds ratios (ORs) and 95% confidence intervals (CIs) using multinomial logistic regression models adjusting for potential confounders. Among 5,532 eligible women, we observed that maternal blood pressure in early gestation was significantly higher in women with low sleep efficiency (≤85%), long sleep duration (≥9 hours/night), and snoring. Compared with nonsnorers, snoring in early gestation was independently associated with preeclampsia (OR = 1.72 (95% CI: 1.09, 2.73) for snoring once or twice per week; OR = 2.06 (95% CI: 1.01, 4.31) for snoring 3 or more times per week), particularly for term preeclampsia (OR = 1.79 (95% CI: 1.08, 2.95) and 2.26 (95% CI: 1.03, 4.95), respectively). Results suggest that snoring in early gestation may be a significant risk factor for preeclampsia, with a dose-response pattern.

Deep-learning and conventional radiomics to predict IDH genotyping status based on magnetic resonance imaging data in adult diffuse glioma.

Objectives: In adult diffuse glioma, preoperative detection of isocitrate dehydrogenase (IDH) status helps clinicians develop surgical strategies and evaluate patient prognosis. Here, we aim to identify an optimal machine-learning model for prediction of IDH genotyping by combining deep-learning (DL) signatures and conventional radiomics (CR) features as model predictors. Methods: In this study, a total of 486 patients with adult diffuse gliomas were retrospectively collected from our medical center (n=268) and the public database (TCGA, n=218). All included patients were randomly divided into the training and validation sets by using nested 10-fold cross-validation. A total of 6,736 CR features were extracted from four MRI modalities in each patient, namely T1WI, T1CE, T2WI, and FLAIR. The LASSO algorithm was performed for CR feature selection. In each MRI modality, we applied a CNN+LSTM-based neural network to extract DL features and integrate these features into a DL signature after the fully connected layer with sigmoid activation. Eight classic machine-learning models were analyzed and compared in terms of their prediction performance and stability in IDH genotyping by combining the LASSO-selected CR features and integrated DL signatures as model predictors. In the validation sets, the prediction performance was evaluated by using accuracy and the area under the curve (AUC) of the receiver operating characteristics, while the model stability was analyzed by using the relative standard deviation of the AUC (RSDAUC). Subgroup analyses of DL signatures and CR features were also individually conducted to explore their independent prediction values. Results: Logistic regression (LR) achieved favorable prediction performance (AUC: 0.920 ± 0.043, accuracy: 0.843 ± 0.044), whereas support vector machine with the linear kernel (l-SVM) displayed low prediction performance (AUC: 0.812 ± 0.052, accuracy: 0.821 ± 0.050). With regard to stability, LR also showed high robustness against data perturbation (RSDAUC: 4.7%). Subgroup analyses showed that DL signatures outperformed CR features (DL, AUC: 0.915 ± 0.054, accuracy: 0.835 ± 0.061, RSDAUC: 5.9%; CR, AUC: 0.830 ± 0.066, accuracy: 0.771 ± 0.051, RSDAUC: 8.0%), while DL and DL+CR achieved similar prediction results. Conclusion: In IDH genotyping, LR is a promising machine-learning classification model. Compared with CR features, DL signatures exhibit markedly superior prediction values and discriminative capability.

Association between glycaemia risk index (GRI) and diabetic retinopathy in type 2 diabetes: A cohort study.

AIM: To investigate the association between a new composite metric, glycaemia risk index (GRI), and incident diabetic retinopathy (DR). METHODS: A total of 1204 adults with type 2 diabetes without DR at baseline were included between 2005 and 2019 from a single centre in Shanghai, China. GRI was obtained from continuous glucose monitoring data at baseline. Cox proportion hazard regression analysis was used to assess the association between GRI and the risk of incident DR. RESULTS: During a median follow-up of 8.4 years, 301 patients developed DR. The multivariable-adjusted hazard ratios (HRs) for incident DR across ascending GRI quartiles (≤14 [reference], 15 ~ 28, 29 ~ 47 and > 47) were 1.00, 1.05 (95% CI 0.74-1.48), 1.33 (95% confidence interval [CI] 0.96-1.84) and 1.53 (95% CI 1.11-2.11), respectively. For each 1-SD increase in GRI, the risk of DR was increased by 20% (HR 1.20, 95% CI 1.07-1.33) after adjustment for confounders. CONCLUSIONS: In patients with type 2 diabetes, higher GRI is associated with an increased risk of incident DR. GRI has the potential to be a valuable clinical measure, which needs to be further explored in future studies.

Association of thyroid stimulating hormone and time in range with risk of diabetic retinopathy in euthyroid type 2 diabetes.

AIMS: Diabetic retinopathy (DR) can occur even in well-controlled type 2 diabetes, suggesting residual risks of DR in this population. In particular, we investigated the combined effect of thyroid function and glycaemic control assessed by an emerging metric, time in range (TIR) with DR. MATERIALS AND METHODS: In this cross-sectional study, a total of 2740 euthyroid patients with type 2 diabetes were included. Thyroid indicators, including thyroid-stimulating hormone (TSH), free triiodothyronine, free thyroxine, thyroid peroxidase antibody and thyroglobulin antibody, were measured. TIR was measured using continuous glucose monitoring data. RESULTS: Overall, the multivariable-adjusted odds ratios (ORs) for DR across ascending tertiles of TSH were 1.00 (reference), 1.06 (95% confidence interval [CI] 0.85-1.32), and 1.48 (95% CI 1.19-1.85). Even in well-controlled participants who achieved a TIR target of >70% (n = 1449), the prevalence of DR was 23.8%, which was significantly related to TSH (OR = 1.54, 95% CI 1.12-2.12, highest vs. lowest TSH tertile). Participants were then classified into 6 groups by the joint categories of TIR (>70%, ≤70%) and TSH (tertiles), and the multivariable-adjusted ORs for DR were highest in TIR ≤70% and the highest TSH tertile group (OR = 1.96, 95% CI 1.41-2.71) when compared with the TIR >70% and the lowest TSH tertile group. CONCLUSIONS: In type 2 diabetic patients with well-controlled glycaemic status, higher TSH within the normal range was associated with an increased risk of DR. The combination of suboptimal TSH and TIR further increased the risk of DR.

Comparison of glucose time in range and area under curve in range in relation to risk of diabetic retinopathy in type 2 diabetes patients.

AIMS/INTRODUCTION: We proposed a novel continuous glucose monitoring (CGM)-based metric, area under the curve in range (AucIR), for integrating both the amplitude and duration of dysglycemia, and further compared AucIR with the emerging key CGM-derived metric, time in range (TIR). MATERIALS AND METHODS: A total of 2,030 adult patients with type 2 diabetes were enrolled during May 2020 to October 2021. AucIR and TIR were measured with 7-day CGM data. Logistic regression analysis and the C-statistic was carried out to assess the association of AucIR and TIR with diabetic retinopathy (DR). RESULTS: Both AucIR (r = -0.89) and TIR (r = -0.95) were strongly correlated with mean glucose levels. Compared with TIR, AucIR showed a tighter relationship with parameters of glycemic variability, including the coefficient of variation (r = -0.56), standard deviation (r = -0.89) and mean amplitude of glycemic excursions (r = -0.70). For each absolute 10% decrease in AucIR, the risk of DR was increased by 7% (95% confidence interval 1.02-1.13) after adjustment for confounders. With respect to TIR, each absolute 10% decrease was associated with an 8% (95% confidence interval 1.03-1.14) increased risk of DR. The model discrimination for DR, as measured by C-statistic, did not differ significantly between the two metrics (P > 0.05). CONCLUSIONS: AucIR did not provide added benefit over TIR in the assessment of DR risk among patients with type 2 diabetes. The potential value of AucIR needs to be explored in future studies.

Association of time in range with lower extremity atherosclerotic disease in type 2 diabetes mellitus: a prospective cohort study.

PURPOSE: Time in range (TIR) has surfaced as a key continuous glucose monitoring (CGM)-derived metric, which was linked to diabetes-related outcomes. We aimed to investigate the association of TIR with the risk of lower extremity atherosclerotic disease (LEAD) among patients with type 2 diabetes. METHODS: A total of 1351 adult patients with type 2 diabetes were prospectively recruited from a single center in Shanghai, China. TIR was obtained from CGM data at baseline. LEAD was measured with color Doppler ultrasonography. Cox proportion hazard regression analysis was used to assess the association between TIR and the risk of incident/progressive LEAD. RESULTS: During a median follow-up of 7.4 years, 450 participants developed incident/progressive LEAD. The multivariable-adjusted hazard ratios (HRs) for incident/progressive LEAD across different levels of TIR ( > 85%, 71~85%, 51~70%, and ≤50%) were 1.00, 1.15 (95% confidence interval [CI] 0.87-1.52), 1.37 (95% CI 1.04-1.80) and 1.46 (95% CI 1.10-1.94) (P for trend = 0.004), respectively. With each 10% decrease in TIR, the multivariable-adjusted risk of incident/progressive LEAD increased by 7% (95% CI 1.02-1.11). Similar results were found in the association between TIR and incident LEAD as the secondary outcome (P for trend < 0.001). CONCLUSIONS: The current study found an inverse association of TIR with the risk of LEAD among patients with type 2 diabetes.

Synthesis of Anionic Nanogels for Selective and Efficient Enzyme Encapsulation.

Polyelectrolyte nanogels containing cross-linked ionic polymer networks feature both soft environment and intrinsic charges which are of great potential for enzyme encapsulation. In this work, well-defined poly(acrylic acid) (PAA) nanogels have been synthesized based on a facile strategy, namely, electrostatic assembly directed polymerization (EADP). Specifically, AA monomers are polymerized together with a cross-linker in the presence of a cationic-neutral diblock copolymer as the template. Effects of control factors including pH, salt concentration, and cross-linking degree have been investigated systematically, based on which the optimal preparation of PAA nanogels has been established. The obtained nanogel features not only compatible pocket for safely loading enzymes without disturbing their structures, but also abundant negative charges which enable selective and efficient encapsulation of cationic enzymes. The loading capacities of PAA nanogels for cytochrome (cyt c) and lysozyme are 100 and 125 µg/mg (enzyme/nanogel), respectively. More notably, the PAA network seems to modulate a favorable microenvironment for cyt c and induces 2-fold enhanced activity for the encapsulated enzymes, as indicated by the steady-state kinetic assay. Our study reveals the control factors of EADP for optimal synthesis of anionic nanogels and validates their distinctive advances with respect to efficient loading and activation of cationic enzymes.

Interleukin-10 levels and risk of gestational diabetes mellitus: a systematic review and meta-analysis.

BACKGROUND AND OBJECTIVE: Association of interleukin-10 (IL-10) polymorphism with diabetes and its complication was recently established, while there were few kinds of research considering the potential role of IL-10 in gestational diabetes (GDM). This study aimed to systematically review the association between serum IL-10 level and GDM susceptibility. METHODS: A comprehensive literature search for the published studies in PubMed, Scopus, CENTRAL (Cochrane Central Register of Controlled Trials), and Google Scholar databases was performed for English language papers published up to 31st July 2020. Following key terms were used: "Cytokine level" OR "Interleukin-10" OR "IL-10," OR "Pro-inflammatory Cytokines" OR "gestational diabetes mellitus" OR "GDM." Fixed or random-effects models were used to estimate the pooled SDM and 95% confidence intervals (CIs). Begg's funnel plot was used to assess the potential for publication bias. RESULTS: In our meta-analysis, a total of ten studies for the risk of GDM involving 609 GDM cases and 664 controls were included. No significant association between IL-10 levels and risk of GDM as compared to control subjects (SMD = -0.09, 95% CI = -0.73 to 0.55). Subgroup analysis based on ethnicity also does not reveal any association between IL-10 levels and risk of GDM as compared to control subjects has more or less similar trends in Caucasian (SMD = -0.07, 95% CI = -0.58 to 0.45) as well as Asian population (SMD = -0.03, 95% CI = -1.56 to 1.49). CONCLUSION: Our findings suggest that the serum IL-10 level may not be significantly associated with an increased risk of susceptibility to GDM. Further well-designed prospective studies embedded with a large sample size are needed to confirm these findings.

Consumption of Coffee and Risk of Gestational Diabetes Mellitus: A Systematic Review and Meta-Analysis of Observational Studies.

Objective: The results from epidemiologic studies on the relationship between intake of coffee and the risk of gestational diabetes mellitus (GDM) remain inconclusive. A meta-analysis was performed to achieve a comprehensive finding regarding the association between intake of coffee and the risk of GDM. Methods: PubMed, Scopus, ISI Web of Science, and Google Scholar were searched to find articles published up to August 2021. Observational studies that reported risk estimates [risk ratios (RRs), hazard ratios (HRs), and odds ratios (ORs)] for the association of consumption of coffee with the risk of GDM in pregnant women were included. Random effects model was applied to calculate summarized risk estimate and 95% CIs for the highest vs. lowest categories of intake of coffee. Results: Seven observational studies (three cohort, two case-control, and two cross-sectional studies) with 75,607 participants and 1,625 women with GDM met the inclusion criteria. The meta-analysis of comparing the highest vs. lowest intake of coffee categories showed no significant association between intake of coffee and risk of GDM (summarized risk estimate: 0.89; 95% CI: 0.76, 1.05; I 2 = 63.4%). Subgroup analysis showed that consumption of coffee had an inverse relationship with GDM in studies conducted in non-Asia countries (summarized risk estimate: 0.75; 95% CI: 0.58, 0.97; I 2 = 6%). Conclusion: This study has shown that high consumption of coffee did not decrease the risk of GDM. Furthermore, large-scale cohort studies are required to confirm our findings.

Regulated Polyelectrolyte Nanogels for Enzyme Encapsulation and Activation.

Polyelectrolyte (PE) nanogels consisting of cross-linked PE networks integrate the advanced features of both nanogels and PEs. The soft environment and abundant intrinsic charges are of special interest for enzyme immobilization. However, the crucial factors that regulate enzyme encapsulation and activation remain obscure to date. Herein, we synthesized cationic poly (dimethyl aminoethyl methacrylate), PDMAEMA, nanogels with well-defined size and cross-link degrees and fully investigated the effects of different control factors on lipase immobilization. We demonstrate that the cationic PDMAEMA nanogels indeed enable efficient and safe loading of anionic lipase without disturbing their structures. Strong charge interaction achieved by tuning pH and larger particle size are favorable for lipase loading, while the enhanced enzymatic activity demands nanogels with smaller size and a moderate cross-link degree. As such, PDMAEMA nanogels with a hydrodynamic radius of 35 nm and 30% cross-linker fraction display the optimal catalytic efficiency, which is fourfold of that of free lipase. Moreover, the immobilization endows enhanced enzymatic activity in a broad scope of pH, ionic strength, and temperature, demonstrating effective protection and activation of lipase by the designed nanogels. Our study validates the crucial controls of the size and structure of PE nanogels on enzyme encapsulation and activation, and the revealed findings shall be helpful for designing functional PE nanogels and boosting their applications for enzyme immobilization.

Maternal exposure to Wenchuan earthquake and prolonged risk of offspring birth outcomes: a natural experiment study.

BACKGROUND: The prolonged effects of disasters on reproductive outcomes among the survivors are less studied, and the findings are inconsistent. We examined the associations of maternal exposure to the 2008 Wenchuan earthquake years before conception with adverse birth outcomes. METHODS: We included 73,493 women who delivered in 96 hospitals in 24 provinces and autonomous regions from the 2015/16 China Labor and Delivery Survey. We weighted the multivariable logistic models based on the combination of coarsened exact matching (CEM) weight and survey weight, and performed sex-stratified analysis to test whether associations of maternal earthquake exposure with adverse birth outcomes (Stillbirth, preterm birth [PTB], low birthweight [LBW], and small for gestational age [SGA]) varied by sex. RESULTS: The bivariate models showed that the weighted incidence of each adverse birth outcome was higher in exposed group than unexposed group: stillbirth (2.00% vs. 1.33%), PTB (14.14% vs. 7.32%), LBW (10.82% vs. 5.76%), and SGA (11.32% vs. 9.52%). The multivariable models showed maternal earthquake exposure was only associated significantly with a higher risk of PTB in offspring among all births (adjusted risk ratio [aRR](95%CI):1.25(1.06-1.48), P = 0.010). The sex-stratified analysis showed the association was significant among male births (aRR (95%CI): 1.40(1.12-1.75),P = 0.002),but unsignificant among female births. The sensitivity analysis reported similar findings. CONCLUSIONS: The 2008 Wenchuan earthquake exposure has a long-term effect on PTB. Maternal acute exposure to disasters could be a major monitor for long-term reproductive outcomes. More attention should be paid to the underlining reasons for disaster-related adverse birth outcomes.

Tetramethylpyrazine reduces the consequences of nitric oxide inhibition in pregnant rats.

Pre-eclampsia (PE) is closely associated with perinatal morbidity and mortality and we want to investigate tetramethylpyrazine (TMP)'s effects on PE. Pregnant Sprague-Dawley rats were randomly divided into five groups: normal pregnant (PC), PE, PE+TMP 20 mg/kg, PE+TMP 40 mg/kg, and PE+TMP 60 mg/kg group. The PE rat model was established via L-NAME treatment. Systolic blood pressures (SBP) and urinary protein concentration were detected via the tail-cuff method and CBB kit, respectively. mRNA levels of key genes were analyzed via quantitative PCR and protein levels of key genes were measured by ELISA or western blot. TMP decreased SBP and urinary protein concentration of PE rats. TMP inhibited L-NAME-induced decrease in pups alive ratio, pups weight, and the ratio of pups/placenta weight and reversed L-NAME induced changes in placental histology, whereas it had little effect on placental weight. Urinary nephrin and podocin expressions were enhanced and serum placental growth factor level was decreased in PE rats, whereas TMP inhibited the above phenomena. TMP suppressed L-NAME-induced sFlt-1 upregulation in serums and kidneys of PE rats, whereas it downregulated IL-6 and MCP-1 expression in PE rats' serums, placentas and kidneys. TMP also suppressed the increase in placental sFlt-1 and vascular endothelial growth factor level caused by L-NAME. In addition, TMP inhibited CHOP and GRP78 expressions and decreased the ratio of p-elF2α/elF2α in PE rats. TMP attenuated the consequences of NO inhibition in pregnant rats.