Serum arsenic augments gallstone risk in Henan rural cohort with multiple metal exposure.

BACKGROUND: People are exposed to metals in various ways during their daily lives. However, the association between metal exposure and gallstones remains unclear. OBJECTIVES: To investigate the relationship between serum elemental concentrations and the risk of gallstones. METHODS: Participants (n = 4204) were drawn from the Henan Rural Cohort. Gallstone diagnosis was based on abdominal ultrasound reports during follow-up. Baseline serum elemental concentrations were measured using inductively coupled plasma mass spectrometry. The relationship between serum elemental levels and gallstones was evaluated using robust Poisson regression, restricted cubic spline (RCS), quantile g-computation (Qgcomp), grouped weighted quantile sum (GWQS) and Bayesian kernel machine regression (BKMR). RESULTS: 121 individuals were diagnosed with gallstone (incidence rate of 2.88 %). In robust Poisson regression, after adjusting for confounding factors, the highest quartile of arsenic concentration compared to the lowest quartile had a 1.90 times higher relative risk (RR) [95 % confidence interval (CI): 1.05, 3.44]. Conversely, the highest quartile of zinc concentration compared to the lowest quartile had a 0.50 times lower RR (95 % CI: 0.28, 0.89). RCS showed an approximately "S"-shaped nonlinear relationship between serum arsenic levels and gallstones, with increasing arsenic concentration leading to a higher risk of gallstones; however, the risk plateaued when arsenic concentration exceeded 0.62 µg/L. Both the Qgcomp and GWQS indicated that arsenic plays a significant role in increasing the risk of gallstones, whereas zinc plays a significant role in reducing the risk of gallstones. BKMR showed that raising arsenic exposure from the 25th to the 75th percentile increased the risk of gallstones, while raising serum zinc concentration reduced it. CONCLUSIONS: Higher serum arsenic concentration increases the risk of gallstones, whereas higher zinc concentration may reduce the risk. Effective prevention of gallstones may require further reduction of arsenic exposure and appropriate increases in zinc intake.

Differential Effects of Continuous Theta Burst Stimulation over the Bilateral and Unilateral Cerebellum on Working Memory.

Recent functional MRI studies have implicated the cerebellum in working memory (WM) alongside the prefrontal cortex. Some findings indicate that the right cerebellum is activated during verbal tasks, while the left is engaged during visuospatial tasks, suggesting cerebellar lateralization in WM function. The cerebellum could be a potential target for non-invasive brain stimulation (NIBS) to enhance WM function in cognitive disorders. However, the comprehensive influence of cerebellar lateralization on different types of WM and the effect of stimulation over the unilateral or bilateral cerebellum remain uncertain. This study was to investigate the cerebellum's functional lateralization and its specific impact on various aspects of WM in a causal manner using unilateral or bilateral cerebellar continuous theta burst stimulation (cTBS), a form of inhibitroy NIBS. Twenty-four healthy participants underwent four sessions of cTBS targeting the left, right, or bilateral Crus I of the cerebellum, or a sham condition, in a controlled cross-over design. WM performance was assessed pre- and post-stimulation using neuropsychological tests, including the 3-back task, spatial WM task, and digit span task. Results indicated that cTBS over the bilateral and right cerebellum both led to a greater improvement in 3-back task performance compared to sham stimulation. Additionally, active cTBS over the bilateral cerebellum yielded better performance in the spatial WM task than sham stimulation. However, no significant differences were observed between stimulation conditions for the auditory digit span task. This study may provide novel causal evidence highlighting the specific involvement of the right and bilateral cerebellum in various types of WM. Specifically, the right cerebellum appears crucial for updating and tracking 3-back WM content, while spatial WM processes require the coordinated engagement of both cerebellar hemispheres.

Clinical and Imaging Features of Pulmonary Nodular Lymphoid Hyperplasia.

PURPOSE: To investigate the clinical and radiographic features of PNLH and the relationship with pathologic features. MATERIALS AND METHODS: A total of 11 patients in whom PNLH was confirmed in our department were retrospectively studied. The clinical and radiographic features were extracted and analyzed, and we also discussed the relationship between radiologic and pathologic features. RESULTS: Of the 11 patients with PNLH, 5 were discovered incidentally, while 4 presented with chest symptoms. Laboratory tests showed no specificity and the lesions were located under the pleura with an adjacent pleural indentation. Most lesions were solid, with some showing signs of spiculation or spiculate protuberance. In some cases, hypodense areas and vocules were visible. The enhanced scan showed marked enhancement, but most had no lymph node enlargement, and there was no pleural effusion. CONCLUSIONS: The clinical manifestations of PNLH are nonspecific and the imaging features overlap with those of malignant lung tumors, and the diagnosis depends on pathologic examination.

Do social economic status modify the association between air pollution and depressive or anxiety symptoms? A big sample cross-sectional study from the rural areas of Central China.

BACKGROUND: Fewer studies have examined the relationship between air pollution and depressive or anxiety symptoms in rural residents. Social economic status (SES), as an important indicator of the current state of socioeconomic development, we know little about how it modifies the relationship between air pollution and symptoms of depression or anxiety. METHODS: The patient health questionnaire (PHQ-2) and generalized anxiety scale (GAD-2) were used to learn about the prevalence of depressive and anxiety symptoms, the social economic status of the participants was categorized into two levels: lower and higher, and a binary logistic regression model was used to analyze the relationship between air pollution and residents' symptoms of depression or anxiety. RESULTS: A total of 10,670 adults were enrolled in this study, of which a total of 1292 participants suffered from depressive symptoms and 860 suffered from anxiety symptoms. Short-term exposure to PM2.5 and O3, singly or in combination, may be associated with the onset of depression symptoms, and there was a significant interaction between SES and exposure to PM2.5 or O3. Residents of areas with higher SES may have a lower risk of suffering from anxiety symptoms after O3 exposure compared to participants living in lower SES. LIMITATIONS: The study was a cross-sectional study, which may have lowered the quality level of the evidence. CONCLUSIONS: Short-term PM2.5 and O3 exposure may be associated with an increased prevalence risk of depressive symptoms. Higher levels of SES may reduce the adverse effects of air pollution on depressive or anxiety symptoms.

Nickel-Catalyzed Cross-Coupling of Aryl Diazonium Salts with Aryl Bromides.

Herein, we present a one-pot method for the direct cross-coupling of aryl diazonium salts and aryl bromides in an economical way that avoids the use of sensitive organometallic reagents. The reaction is accomplished with the assistance of nickel catalysts, ligands, magnesium turnings, lithium chloride, and triethylamine, avoiding the use of pre-activated organometallic reagents.

Multi-machine Learning Model Based on Habitat Subregions for Outcome Prediction in Adenomyosis Treated by Uterine Artery Embolization.

RATIONALE AND OBJECTIVES: To establish and validate a predictive multi-machine learning model for the long-term efficacy of uterine artery embolization (UAE) in the treatment of adenomyosis based on habitat subregions. MATERIALS AND METHODS: Patients who underwent UAE for adenomyosis at institution A between November 2015 and June 2018 were included in the training cohort and those at institution B between June 2017 and June 2019 were included in the test cohort. The regions of interest (ROI) were manually segmented on the T2-weighted images (T2WI). The ROIs were subsequently partitioned into habitat subregions using k-means clustering. Radiomic features were extracted from each subregion on T1WI, T2WI, apparent diffusion coefficient, and contrast-enhanced images. The least absolute shrinkage and selection operator (LASSO) was used to select the subregion radiomics features. With the improvement in patients' symptoms at 36 months post-UAE, the habitat subregion features were trained using six machine-learning classifiers. The most suitable classifier was chosen based on model performance to establish the habitat radiomics model (HRM). The efficacy of the model was validated using both the training and test cohorts. Finally, a whole-region radiomics model (WRM) and clinical model (CM) were established. The Delong test was used to compare the predictive performance of the habitat subregion model and the two other models. RESULTS: The study included 258 patients, 191 in the training cohort and 67 in the test cohort. The ROIs were divided into four habitat subregions. Radiomics features were extracted from different sequence images of the subregions. After LASSO regression, 24 habitat subregion features were included in the model. Based on the receiver operating characteristic curve analysis, the area under the curve (AUC) of the HRM was 0.921 (95% CI, 0.857-0.985, training) and 0.890 (95% CI, 0.736-1.000, test). The AUCs for the WRM were 0.805 (95% CI, 0.737-0.872, training) and 0.693 (95% CI, 0.497-0.889, test). Compared to the HRM, the difference in predictive performance was statistically significant (p = 0.008, training; p = 0.007, test). The AUCs for the CM were 0.788 (95% CI, 0.711-0.866, training) and 0.735 (95% CI, 0.566-0.903, test). Compared to the HRM, there was a statistically significant difference in the training cohort (p = 0.014) but not in the test cohort (p = 0.186). CONCLUSION: The HRM can predict the long-term efficacy of UAE in the treatment of adenomyosis. The predictive performance was superior to that of both the WRM and CM, serving as an effective tool to assist interventional physicians in clinical decision-making.

Adipose Triglyceride Lipase-Mediated Adipocyte Lipolysis Exacerbates Acute Pancreatitis Severity in Mouse Models and Patients.

Dyslipolysis of adipocytes plays a critical role in various diseases. Adipose triglyceride lipase (ATGL) is a rate-limiting enzyme in adipocyte autonomous lipolysis. However, the degree of adipocyte lipolysis related to the prognoses in acute pancreatitis (AP) and the role of ATGL-mediated lipolysis in the pathogenesis of AP remain elusive. Herein, the visceral adipose tissue consumption rate in the acute stage was measured in both patients with AP and mouse models. Lipolysis levels and ATGL expression were detected in cerulein-induced AP models. CL316,243, a lipolysis stimulator, and adipose tissue-specific ATGL knockout mice were used to further investigate the role of lipolysis in AP. The ATGL-specific inhibitor, atglistatin, was used in C57Bl/6N and ob/ob AP models. This study indicated that increased visceral adipose tissue consumption rate in the acute phase was independently associated with adverse prognoses in patients with AP, which was validated in mouse AP models. Lipolysis of adipocytes was elevated in AP mice. Stimulation of lipolysis aggravated AP. Genetic blockage of ATGL specifically in adipocytes alleviated the damage to AP. The application of atglistatin effectively protected against AP in both lean and obese mice. These findings demonstrated that ATGL-mediated adipocyte lipolysis exacerbates AP and highlighted the therapeutic potential of ATGL as a drug target for AP.

The role of the immune system in depersonalisation disorder.

OBJECTIVES: Depersonalisation-derealization disorder (DPD) is a dissociative disorder that impairs cognitive function and occupational performance. Emerging evidence indicate the levels of tumour necrosis factor-α and interleukin associated with the dissociative symptoms. In this study, we aimed to explore the role of the immune system in the pathology of DPD. METHODS: We screened the protein expression in serum samples of 30 DPD patients and 32 healthy controls. Using a mass spectrometry-based proteomic approach, we identified differential proteins that were verified in another group of 25 DPD patients and 30 healthy controls using immune assays. Finally, we performed a correlation analysis between the expression of differential proteins and clinical symptoms of patients with DPD. RESULTS: We identified several dysregulated proteins in patients with DPD compared to HCs, including decreased levels of C-reactive protein (CRP), complement C1q subcomponent subunit B, apolipoprotein A-IV, and increased levels of alpha-1-antichymotrypsin (SERPINA3). Moreover, the expression of CRP was positively correlated with visuospatial memory and the ability to inhibit cognitive interference of DPD. The expression of SERPINA3 was positively correlated with the ability to inhibit cognitive interference and negatively correlated with the perceptual alterations of DPD. CONCLUSIONS: The dysregulation of the immune system may be the underlying biological mechanism in DPD. And the expressions of CRP and SERPINA3 can be the potential predictors for the cognitive performance of DPD.

Using Apparent Diffusion Coefficient (ADC) of Endometrial Cancer MRI to Determine P53 Molecular Subtypes.

BACKGROUND: Endometrial Cancer (EC) is a highly heterogeneous cancer comprising both histological and molecular subtypes. Using a non-invasive modality method to trigger these subtypes as early as possible can aid clinicians in establishing individualized treatment. PURPOSE: The study aimed to clarify the value of the Apparent Diffusion Coefficient (ADC) of EC MRI in determining molecular subtypes. MATERIAL AND METHODS: We retrospectively recruited 109 patients with pathologically proven EC (78 endometrioid cancers and 31 non-endometrioid cancers) with available molecular classification from a tertiary centre. MRI was prospectively performed a month prior to surgery; images were blindly interpreted by two experienced radiologists with consensus reading. The ADC value was measured by an experienced radiologist on the commercially available processing workstation. Interoperator measurement consistency was calculated. RESULTS: Our sample comprised 17 PLOE, 32 MSI-H, 31 NSMP, and 29 P53abn ECs. Clinical information did not differ significantly among the groups. The maximum diameter and volume of the lesions differed among the groups. The ADC value in the maximal area (ADCarea) or region of interest (ROI, ADCroi) in the P53abn group was higher than that in the other groups (894.0 ±12.6 and 817.5 ± 83.3 x10-6 mm2/s). The ADC mean values were significantly different between the P53abn group and the other groups (P = 0.000). The nomogram showed the highest discriminative ability to distinguish P53abn EC from other types (AUC: 0.859). CONCLUSION: Our results have suggested the quantitative MR characteristics (ADC values) derived from preoperative EC MRI to provide useful information in preoperatively determining P53abn cancer.

A Hybrid Model for Fetal Growth Restriction Assessment by Automatic Placental Radiomics on T2-Weighted MRI and Multifeature Fusion.

BACKGROUND: MRI-based placental analyses have been used to improve fetal growth restriction (FGR) assessment by complementing ultrasound-based measurements. However, these are still limited by time-consuming manual annotation in MRI data and the lack of mother-based information. PURPOSE: To develop and validate a hybrid model for accurate FGR assessment by automatic placental radiomics on T2-weighted imaging (T2WI) and multifeature fusion. STUDY TYPE: Retrospective. POPULATION: 274 pregnant women (29.5 ± $$ \pm $$ 4.0 years) from two centers were included and randomly divided into training (N = 119), internal test (N = 40), time-independent validation (N = 43), and external validation (N = 72) sets. FIELD STRENGTH/SEQUENCE: 1.5-T, T2WI half-Fourier acquisition single-shot turbo spin-echo pulse sequence. ASSESSMENT: First, the placentas on T2WI were manually annotated, and a deep learning model was developed to automatically segment the placentas. Then, the radiomic features were extracted from the placentas and selected by three-step feature selection. In addition, fetus-based measurement features and mother-based clinical features were obtained from ultrasound examinations and medical records, respectively. Finally, a hybrid model based on random forest was constructed by fusing these features, and further compared with models based on other machine learning methods and different feature combinations. STATISTICAL TESTS: The performances of placenta segmentation and FGR assessment were evaluated by Dice similarity coefficient (DSC) and the area under the receiver operating characteristic curve (AUROC), respectively. A P-value <0.05 was considered statistically significant. RESULTS: The placentas were automatically segmented with an average DSC of 90.0%. The hybrid model achieved an AUROC of 0.923, 0.931, and 0.880 on the internal test, time-independent validation, and external validation sets, respectively. The mother-based clinical features resulted in significant performance improvements for FGR assessment. DATA CONCLUSION: The proposed hybrid model may be able to assess FGR with high accuracy. Furthermore, information complementation based on placental, fetal, and maternal features could also lead to better FGR assessment performance. TECHNICAL EFFICACY: Stage 2.

Significant but partial lipoprotein lipase functional loss caused by a novel occurrence of rare LPL biallelic variants.

BACKGROUND: Lipoprotein lipase (LPL) plays a crucial role in triglyceride hydrolysis. Rare biallelic variants in the LPL gene leading to complete or near-complete loss of function cause autosomal recessive familial chylomicronemia syndrome. However, rare biallelic LPL variants resulting in significant but partial loss of function are rarely documented. This study reports a novel occurrence of such rare biallelic LPL variants in a Chinese patient with hypertriglyceridemia-induced acute pancreatitis (HTG-AP) during pregnancy and provides an in-depth functional characterization. METHODS: The complete coding sequences and adjacent intronic regions of the LPL, APOC2, APOA5, LMF1, and GPIHBP1 genes were analyzed by Sanger sequencing. The aim was to identify rare variants, including nonsense, frameshift, missense, small in-frame deletions or insertions, and canonical splice site mutations. The functional impact of identified LPL missense variants on protein expression, secretion, and activity was assessed in HEK293T cells through single and co-transfection experiments, with and without heparin treatment. RESULTS: Two rare LPL missense variants were identified in the patient: the previously reported c.809G > A (p.Arg270His) and a novel c.331G > C (p.Val111Leu). Genetic testing confirmed these variants were inherited biallelically. Functional analysis showed that the p.Arg270His variant resulted in a near-complete loss of LPL function due to effects on protein synthesis/stability, secretion, and enzymatic activity. In contrast, the p.Val111Leu variant retained approximately 32.3% of wild-type activity, without impacting protein synthesis, stability, or secretion. Co-transfection experiments indicated a combined activity level of 20.7%, suggesting no dominant negative interaction between the variants. The patient's post-heparin plasma LPL activity was about 35% of control levels. CONCLUSIONS: This study presents a novel case of partial but significant loss-of-function biallelic LPL variants in a patient with HTG-AP during pregnancy. Our findings enhance the understanding of the nuanced relationship between LPL genotypes and clinical phenotypes, highlighting the importance of residual LPL function in disease manifestation and severity. Additionally, our study underscores the challenges in classifying partial loss-of-function variants in classical Mendelian disease genes according to the American College of Medical Genetics and Genomics (ACMG)'s variant classification guidelines.

Gut microbial CAZymes markers for depression.

Major depressive disorder (MDD) is a serious mental illness, characterized by disturbances of gut microbiome, it is required to further explore how the carbohydrate-active enzymes (CAZymes) were changed in MDD. Here, using the metagenomic data from patients with MDD (n = 118) and heath controls (HC, n = 118), we found that the whole CAZymes signatures of MDD were significantly discriminated from that in HC. α-diversity indexes of the two groups were also significantly different. The patients with MDD were characterized by enriched Glycoside Hydrolases (GHs) and Polysaccharide Lyases (PLs) relative to HC. A panel of makers composed of 9 CAZymes mainly belonging to GHs enabled to discriminate the patients with MDD and HC with AUC of 0.824. In addition, this marker panel could classify blinded test samples from the two groups with an AUC of 0.736. Moreover, we found that baseline 4 CAZymes levels also could predict the antidepressant efficacy after adjusted confounding factors and times of depressive episode. Our findings showed that MDD was associated with disturbances of gut CAZymes, which may help to develop diagnostic and predictive tools for depression.

Design and application of a novel "turn-on" fluorescent probe for imaging sulfite in living cells and inflammation models.

Sulfite is one of the main existing forms of sulfur dioxide (SO2) in living system, which has been recognized as an endogenous mediator in inflammation. Evidence has accumulated to show that abnormal level of sulfite is associated with many inflammatory diseases, including neurological diseases and cancers. Herein, a novel fluorescent probe named QX-OA was designed and synthesized to detect sulfite. QX-OA was constructed by choosing quinolinium-xanthene as the fluorophore and levulinate as the specific and relatively steady recognition reaction. The probe showed remarkable green turn-on signal at 550 nm, together with high sensitivity (90-fold) and excellent selectivity to sulfite over other possible interfering species. In the meantime, QX-OA was successfully applied to visualize endogenous and exogenous sulfite in Hela cells. In the LPS-induced inflammation model, QX-OA could visualize the dose-dependent increase of sulfite level (0-2 mg/mL). Consequently, QX-OA was determined to be a potential method for detecting sulfite in pre-clinical diagnosis.

Histogram Feature Analysis of Tumor Body on Diffusion-weighted MR Imaging in Differentiation between Granulosa Cell Tumors and Other Sex-cord Tumors in Ovary: Comparison with Histological Results.

OBJECTIVE: We aimed to differentiate granulosa cell tumors (GCT) from other ovarian sex-cord tumors (OSCs) based on feature analysis of the tumor body on MR imaging. METHODS: We retrospectively enrolled 27 patients with pathologically proven sex-cord tumours (14 GSTs, 8 fibromas, 4 fibrothecomas, and 1 sclerosing stromal tumour) from our institution. All MRI examinations were performed at least one month prior to surgery. MR image features were recorded by two radiologists with consensus readings. Histogram analysis was performed using FeAture Explorer software. The differences in histogram parameters between GCT (38.1 ± 14.6 years) and OSC (43.7 ± 18.0 years) groups were compared. Fourteen randomly selected cellular-type myomas who also underwent MRI in our hospital were considered as the control group. The intra-operator consistency of ADC value was evaluated across measurements twice. RESULTS: The repeatability of conventional ADC measurements on the tumor body was good. The values of ADC-mean, ADC-min, and ADC-max significantly differed across three groups (p < 0.001). The histogram variance on DWI, histogram percentage on T2WI, and ADC min showed the best discriminative performance in determining GCTs from other OSCs with an area under the receiver operator curve (AUC) of 0.997, 0.882, and 0.795, respectively. The histogram variance on DWI yielded a sensitivity of 92.3%, a specificity of 100%, and an accuracy of 96.6% in discriminating GSTs from other OSCs. CONCLUSION: In the present study, feature analysis of tumor body MR imaging has helped to differentiate GST from OSC with better performance than conventional ADC measurements.

3D reconstruction of the uterus and automatic segmentation of the uterine cavity on 3D magnetic resonance imaging: A preliminary study.

Purpose: To determine the differences in 3D shape features between septate uterus (SU) and normal uterus and to train a network to automatically delineate uterine cavity on 3D magnetic resonance imaging (MRI). Methods: A total of 43 patients (22 cases of partial septate uterus and 21 cases of complete septate uterus) were included in the experimental group. Nine volunteers were recruited as a control group. The uterine cavity (UC), myometrium (UM), and cervical canal of the uterus were segmented manually using ITK-SNAP software. The three-dimensional shape features of the UC and UM were extracted by using PyRadiomics. The recurrent saliency transformation network (RSTN) method was used to segment the UC. Results: The values of four 3D shape features were significantly lower in the control group than in the partial septate group and the complete septate group, while the values of two features were significantly higher (p < 0.05). The UCs of the three groups were significantly different in terms of flatness and sphericity. The values of six features were significantly lower in the UMs of the control group than in those of the partial septate group and the complete septate group (p < 0.05). After the deep learning networks were trained, the Dice similarity coefficient (DSC) scores of the four folds for different thresholds were all over 80 %. The average volume ratio between predictions and manual segmentation was 101.2 %. Conclusions: Based on 3D reconstruction, 3D shape features can be used to comprehensively evaluate septate uterus and provide a reference for subsequent research. The UC can be automatically segmented on 3D MRI using the RSTN method.

Serum proteomics analysis of drug-naïve patients with generalised anxiety disorder: Tandem mass tags and multiple reaction monitoring.

OBJECTIVES: The prevalence of generalised anxiety disorder (GAD) is high. However, the underlying mechanisms remain elusive. Proteomics techniques can be employed to assess the pathological mechanisms involved in GAD. METHODS: Twenty-two drug-naive GAD patients were recruited, their serum samples were used for protein quantification and identified using Tandem Mass Tag and Multiple Reaction Monitoring (MRM). Machine learning models were employed to construct predictive models for disease occurrence by using clinical scores and target proteins as input variables. RESULTS: A total of 991 proteins were differentially expressed between GAD and healthy participants. Gene Ontology analysis revealed that these proteins were significantly associated with stress response and biological regulation, suggesting a significant implication in anxiety disorders. MRM validation revealed evident disparities in 12 specific proteins. The machine learning model found a set of five proteins accurately predicting the occurrence of the disease at a rate of 87.5%, such as alpha 1B-glycoprotein, complement component 4 A, transferrin, V3-3, and defensin alpha 1. These proteins had a functional association with immune inflammation. CONCLUSIONS: The development of generalised anxiety disorder might be closely linked to the immune inflammatory stress response.

Dynamic nomogram for predicting infected pancreatic necrosis in female patients of childbearing age with hypertriglyceridemia-induced acute pancreatitis.

BACKGROUND: Hypertriglyceridemia is a common cause of acute pancreatitis. Pregnant women are at risk of developing hypertriglyceridemia-induced acute pancreatitis (HTG-AP); however, whether pregnancy increases the risk of infected pancreatic necrosis (IPN) is unknown. AIM: We aimed to assess the association between pregnancy and IPN. METHODS: This 10-year retrospective cohort study was conducted at Jinling Hospital. Adult female patients of childbearing age with HTG-AP between January 2013 and September 2022 were screened. Logistic regression analyses were performed to assess the risk factors for IPN. Patients admitted within 7 days were assigned to the training and validation sets to develop a dynamic nomogram for IPN prediction. RESULTS: 489 patients were included, and 144 developed IPN. Logistic regression analyses revealed pregnancy (OR: 2.578 95% CI: 1.474-4.510) as an independent risk factor for IPN. Gestation weeks, ARDS, albumin level, and serum creatinine level were selected as the predictors of the dynamic nomogram for IPN prediction, with good discrimination in the training set (AUC 0.867 95% CI: 0.794-0.940) and validation set (AUC 0.957 95% CI: 0.885-1.000). CONCLUSION: Pregnancy increases the risk of IPN in adult patients of childbearing age with HTG-AP, and the dynamic nomogram may help risk stratification for IPN.

The relationship between air pollution and depression and anxiety disorders - A systematic evaluation and meta-analysis of a cohort-based study.

OBJECTIVE: To explore the correlation between air pollution and the onset of depression and anxiety disorders, to draw more comprehensive and integrated conclusions, and to provide recommendations for maintaining mental health and developing policies to reduce mental health risks caused by air pollution. METHODS: Meta-analysis of cohort study articles exploring the relationship between air pollution and depression or anxiety disorders included in Pubmed, Web Of Science, CNKI, and Wan Fang database before October 31, 2022, and subgroup analysis of the association between air pollution and depression and anxiety disorders regarding the air pollutants studied, the study population, and Publication bias analysis and sensitivity analysis. RESULTS: A total of 25 articles meeting the criteria were included in this study, including 23 articles examining the relationship between air pollution and depression and 5 articles examining the relationship between air pollution and anxiety disorders. The results of the meta-analysis were based on the type of pollutant and showed that there was a high degree of heterogeneity among the studies on the relationship between air pollution and depression and a significant heterogeneity among the studies on PM2.5 and the risk of anxiety disorders (I2 = 71%, p < .01), so a random-effects model was selected for the analysis. CO, O3, and SO2 and depression onset had combined RR values of 1.10 (1.00, 1.20), 1.06 (0.87, 1.29), 1.17 (1.06, 1.31), 1.19 (0.90, 1.58), 1.03 (0.99, 1.07), and 1.09 (0.97, 1.24), respectively, and PM2.5 and anxiety The combined RR value for morbidity was 1.10 (0.99, 1.22). The results of sensitivity analysis showed that the combined results were stable and reliable. The results of Egger regression method test showed that none of them had significant publication bias (p > .05). LIMITATION: Combined exposure to air pollutants on depression and anxiety, further studies by other researchers are needed in the future. CONCLUSIONS: PM2.5 and NO2 exposure, especially long-term exposure, may be associated with the onset of depression, and no association was found for the time being between PM10, CO, O3, SO2 exposure and depression and PM2.5 exposure and anxiety disorders.

Whole-tumor apparent diffusion coefficient histogram analysis for preoperative risk stratification in endometrial endometrioid adenocarcinoma.

OBJECTIVE: To investigate the application of whole-tumor apparent diffusion coefficient (ADC) histogram metrics for preoperative risk stratification in endometrial endometrioid adenocarcinoma (EEA). METHODS: Preoperative MRI of 502 EEA patients were retrospectively analyzed. Whole tumor ADC histogram analysis was performed with regions of interest drawn on all tumor slices of diffusion-weighted imaging scans. Risk stratification was based on ESMO-ESTRO-ESP guidelines: low-, intermediate-, high-intermediate-, and high-risk. Univariable analysis was used to compare ADC histogram metrics (tumor volume, minADC, maxADC, and meanADC; 10th, 25th, 50th, 75th, and 90th percentiles of ADC [recorded as P10, P25, P50, P75, and P90 ADC, respectively]; skewness; and kurtosis) between different risk EEAs, and multivariable logistic regression analysis to determine the optimal metric or combined model for risk stratifications. Receiver operating characteristic curve analysis with the area under the curve (AUC) was used for diagnostic performance evaluation. RESULTS: A decreasing tendency in multiple ADC values was observed from the low- to high-intermediate-risk EEAs. The (low + intermediate)-risk EEAs and low-risk EEAs had significantly smaller tumor volumes and higher minADCs, meanADCs, P10, P25, P50, P75, and P90 ADCs than the (high-intermediate + high)-risk EEAs and non-low-risk EEAs (all P < 0.05), respectively. The combined models of the (meanADC + volume) and the (P75 ADC + volume) yielded the largest AUCs of 0.775 and 0.780 in identifying the (low + intermediate)- and the low-risk EEAs from the other EEAs, respectively. CONCLUSION: Whole-tumor ADC histogram metrics might be helpful for preoperatively identifying low- and (low + intermediate)-risk EEAs, facilitating personalized therapeutic planning.

Image Recovery Matters: A Recovery-Extraction Framework for Robust Fetal Brain Extraction From MR Images.

The extraction of the fetal brain from magnetic resonance (MR) images is a challenging task. In particular, fetal MR images suffer from different kinds of artifacts introduced during the image acquisition. Among those artifacts, intensity inhomogeneity is a common one affecting brain extraction. In this work, we propose a deep learning-based recovery-extraction framework for fetal brain extraction, which is particularly effective in handling fetal MR images with intensity inhomogeneity. Our framework involves two stages. First, the artifact-corrupted images are recovered with the proposed generative adversarial learning-based image recovery network with a novel region-of-darkness discriminator that enforces the network focusing on artifacts of the images. Second, we propose a brain extraction network for more effective fetal brain segmentation by strengthening the association between lower- and higher-level features as well as suppressing task-irrelevant features. Thanks to the proposed recovery-extraction strategy, our framework is able to accurately segment fetal brains from artifact-corrupted MR images. The experiments show that our framework achieves promising performance in both quantitative and qualitative evaluations, and outperforms state-of-the-art methods in both image recovery and fetal brain extraction.