Ischemic insular damage and stress ulcer in patients of acute ischemic stroke.

BACKGROUND AND AIMS: Stress ulcer (SU) is a common complication in patients with acute ischemic stroke. The relationship of infarction location and the incidence of SU was unclear. Herein, we aim to investigate the association between ischemic insular damage and the development of SU. METHODS: Data were retrieved from the SPARK study (Effect of Cardiac Function on Short-Term Functional Prognosis in Patients with Acute Ischemic Stroke). We included the patients who had experienced an ischemic stroke within 7 days. The diagnosis of SU was based on clinical manifestations, including hematemesis, bloody nasogastric tube aspirate, or hematochezia. Evaluation of ischemic insular damage was conducted through magnetic resonance imaging. Cyclo-oxygenase regression analysis and Kaplan-Meier survival curves were used to assess the relationship between ischemic insular damage and the occurrence of SU. RESULTS: Among the 1357 patients analyzed, 110 (8.1%) developed SUs during hospitalization, with 69 (6.7%) experiencing infarctions in the anterior circulation. After adjusting for potential confounders, patients with ischemic insular damage exhibited a 2.16-fold higher risk of developing SUs compared to those without insular damage (p = .0206). Notably, among patients with infarctions in the anterior circulation, those with insular damage had a 2.21-fold increased risk of SUs (p = .0387). Moreover, right insular damage was associated with a higher risk of SUs compared to left insular damage or no insular damage (p for trend = .0117). Kaplan-Meier curves demonstrated early separation among groups, persisting throughout the follow-up period (all p < .0001). CONCLUSIONS: This study identified a significant independent correlation between ischemic insular damage, particularly on the right side, and the development of SU during hospitalization, indicating the need to consider prophylactic acid-suppressive treatment for patients with ischemic insular damage.

Real-world experience of teriflunomide in relapsing multiple sclerosis: paramagnetic rim lesions may play a role.

Objectives: The aims of this study were to report the effectiveness and safety of teriflunomide in Chinese patients with relapsing-remitting multiple sclerosis (RRMS) and to explore the association of paramagnetic rim lesion (PRL) burden with patient outcome in the context of teriflunomide treatment and the impact of teriflunomide on PRL burden. Methods: This is a prospective observational study. A total of 100 RRMS patients treated with teriflunomide ≥3 months were included in analyzing drug persistence and safety. Among them, 96 patients treated ≥6 months were included in assessing drug effectiveness in aspects of no evidence of disease activity (NEDA) 3. The number and total volume of PRL were calculated in 76 patients with baseline susceptibility-weighted imaging (SWI), and their association with NEDA3 failure during teriflunomide treatment was investigated. Results: Over a treatment period of 19.7 (3.1-51.7) months, teriflunomide reduced annualized relapse rate (ARR) from 1.1 ± 0.8 to 0.3 ± 0.5, and Expanded Disability Status Scale (EDSS) scores remained stable. At month 24, the NEDA3% and drug persistence rate were 43.8% and 65.1%, respectively. In patients with a baseline SWI, 81.6% had at least 1 PRL, and 42.1% had ≥4 PRLs. The total volume of PRL per patient was 0.3 (0.0-11.5) mL, accounting for 2.3% (0.0%-49.0%) of the total T2 lesion volume. Baseline PRL number ≥ 4 (OR = 4.24, p = 0.009), younger onset age (OR = 0.94, p = 0.039), and frequent relapses in initial 2 years of disease (OR = 13.40, p = 0.026) were associated with NEDA3 failure. The PRL number and volume were not reduced (p = 0.343 and 0.051) after teriflunomide treatment for more than 24 months. No new safety concerns were identified in this study. Conclusion: Teriflunomide is effective in reducing ARR in Chinese patients with RRMS. Patients with less PRL burden, less frequent relapses, and relatively older age are likely to benefit more from teriflunomide, indicating that PRL might be a valuable measurement to inform clinical treatment decision.

Methylation entropy landscape of Chinese long-lived individuals reveals lower epigenetic noise related to human healthy aging.

The transition from ordered to noisy is a significant epigenetic signature of aging and age-related disease. As a paradigm of healthy human aging and longevity, long-lived individuals (LLI, >90 years old) may possess characteristic strategies in coping with the disordered epigenetic regulation. In this study, we constructed high-resolution blood epigenetic noise landscapes for this cohort by a methylation entropy (ME) method using whole genome bisulfite sequencing (WGBS). Although a universal increase in global ME occurred with chronological age in general control samples, this trend was suppressed in LLIs. Importantly, we identified 38,923 genomic regions with LLI-specific lower ME (LLI-specific lower entropy regions, for short, LLI-specific LERs). These regions were overrepresented in promoters, which likely function in transcriptional noise suppression. Genes associated with LLI-specific LERs have a considerable impact on SNP-based heritability of some aging-related disorders (e.g., asthma and stroke). Furthermore, neutrophil was identified as the primary cell type sustaining LLI-specific LERs. Our results highlight the stability of epigenetic order in promoters of genes involved with aging and age-related disorders within LLI epigenomes. This unique epigenetic feature reveals a previously unknown role of epigenetic order maintenance in specific genomic regions of LLIs, which helps open a new avenue on the epigenetic regulation mechanism in human healthy aging and longevity.

Left ventricular ejection fraction <60 % is associated with short-term functional disability in patients of acute ischemic stroke.

Background and objective: The association between cardiac dysfunction and functional outcome in acute ischemic stroke (AIS) is not clear. We aimed to investigate the relationship between the routinely assessed left ventricular ejection fraction (LVEF) and functional outcomes in patients with AIS. Methods: Data came from a prospective, observational, single-center study (Effect of Cardiac Function on Short-term Functional Prognosis in Patients with Acute Ischemic Stroke, SPARK). The LVEF was assessed with transthoracic echocardiography within 7 days of stroke onset. The primary outcome was functional disability, defined as a modified Rankin Scale score of 3-6 at 90 days (range: 0-6, with higher scores indicating greater disability). We also investigated the association of the LVEF with mortality, early neurological deterioration, hospital stay, and costs. Multivariate logistic regression analysis and 2:1 propensity score matching (PSM) were performed to compare the differences in outcomes. Results: A total of 1181 patients were included in this analysis, of which 87 (7.4 %) patients were found to have LVEF of <60 %. In the entire study population, LVEF<60 % was significantly associated with functional disability at 90 days (odds ratio [OR]: 1.85, 95 % confidence intervals (CI): 1.01-3.40) after adjusting for all confounders. After PSM, the association was consistently significant (OR: 5.32, 95 % CI: 3.04-9.30). However, associations of the LVEF with mortality, early neurological deterioration, hospital stay, and costs were not consistently significant across all analyses. In the subgroup analysis, the association of LVEF of <60 % with functional disability was statistically significant in patients with non-cardioembolic stroke, but not in patients with cardioembolic stroke (P for interaction = 0.872). Conclusions: An LVEF of <60 % will likely increase the risk of functional disability in patients with AIS. Future strategies to prevent cardiac dysfunction in the acute phase are needed.

Quantitative differentiation of non-invasive bladder urothelial carcinoma and inverted papilloma based on CT urography.

PURPOSE: To investigate the value of CT urography (CTU) indicators in the quantitative differential diagnosis of bladder urothelial carcinoma (BUC) and inverted papilloma of the bladder (IPB). MATERIAL AND METHODS: The clinical and preoperative CTU imaging data of continuous 103 patients with histologically confirmed BUC or IPB were retrospectively analyzed. The imaging data included 6 qualitative indicators and 7 quantitative measures. The recorded clinical information and imaging features were subjected to univariate and multivariate logistic regression analysis to find independent risk factors for BUC, and a combined multi-indicator prediction model was constructed, and the prediction model was visualized using nomogram. ROC curve analysis was used to calculate and compare the predictive efficacy of independent risk factors and nomogram. RESULTS: Junction smoothness, maximum longitudinal diameter, tumor-wall interface and arterial reinforcement rate were independent risk factors for distinguishing BUC from IPB. The AUC of the combined model was 0.934 (sensitivity = 0.808, specificity = 0.920, accuracy = 0.835), and its diagnostic efficiency was higher than that of junction smoothness (AUC=0.667, sensitivity = 0.654, specificity = 0.680, accuracy = 0.660), maximum longitudinal diameter (AUC=0.757, sensitivity = 0.833, specificity = 0.604, accuracy = 0.786), tumor-wall interface (AUC=0.888, sensitivity = 0.755, specificity = 0.808, accuracy = 0.816) and Arterial reinforcement rate (AUC=0.786, sensitivity = 0.936, specificity = 0.640, accuracy = 0.864). CONCLUSION: Above qualitative and quantitative indicators based on CTU and the combination of them may be helpful to the differential diagnosis of BUC and IPB, thus better assisting in clinical decision-making. KEY POINTS: 1. Bladder urothelial carcinoma (BUC) and inverted papilloma of the bladder (IPB) exhibit similar clinical symptoms and imaging presentations. 2. The diagnostic value of CT urography (CTU) in distinguishing between BUC and IPB has not been documented. 3. BUC and IPB differ in lesion size, growth pattern and blood supply. 4. The diagnostic efficiency is optimized by integrating multiple independent risk factors into the prediction model.

Automatic segmentation of hepatocellular carcinoma on dynamic contrast-enhanced MRI based on deep learning.

Objective. Precise hepatocellular carcinoma (HCC) detection is crucial for clinical management. While studies focus on computed tomography-based automatic algorithms, there is a rareness of research on automatic detection based on dynamic contrast enhanced (DCE) magnetic resonance imaging. This study is to develop an automatic detection and segmentation deep learning model for HCC using DCE.Approach: DCE images acquired from 2016 to 2021 were retrospectively collected. Then, 382 patients (301 male; 81 female) with 466 lesions pathologically confirmed were included and divided into an 80% training-validation set and a 20% independent test set. For external validation, 51 patients (42 male; 9 female) in another hospital from 2018 to 2021 were included. The U-net architecture was modified to accommodate multi-phasic DCE input. The model was trained with the training-validation set using five-fold cross-validation, and furtherly evaluated with the independent test set using comprehensive metrics for segmentation and detection performance. The proposed automatic segmentation model consisted of five main steps: phase registration, automatic liver region extraction using a pre-trained model, automatic HCC lesion segmentation using the multi-phasic deep learning model, ensemble of five-fold predictions, and post-processing using connected component analysis to enhance the performance to refine predictions and eliminate false positives.Main results. The proposed model achieved a mean dice similarity coefficient (DSC) of 0.81 ± 0.11, a sensitivity of 94.41 ± 15.50%, a precision of 94.19 ± 17.32%, and 0.14 ± 0.48 false positive lesions per patient in the independent test set. The model detected 88% (80/91) HCC lesions in the condition of DSC > 0.5, and the DSC per tumor was 0.80 ± 0.13. In the external set, the model detected 92% (58/62) lesions with 0.12 ± 0.33 false positives per patient, and the DSC per tumor was 0.75 ± 0.10.Significance.This study developed an automatic detection and segmentation deep learning model for HCC using DCE, which yielded promising post-processed results in accurately identifying and delineating HCC lesions.

Real-world effectiveness and safety of evolocumab in very high-risk atherosclerotic cardiovascular disease patients with acute ischemic stroke.

BACKGROUND: We investigated evolocumab's real-world effectiveness and safety on a background of statin therapy in the acute phase of ischemic stroke (IS) patients with a very high-risk of atherosclerotic cardiovascular disease (ASCVD). METHODS: A real-world, single-center, retrospective study was conducted in the neurology department at Tianjin Huanhu Hospital in China. Patients were divided into two groups: evolocumab treatment (140 mg every two weeks) or the standard of care (SOC) group. The primary efficacy outcome of the study was the achievement of a targeted lipid control rate and the incidence of major adverse cardiovascular events (MACE) by the end of the follow-up. MACE was defined as a composite of various cardiovascular events, cerebrovascular events such as stroke or TIA, and event-related deaths. Propensity score matching (PSM) analysis was utilized to account for confounding factors between groups. Survival analyses were performed using the Kaplan-Meier method and COX regression modeling. RESULTS: 1080 AIS patients with very high-risk ASCVD were recruited. After PSM, there were 528 individuals, with 206 in the evolocumab group and 322 in the SOC group. At 12 months of follow-up, the proportion of LDL-C < 1.4mmol/L and ≥ 50% reduction was 44.91% in the evolocumab group, compared with only 3.12% of SOC-treated patients (p < 0.01). The median follow-up time for clinical events was 15 months. The evolocumab group was associated with a lower risk of cerebrovascular events compared to the SOC group (HR, 0.45; 95% CI, 0.23-0.89; p = 0.02). CONCLUSIONS: This real-world study suggested that evolocumab on a background of statin reduced the LDL-C levels significantly and lowered the incidence of recurrent cerebrovascular events in the very high-risk ASCVD patients with AIS in China.

Scrutiny of genome-wide somatic mutation profiles in centenarians identifies the key genomic regions for human longevity.

Somatic mutations accumulate with age and are associated closely with human health, their characterization in longevity cohorts remains largely unknown. Here, by analyzing whole genome somatic mutation profiles in 73 centenarians and 51 younger controls in China, we found that centenarian genomes are characterized by a markedly skewed distribution of somatic mutations, with many genomic regions being specifically conserved but displaying a high function potential. This, together with the observed more efficient DNA repair ability in the long-lived individuals, supports the existence of key genomic regions for human survival during aging, with their integrity being of essential to human longevity.

Structural, static, and dynamic functional MRI predictors for conversion from mild cognitive impairment to Alzheimer's disease: Inter-cohort validation of Shanghai Memory Study and ADNI.

Mild cognitive impairment (MCI) is a critical prodromal stage of Alzheimer's disease (AD), and the mechanism underlying the conversion is not fully explored. Construction and inter-cohort validation of imaging biomarkers for predicting MCI conversion is of great challenge at present, due to lack of longitudinal cohorts and poor reproducibility of various study-specific imaging indices. We proposed a novel framework for inter-cohort MCI conversion prediction, involving comparison of structural, static, and dynamic functional brain features from structural magnetic resonance imaging (sMRI) and resting-state functional MRI (fMRI) between MCI converters (MCI_C) and non-converters (MCI_NC), and support vector machine for construction of prediction models. A total of 218 MCI patients with 3-year follow-up outcome were selected from two independent cohorts: Shanghai Memory Study cohort for internal cross-validation, and Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort for external validation. In comparison with MCI_NC, MCI_C were mainly characterized by atrophy, regional hyperactivity and inter-network hypo-connectivity, and dynamic alterations characterized by regional and connectional instability, involving medial temporal lobe (MTL), posterior parietal cortex (PPC), and occipital cortex. All imaging-based prediction models achieved an area under the curve (AUC) > 0.7 in both cohorts, with the multi-modality MRI models as the best with excellent performances of AUC > 0.85. Notably, the combination of static and dynamic fMRI resulted in overall better performance as relative to static or dynamic fMRI solely, supporting the contribution of dynamic features. This inter-cohort validation study provides a new insight into the mechanisms of MCI conversion involving brain dynamics, and paves a way for clinical use of structural and functional MRI biomarkers in future.

Joint radiomics and spatial distribution model for MRI-based discrimination of multiple sclerosis, neuromyelitis optica spectrum disorder, and myelin-oligodendrocyte-glycoprotein-IgG-associated disorder.

OBJECTIVE: To develop a discrimination pipeline concerning both radiomics and spatial distribution features of brain lesions for discrimination of multiple sclerosis (MS), aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorder (NMOSD), and myelin-oligodendrocyte-glycoprotein-IgG-associated disorder (MOGAD). METHODS: Hyperintensity T2 lesions were delineated in 212 brain MRI scans of MS (n = 63), NMOSD (n = 87), and MOGAD (n = 45) patients. To avoid the effect of fixed training/test dataset sampling when developing machine learning models, patients were allocated into 4 sub-groups for cross-validation. For each scan, 351 radiomics and 27 spatial distribution features were extracted. Three models, i.e., multi-lesion radiomics, spatial distribution, and joint models, were constructed using random forest and logistic regression algorithms for differentiating: MS from the others (MS models) and MOGAD from NMOSD (MOG-NMO models), respectively. Then, the joint models were combined with demographic characteristics (i.e., age and sex) to create MS and MOG-NMO discriminators, respectively, based on which a three-disease discrimination pipeline was generated and compared with radiologists. RESULTS: For classification of both MS-others and MOG-NMO, the joint models performed better than radiomics or spatial distribution model solely. The MS discriminator achieved AUC = 0.909 ± 0.027 and bias-corrected C-index = 0.909 ± 0.027, and the MOG-NMO discriminator achieved AUC = 0.880 ± 0.064 and bias-corrected C-index = 0.883 ± 0.068. The three-disease discrimination pipeline differentiated MS, NMOSD, and MOGAD patients with 75.0% accuracy, prominently outperforming the three radiologists (47.6%, 56.6%, and 66.0%). CONCLUSIONS: The proposed pipeline integrating multi-lesion radiomics and spatial distribution features could effectively differentiate MS, NMOSD, and MOGAD. CLINICAL RELEVANCE STATEMENT: The discrimination pipeline merging both radiomics and spatial distribution features of brain lesions may facilitate the differential diagnoses of multiple sclerosis, neuromyelitis optica spectrum disorder, and myelin-oligodendrocyte-glycoprotein-IgG-associated disorder. KEY POINTS: • Our study introduces an approach by combining radiomics and spatial distribution models. • The joint model exhibited superior performance in distinguishing multiple sclerosis from aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorder and myelin-oligodendrocyte-glycoprotein-IgG-associated disorder as well as discriminating the latter two diseases. • The three-disease discrimination pipeline showcased remarkable accuracy, surpassing the performance of experienced radiologists, highlighting its potential as a valuable diagnostic tool.

Altered Neurovascular Coupling in Patients With Mitochondrial Myopathy, Encephalopathy, Lactic Acidosis, and Stroke-Like Episodes (MELAS): A Combined Resting-State fMRI and Arterial Spin Labeling Study.

BACKGROUND: Coupling between neuronal activity and blood perfusion is termed neurovascular coupling (NVC), and it provides a potentially new mechanistic perspective into understanding numerous brain diseases. Although abnormal brain activity and blood supply have been separately reported in mitochondrial myopathy, encephalopathy, lactic acidosis, and stroke-like episodes (MELAS), whether anomalous NVC would be present is unclear. PURPOSE: To investigate NVC changes and potential neural basis in MELAS by combining resting-state functional MRI (rs-fMRI) and arterial spin labeling (ASL). STUDY TYPE: Prospective. SUBJECTS: Twenty-four patients with MELAS (age: 29.8 ± 7.3 years) in the acute stage and 24 healthy controls (HCs, age: 26.4 ± 8.1 years). Additionally, 12 patients in the chronic stage were followed up. FIELD STRENGTH/SEQUENCE: 3.0 T, resting-state gradient-recalled echo-planar imaging and pseudo-continuous 3D ASL sequences. ASSESSMENT: Amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo), and functional connectivity strength (FCS) were calculated from rs-fMRI, and cerebral blood flow (CBF) was computed from ASL. Global NVC was assessed by correlation coefficients of CBF-ALFF, CBF-fALFF, CBF-ReHo, and CBF-FCS. Regional NVC was also evaluated by voxel-wise and lesion-wise ratios of CBF/ALFF, CBF/fALFF, CBF/ReHo, and CBF/FCS. STATISTICAL TESTS: Two-sample t-test, paired-sample t-test, Gaussian random fields correction. A P value <0.05 was considered statistically significant. RESULTS: Compared with HC, MELAS patients in acute stage showed significantly reduced global CBF-ALFF, CBF-fALFF, CBF-ReHo, and CBF-FCS coupling (P < 0.001). Altered CBF/ALFF, CBF/fALFF, CBF/ReHo, and CBF/FCS ratios were found mainly distributed in the middle cerebral artery territory in MELAS patients. In addition, significantly increased NVC ratios were found in the acute stroke-like lesions in acute stage (P < 0.001), with a recovery trend in chronic stage. DATA CONCLUSIONS: This study showed dynamic alterations in NVC in MELAS patients from acute to chronic stage, which may provide a novel insight for understanding the pathogenesis of MELAS. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 1.

Anterior cruciate ligament femoral footprint is oblong-ovate, triangular, or two-tears shaped in healthy young adults: three-dimensional MRI analysis.

PURPOSE: This study aimed to evaluate the morphology of the anterior cruciate ligament (ACL) femoral footprint with three-dimensional magnetic resonance imaging (3D MRI) in healthy knees. METHODS: Fifty subjects with healthy knees were recruited, utilising 3D-SPACE sequences for ACL evaluation. The ACL was manually segmented, and the shape, size and location of the ACL femoral footprint were evaluated on a reformatted oblique-sagittal plane, which aligned closely with the ACL attachment. Statistical analysis included one-way ANOVA for continuous variables and Fisher's exact test for categorical variables, with a P value < 0.05 considered significant. RESULTS: Three types of ACL femoral footprint shape were identified, namely, oblong-ovate (OO) in 33 knees (66%), triangular (Tr) in 12 knees (24%) and two-tears (TT) in 5 knees (10%), with the mean areas being 58, 47 and 68 mm2, respectively. Within group TT, regions with similar sizes but different locations were identified: high tear (TT-H) and low tear (TT-L). Notably, group OO demonstrated a larger notch height index, whilst group TT was characterised by a larger α angle and lateral femoral condyle index. A noticeable variation was observed in the location of the femoral footprint centre across groups, with group TT-L and group Tr showing a more distal position relative to the apex of the deep cartilage. According to the Bernard and Hertel (BH) grid, the ACL femoral footprint centres in group TT-L exhibited a shallower and higher position than other groups. Furthermore, compared to group OO and TT-H, group Tr showed a significantly higher position according to the BH grid. CONCLUSION: In this study, the morphology of the ACL femoral footprint in healthy young adults was accurately evaluated using 3D MRI, revealing three distinct shapes: OO, Tr and TT. The different ACL femoral footprint types showed similar areas but markedly different locations. These findings emphasise the necessity of considering both the shape and precise location of the ACL femoral footprint during clinical assessments, which might help surgeons enhance patient-specific surgical plans before ACL reconstruction. LEVEL OF EVIDENCE: IV.

Online decision tools for personalized survival prediction and treatment optimization in elderly patients with lung squamous cell carcinoma: a retrospective cohort study.

BACKGROUND: Despite major advances in cancer therapeutics, the therapeutic options of Lung Squamous Cell Carcinoma (LSCC)-specific remain limited. Furthermore, the current staging system is imperfect for defining a prognosis and guiding treatment due to its simplicity and heterogeneity. We sought to develop prognostic decision tools for individualized survival prediction and treatment optimization in elderly patients with LSCC. METHODS: Clinical data of 4564 patients (stageIB-IIIB) diagnosed from 2010 to 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database for prognostic nomograms development. The proposed models were externally validated using a separate group consisting of 1299 patients (stage IB-IIIB) diagnosed from 2012-2015 in China. The prognostic performance was measured using the concordance index (C-index), calibration curves, the average time-dependent area under the receiver operator characteristic curves (AUC), and decision curve analysis. RESULTS: Eleven candidate prognostic variables were identified by the univariable and multivariable Cox regression analysis. The calibration curves showed satisfactory agreement between the actual and nomogram-estimated Lung Cancer-Specific Survival (LCSS) rates. By calculating the c-indices and average AUC, our nomograms presented a higher prognostic accuracy than the current staging system. Clinical usefulness was revealed by the decision curve analysis. User-friendly online decision tools integrating proposed nomograms were created to estimate survival for patients with different treatment regimens. CONCLUSIONS: The decision tools for individualized survival prediction and treatment optimization might facilitate clinicians with decision-making, medical teaching, and experimental design. Online tools are expected to be integrated into clinical practice by using the freely available website ( https://loyal-brand-611803.framer.app/ ).

Sex Differences in Prevalence and Clinical Correlates of Depression in Individuals Infected with Omicron in China.

Purpose: Sex differences in depression have been well recognized. However, sex differences in depression among Omicron-infected individuals have received little systematic study. This study compared sex differences in depression in infected individuals during the 2022 Omicron pandemic in China. Patients and Methods: 506 individuals infected with Omicron (males/females = 268/238) were recruited from Tianjin and Shanghai in China. Self-developed Scale of Demographics were used to collect demographic and clinical data, Zung Self-Rating Depression Scale (SDS), Zung Self-Rating Anxiety Scale (SAS), the Connor-Davidson Resilience Scale (CD-RISC), De Jong Gierveld Scale (DJGLS), and the Penn State Worry Questionnaire (PSWQ) were used to measure respondents' depression, anxiety, resilience, loneliness and worry, respectively. Results: The prevalence rate of depression in male patients was significantly higher than in female patients (42.2% versus 31.9%; χ2 = 5.64, p = 0.018). Regression analysis showed that in female patients, depression was associated with anxiety [OR = 1.26, 95% CI (1.16-1.36), p < 0.001], and resilience [OR = 0.98, 95% CI (0.96-1.00), p < 0.05], while in male patients, depression was associated with anxiety [OR = 1.24, 95% CI (1.15-1.33), p < 0.001]. Conclusion: This on-site study demonstrates that depression is more frequent in male than female Omicron-infected patients and suggests that sex differences should be considered in prevention and treatment strategies for depression during the Omicron pandemic.

Value of a combined magnetic resonance imaging-based radiomics-clinical model for predicting extracapsular extension in prostate cancer: a preliminary study.

Background: Extracapsular extension (ECE) of prostate cancer (PCa) is closely related to the treatment and prognosis of patients, and radiomics has been widely used in the study of PCa. This study aimed to evaluate the value of a combined model considering magnetic resonance imaging (MRI)-based radiomics and clinical parameters for predicting ECE in PCa. Methods: A total of 392 PCa patients enrolled in this retrospective study were randomly divided into the training and validation sets at a ratio of 7:3. Radiologists assessed all lesions by Mehralivand grade. Radiomics features were extracted and selected to build a radiomics model, while clinical parameters were noted to construct the clinical model. The combined model was constructed by the integration of the radiomics model and clinical model. Meanwhile, the nomogram for predicting ECE was constructed based on the combined model. Then, the area under the receiver operating characteristic (ROC) curve (AUC), Delong test and the decision curve analysis (DCA) were used to compare the performance among the combined model, radiomics model, clinical model and Mehralivand grade. Results: The AUC of the combined model in the validation set was comparable to that of the radiomics model [AUC =0.894 (95% confidence interval (CI): 0.837-0.950) vs. 0.835 (95% CI: 0.763-0.908), P>0.05]. In addition, the sensitivity of the combined model and radiomics model was 90.7% and 77.8%, with an accuracy of 81.4% and 76.3%, respectively. On the other hand, the AUCs of the Mehralivand grade of radiologists and clinical model were 0.774 (95% CI: 0.691-0.857) and 0.749 (95% CI: 0.658-0.840), respectively, in the validation set, which were lower than those in the combined model (P<0.05). The DCA implied that the combined model could obtain the maximum net clinical benefits compared with the clinical model, the Mehralivand grade and radiomics model. Conclusions: The combined model has a satisfactory predictive value for ECE in PCa patients compared with the clinical model, Mehralivand grade of radiologists, and the radiomics model.

Appropriateness and Comprehensiveness of Using ChatGPT for Perioperative Patient Education in Thoracic Surgery in Different Language Contexts: Survey Study.

BACKGROUND: ChatGPT, a dialogue-based artificial intelligence language model, has shown promise in assisting clinical workflows and patient-clinician communication. However, there is a lack of feasibility assessments regarding its use for perioperative patient education in thoracic surgery. OBJECTIVE: This study aimed to assess the appropriateness and comprehensiveness of using ChatGPT for perioperative patient education in thoracic surgery in both English and Chinese contexts. METHODS: This pilot study was conducted in February 2023. A total of 37 questions focused on perioperative patient education in thoracic surgery were created based on guidelines and clinical experience. Two sets of inquiries were made to ChatGPT for each question, one in English and the other in Chinese. The responses generated by ChatGPT were evaluated separately by experienced thoracic surgical clinicians for appropriateness and comprehensiveness based on a hypothetical draft response to a patient's question on the electronic information platform. For a response to be qualified, it required at least 80% of reviewers to deem it appropriate and 50% to deem it comprehensive. Statistical analyses were performed using the unpaired chi-square test or Fisher exact test, with a significance level set at P<.05. RESULTS: The set of 37 commonly asked questions covered topics such as disease information, diagnostic procedures, perioperative complications, treatment measures, disease prevention, and perioperative care considerations. In both the English and Chinese contexts, 34 (92%) out of 37 responses were qualified in terms of both appropriateness and comprehensiveness. The remaining 3 (8%) responses were unqualified in these 2 contexts. The unqualified responses primarily involved the diagnosis of disease symptoms and surgical-related complications symptoms. The reasons for determining the responses as unqualified were similar in both contexts. There was no statistically significant difference (34/37, 92% vs 34/37, 92%; P=.99) in the qualification rate between the 2 language sets. CONCLUSIONS: This pilot study demonstrates the potential feasibility of using ChatGPT for perioperative patient education in thoracic surgery in both English and Chinese contexts. ChatGPT is expected to enhance patient satisfaction, reduce anxiety, and improve compliance during the perioperative period. In the future, there will be remarkable potential application for using artificial intelligence, in conjunction with human review, for patient education and health consultation after patients have provided their informed consent.

Risk prediction of gestational diabetes mellitus in women with polycystic ovary syndrome based on a nomogram model.

Women with polycystic ovary syndrome are prone to develop gestational diabetes mellitus, a disease which may have significant impact on the postpartum health of both mother and infant. We performed a retrospective cohort study to develop and test a model that could predict gestational diabetes mellitus in the first trimester in women with polycystic ovary syndrome. Our study included 434 pregnant women who were referred to the obstetrics department between December 2017 and March 2020 with a diagnosis of polycystic ovary syndrome. Of these women, 104 were diagnosed with gestational diabetes mellitus in the second trimester. Univariate analysis revealed that in the first trimester, Hemoglobin A1c (HbA1C), age, total cholesterol(TC), low-density lipoprotein cholesterol (LDL-C), SBP (systolic blood pressure), family history, body mass index (BMI), and testosterone were predictive factors of gestational diabetes mellitus (P < 0.05). Logistic regression revealed that TC, age, HbA1C, BMI and family history were independent risk factors for gestational diabetes mellitus. The area under the ROC curve of the gestational diabetes mellitus risk prediction model was 0.937 in this retrospective analysis, demonstrating a great discriminatory ability. The sensitivity and specificity of the prediction model were 0.833 and 0.923, respectively. The Hosmer-Lemeshow test also showed that the model was well calibrated.

Mitogenome evidence shows two radiation events and dispersals of matrilineal ancestry from northern coastal China to the Americas and Japan.

Although it is widely recognized that the ancestors of Native Americans (NAs) primarily came from Siberia, the link between mitochondrial DNA (mtDNA) lineage D4h3a (typical of NAs) and D4h3b (found so far only in East China and Thailand) raises the possibility that the ancestral sources for early NAs were more variegated than hypothesized. Here, we analyze 216 contemporary (including 106 newly sequenced) D4h mitogenomes and 39 previously reported ancient D4h data. The results reveal two radiation events of D4h in northern coastal China, one during the Last Glacial Maximum and the other within the last deglaciation, which facilitated the dispersals of D4h sub-branches to different areas including the Americas and the Japanese archipelago. The coastal distributions of the NA (D4h3a) and Japanese lineages (D4h1a and D4h2), in combination with the Paleolithic archaeological similarities among Northern China, the Americas, and Japan, lend support to the coastal dispersal scenario of early NAs.

Longevity-Associated Transcription Factor ATF7 Promotes Healthspan by Suppressing Cellular Senescence and Systematic Inflammation.

Aging is characterized by persistent low-grade systematic inflammation, which is largely responsible for the occurrence of various age-associated diseases. We and others have previously reported that long-lived people (such as centenarians) can delay the onset of or even escape certain major age-related diseases. Here, by screening blood transcriptome and inflammatory profiles, we found that long-lived individuals had a relatively lower inflammation level (IL6, TNFα), accompanied by up-regulation of activating transcription factor 7 (ATF7). Interestingly, ATF7 expression was gradually reduced during cellular senescence. Loss of ATF7 induced cellular senescence, while overexpression delayed senescence progress and senescence-associated secretory phenotype (SASP) secretion. We showed that the anti-senescence effects of ATF7 were achieved by inhibiting nuclear factor kappa B (NF-κB) signaling and increasing histone H3K9 dimethylation (H3K9me2). In Caenorhabditis elegans, ATF7 overexpression significantly suppressed aging biomarkers and extended lifespan. Our findings suggest that ATF7 is a longevity-promoting factor that lowers cellular senescence and inflammation in long-lived individuals.