Deep learning-based computer-aided diagnosis system for the automatic detection and classification of lateral cervical lymph nodes on original ultrasound images of papillary thyroid carcinoma: a prospective diagnostic study.

PURPOSE: This study aims to develop a deep learning-based computer-aided diagnosis (CAD) system for the automatic detection and classification of lateral cervical lymph nodes (LNs) on original ultrasound images of papillary thyroid carcinoma (PTC) patients. METHODS: A retrospective data set of 1801 cervical LN ultrasound images from 1675 patients with PTC and a prospective test set including 185 images from 160 patients were collected. Four different deep leaning models were trained and validated in the retrospective data set. The best model was selected for CAD system development and compared with three sonographers in the retrospective and prospective test sets. RESULTS: The Deformable Detection Transformer (DETR) model showed the highest diagnostic efficacy, with a mean average precision score of 86.3% in the retrospective test set, and was therefore used in constructing the CAD system. The detection performance of the CAD system was superior to the junior sonographer and intermediate sonographer with accuracies of 86.3% and 92.4% in the retrospective and prospective test sets, respectively. The classification performance of the CAD system was better than all sonographers with the areas under the curve (AUCs) of 94.4% and 95.2% in the retrospective and prospective test sets, respectively. CONCLUSIONS: This study developed a Deformable DETR model-based CAD system for automatically detecting and classifying lateral cervical LNs on original ultrasound images, which showed excellent diagnostic efficacy and clinical utility. It can be an important tool for assisting sonographers in the diagnosis process.

Acupressure: a possible therapeutic strategy for anxiety related to COVID-19: a meta-analysis of randomized controlled trials.

Background: From the end of 2019 to December 2023, the world grappled with the COVID-19 pandemic. The scope and ultimate repercussions of the pandemic on global health and well-being remained uncertain, ushering in a wave of fear, anxiety, and worry. This resulted in many individuals succumbing to fear and despair. Acupoint massage emerged as a safe and effective alternative therapy for anxiety relief. However, its efficacy was yet to be extensively backed by evidence-based medicine. This study aimed to enhance the clinical effectiveness of acupoint massage and extend its benefits to a wider population. It undertakes a systematic review of the existing randomized controlled trials (RCTs) assessing the impact of acupoint massage on anxiety treatment, discussing its potential benefits and implications. This research aims to furnish robust evidence supporting anxiety treatment strategies for patients afflicted with COVID-19 disease and spark new approaches to anxiety management. Objectives: This study evaluates the evidence derived from randomised controlled trials (RCTs), quantifies the impact of acupressure on anxiety manifestations within the general population, and proposes viable supplementary intervention strategies for managing COVID-19 related anxiety. Materials and methods: This review included RCTs published between February 2014 and July 2023, that compared the effects of acupressure with sham control in alleviating anxiety symptomatology as the outcome measure. The studies were sourced from the multiple databases, including CINAHL, EBM Reviews, Embase, Medline, PsycINFO, Scopus and Web of Science. A meta-analysis was performed on the eligible studies, and an overall effect size was computed specifically for the anxiety outcome. The Cochrane Collaboration Bias Risk Assessment Tool (RevMan V5.4) was employed to assess bias risk, data integration, meta-analysis, and subgroup analysis. The mean difference, standard mean deviation, and binary data were used to represent continuous outcomes. Results: Of 1,110 studies of potential relevance, 39 met the criteria for inclusion in the meta-analysis. The majority of the studies reported a positive effect of acupressure in assuaging anticipatory anxiety about treatment. Eighteen studies were evaluated using the STAI scale. The acupressure procedures were thoroughly documented, and studies exhibited a low risk of bias. The cumulative results of the 18 trials showcased a more substantial reduction in anxiety in the acupressure group compared to controls (SMD = -5.39, 95% CI -5.61 to -5.17, p < 0.01). A subsequent subgroup analysis, based on different interventions in the control group, demonstrated improvement in anxiety levels with sham acupressure in improving changes in anxiety levels (SMD -1.61, 95% CI: -2.34 to -0.87, p < 0.0001), and blank controls (SMD -0.92, 95% CI: -2.37 to 0.53, p = 0.22). Conclusion: In the clinical research of traditional Chinese medicine treatment of anxiety, acupressure demonstrated effectiveness in providing instant relief from anxiety related to multiple diseases with a medium effect size. Considering the increasing incidence of anxiety caused by long COVID, the widespread application of acupressure appears feasible. However, the results were inconsistent regarding improvements on physiological indicators, calling for more stringent reporting procedures, including allocation concealment, to solidify the findings.

Variations in species diversity patterns and community assembly rules among vegetation types in the karst landscape.

The various vegetation types in the karst landscape have been considered the results of heterogeneous habitats. However, the lack of a comprehensive understanding of regional biodiversity patterns and the underlying ecological processes limits further research on ecological management. This study established forest dynamic plots (FDPs) of the dominant vegetation types (shrubland, SL; mixed tree and shrub forest, MTSF; coniferous forest, CF; coniferous broadleaf mixed forest, CBMF; and broadleaf forest, BF) in the karst landscape and quantified the species diversity patterns and potential ecological processes. The results showed that in terms of diversity patterns, the evenness and species richness of the CF community were significantly lower than other vegetation types, while the BF community had the highest species richness. The other three vegetation types showed no significant variation in species richness and evenness. However, when controlling the number of individuals of FDPs, the rarefied species richness showed significant differences and ranked as BF > SL > MTSF > CBMF > CF, highlighting the importance of considering the impacts of abundance. Additionally, the community assembly of climax communities (CF or BF) was dominated by stochastic processes such as species dispersal or species formation, whereas deterministic processes (habitat filtering) dominated the secondary forests (SL, MTSF, and CBMF). These findings proved that community assembly differs mainly between the climax community and other communities. Hence, it is crucial to consider the biodiversity and of the potential underlying ecological processes together when studying regional ecology and management, particularly in heterogeneous ecosystems.

Recent Advances in Signaling Pathways and Kinase Inhibitors for Leukemia Chemotherapy.

Leukemia is a malignant clonal disease of hematopoietic stem cells, which accounts for about 3% of the total incidence of tumors and is particularly prevalent among children and adolescents. It mainly includes four types of leukemia, namely ALL, AML, CLL, and CML, which are often aggressive and challenging diseases to treat. Several signaling pathways are dysregulated in almost all types of leukemia, such as JAK, PI3K, and MAPK, and others are dysregulated in specific types of leukemia, like Wnt/ß-catenin, Hedgehog, FLT3, Bcr-Abl, and so on. Many efforts have been devoted to developing small molecule inhibitors targeting protein kinases involved in leukemia-related signaling pathways. In this review, we focus on the study of signaling pathways and protein kinases that developed as targets of anti-leukemia drug therapy and report the research progress of relevant small molecule kinase inhibitors over the last five years.

PGR-KITLG signaling drives a tumor-mast cell regulatory feedback to modulate apoptosis of breast cancer cells.

The immune microenvironment constructed by tumor-infiltrating immune cells and the molecular phenotype defined by hormone receptors (HRs) have been implicated as decisive factors in the regulation of breast cancer (BC) progression. Here, we found that the infiltration of mast cells (MCs) informed impaired prognoses in HR(+) BC but predicted improved prognoses in HR(-) BC. However, molecular features of MCs in different BC remain unclear. We next discovered that HR(-) BC cells were prone to apoptosis under the stimulation of MCs, whereas HR(+) BC cells exerted anti-apoptotic effects. Mechanistically, in HR(+) BC, the KIT ligand (KITLG), a major mast cell growth factor in recruiting and activating MCs, could be transcriptionally upregulated by the progesterone receptor (PGR), and elevate the production of MC-derived granulin (GRN). GRN attenuates TNFα-induced apoptosis in BC cells by competitively binding to TNFR1. Furthermore, disruption of PGR-KITLG signaling by knocking down PGR or using the specific KITLG-cKIT inhibitor iSCK03 potently enhanced the sensitivity of HR(+) BC cells to MC-induced apoptosis and exerted anti-tumor activity. Collectively, these results demonstrate that PGR-KITLG signaling in BC cells preferentially induces GRN expression in MCs to exert anti-apoptotic effects, with potential value in developing precision medicine approaches for diagnosis and treatment.

Functional brain network controllability dysfunction in Alzheimer's disease and its relationship with cognition and gene expression profiling.

Objective. In recent studies, network control theory has been applied to clarify transitions between brain states, emphasizing the significance of assessing the controllability of brain networks in facilitating transitions from one state to another. Despite these advancements, the potential alterations in functional network controllability associated with Alzheimer's disease (AD), along with the underlying genetic mechanisms responsible for these alterations, remain unclear.Approach. We conducted a comparative analysis of functional network controllability measures between patients with AD (n= 64) and matched normal controls (NCs,n= 64). We investigated the association between altered controllability measures and cognitive function in AD. Additionally, we conducted correlation analyses in conjunction with the Allen Human Brain Atlas to identify genes whose expression was correlated with changes in functional network controllability in AD, followed by a set of analyses on the functional features of the identified genes.Main results. In comparison to NCs, patients with AD exhibited a reduction in average controllability, predominantly within the default mode network (DMN) (63% of parcellations), and an increase in average controllability within the limbic (LIM) network (33% of parcellations). Conversely, AD patients displayed a decrease in modal controllability within the LIM network (27% of parcellations) and an increase in modal controllability within the DMN (80% of parcellations). In AD patients, a significant positive correlation was found between the average controllability of the salience network and the mini-mental state examination scores. The changes in controllability measures exhibited spatial correlation with transcriptome profiles. The significant genes identified exhibited enrichment in neurobiologically relevant pathways and demonstrated preferential expression in various tissues, cell types, and developmental periods.Significance. Our findings have the potential to offer new insights into the genetic mechanisms underlying alterations in the controllability of functional networks in AD. Additionally, these results offered perspectives for a deeper understanding of the pathogenesis and the development of therapeutic strategies for AD.

The Effect of Micronutrients on Obese Phenotype of Adult Mice Is Dependent on the Experimental Environment.

The environment of the test laboratory affects the reproducibility of treatment effects on physiological phenotypes of rodents and may be attributed to the plasticity of the epigenome due to nutrient-gene-environment interactions. Here, we explored the reproducibility of adding a multi-vitamin-mineral (MVM) mix to a nutrient-balanced high-fat (HF) diet on obesity, insulin resistance (IR), and gene expression in the tissues of adult male mice. Experiments of the same design were conducted in three independent animal facilities. Adult C57BL/6J male mice were fed an HF diet for 6 weeks (diet induced-obesity model) and then continued for 9-12 weeks on the HF diet with or without 5-fold additions of vitamins A, B1, B6, B12, Zn, and 2-fold Se. The addition of the MVM affected body weight, fat mass, gene expression, and markers of IR in all three locations (p < 0.05). However, the direction of the main effects was influenced by the interaction with the experimental location and its associated environmental conditions known to affect the epigenome. In conclusion, MVM supplementation influenced phenotypes and expression of genes related to adipose function in obese adult male mice, but the experimental location and its associated conditions were significant interacting factors. Preclinical studies investigating the relationship between diet and metabolic outcomes should acknowledge the plasticity of the epigenome and implement measures to reproduce studies in different locations.

IInception-CBAM-IBiGRU based fault diagnosis method for asynchronous motors.

Aiming at the problems of insufficient extraction of asynchronous motor fault features by traditional deep learning algorithms and poor diagnosis of asynchronous motor faults in robust noise environments, this paper proposes an end-to-end fault diagnosis method for asynchronous motors based on IInception-CBAM-IBiGRU. The method first uses a signal-to-grayscale image conversion method to convert one-dimensional vibration signals into two-dimensional images and initially extracts shallow features through two-dimensional convolution; then the Improved Inception (IInception) module is used as a residual block to learning features at different scales with a residual structure, and extracts its important feature information through the Convolutional Block Attention Module (CBAM) to extract important feature information and adjust the weight parameters; then the feature information is input to the Improved Bi-directional Gate Recurrent Unit (IBiGRU) to extract its timing features further; finally, the fault identification is achieved by the SoftMax function. The primary hyperparameters in the model are optimized by the Weighted Mean Of Vectors Algorithm (INFO). The experimental results show that the method is effective in fault diagnosis of asynchronous motors, with an accuracy rate close to 100%, and can still maintain a high accuracy rate under the condition of low noise ratio, with good robustness and generalization ability.

Mitochondrial ferritin alleviates ferroptosis in a kainic acid-induced mouse epilepsy model by regulating iron homeostasis: Involvement of nuclear factor erythroid 2-related factor 2.

BACKGROUND: Epilepsy is a widespread and chronic disease of the central nervous system caused by a variety of factors. Mitochondrial ferritin (FtMt) refers to ferritin located within the mitochondria that may protect neurons against oxidative stress by binding excess free iron ions in the cytoplasm. However, the potential role of FtMt in epilepsy remains unclear. We aimed to investigate whether FtMt and its related mechanisms can regulate epilepsy by modulating ferroptosis. METHODS: Three weeks after injection of adeno-associated virus (AAV) in the skull of adult male C57BL/6 mice, kainic acid (KA) was injected into the hippocampus to induce seizures. Primary hippocampal neurons were transfected with siRNA using a glutamate-mediated epilepsy model. After specific treatments, Western blot analysis, immunofluorescence, EEG recording, transmission electron microscopy, iron staining, silver staining, and Nissl staining were performed. RESULTS: At different time points after KA injection, the expression of FtMt protein in the hippocampus of mice showed varying degrees of increase. Knockdown of the FtMt gene by AAV resulted in an increase in intracellular free iron levels and a decrease in the function of iron transport-related proteins, promoting neuronal ferroptosis and exacerbating epileptic brain activity in the hippocampus of seizure mice. Additionally, increasing the expression level of FtMt protein was achieved by AAV-mediated upregulation of nuclear factor erythroid 2-related factor 2 (Nrf2) gene in the hippocampus of seizure mice. CONCLUSIONS: In epilepsy, Nrf2 modulates ferroptosis by involving the expression of FtMt and may be a potential therapeutic mechanism of neuronal injury after epilepsy. Targeting this relevant process for treatment may be a therapeutic strategy to prevent epilepsy.

Application of deep-learning to the automatic segmentation and classification of lateral lymph nodes on ultrasound images of papillary thyroid carcinoma.

PURPOSE: It is crucial to preoperatively diagnose lateral cervical lymph node (LN) metastases (LNMs) in papillary thyroid carcinoma (PTC) patients. This study aims to develop deep-learning models for the automatic segmentation and classification of LNM on original ultrasound images. METHODS: This study included 1000 lateral cervical LN ultrasound images (consisting of 512 benign and 558 metastatic LNs) collected from 728 patients at the Chongqing General Hospital between March 2022 and July 2023. Three instance segmentation models (MaskRCNN, SOLO and Mask2Former) were constructed to segment and classify ultrasound images of lateral cervical LNs by recognizing each object individually and in a pixel-by-pixel manner. The segmentation and classification results of the three models were compared with an experienced sonographer in the test set. RESULTS: Upon completion of a 200-epoch learning cycle, the loss among the three unique models became negligible. To evaluate the performance of the deep-learning models, the intersection over union threshold was set at 0.75. The mean average precision scores for MaskRCNN, SOLO and Mask2Former were 88.8%, 86.7% and 89.5%, respectively. The segmentation accuracies of the MaskRCNN, SOLO, Mask2Former models and sonographer were 85.6%, 88.0%, 89.5% and 82.3%, respectively. The classification AUCs of the MaskRCNN, SOLO, Mask2Former models and sonographer were 0.886, 0.869, 0.90.2 and 0.852 in the test set, respectively. CONCLUSIONS: The deep learning models could automatically segment and classify lateral cervical LNs with an AUC of 0.92. This approach may serve as a promising tool to assist sonographers in diagnosing lateral cervical LNMs among patients with PTC.

Atypical dynamic network reconfiguration and genetic mechanisms in patients with major depressive disorder.

BACKGROUND: Brain dynamics underlie complex forms of flexible cognition or the ability to shift between different mental modes. However, the precise dynamic reconfiguration based on multi-layer network analysis and the genetic mechanisms of major depressive disorder (MDD) remains unclear. METHODS: Resting-state functional magnetic resonance imaging (fMRI) data were acquired from the REST-meta-MDD consortium, including 555 patients with MDD and 536 healthy controls (HC). A time-varying multi-layer network was constructed, and dynamic modular characteristics were used to investigate the network reconfiguration. Additionally, partial least squares regression analysis was performed using transcriptional data provided by the Allen Human Brain Atlas (AHBA) to identify genes associated with atypical dynamic network reconfiguration in MDD. RESULTS: In comparison to HC, patients with MDD exhibited lower global and local recruitment coefficients. The local reduction was particularly evident in the salience and subcortical networks. Spatial transcriptome correlation analysis revealed an association between gene expression profiles and atypical dynamic network reconfiguration observed in MDD. Further functional enrichment analyses indicated that positively weighted reconfiguration-related genes were primarily associated with metabolic and biosynthetic pathways. Additionally, negatively enriched genes were predominantly related to programmed cell death-related terms. CONCLUSIONS: Our findings offer robust evidence of the atypical dynamic reconfiguration in patients with MDD from a novel perspective. These results offer valuable insights for further exploration into the mechanisms underlying MDD.

Knowledge graph construction based on granulosa cells transcriptome from polycystic ovary syndrome with normoandrogen and hyperandrogen.

PCOS is a widespread disease that primarily caused in-pregnancy in pregnant-age women. Normoandrogen (NA) and Hyperandrogen (HA) PCOS are distinct subtypes of PCOS, while bio-markers and expression patterns for NA PCOS and HA PCOS have not been disclosed. We performed microarray analysis on granusola cells from NA PCOS, HA PCOS and normal tissue from 12 individuals. Afterwards, microarray data were processed and specific genes for NA PCOS and HA PCOS were identified. Further functional analysis selected IL6R and CD274 as new NA PCOS functional markers, and meanwhile selected CASR as new HA PCOS functional marker. IL6R, CD274 and CASR were afterwards experimentally validated on mRNA and protein level. Subsequent causal relationship analysis based on Apriori Rules Algorithm and co-occurrence methods identified classification markers for NA PCOS and HA PCOS. According to classification markers, downloaded transcriptome datasets were merged with our microarray data. Based on merged data, causal knowledge graph was constructed for NA PCOS or HA PCOS and female infertility on NA PCOS and HA PCOS. Gene-drug interaction analysis was then performed and drugs for HA PCOS and NA PCOS were predicted. Our work was among the first to indicate the NA PCOS and HA PCOS functional and classification markers and using markers to construct knowledge graphs and afterwards predict drugs for NA PCOS and HA PCOS based on transcriptome data. Thus, our study possessed biological and clinical value on further understanding the inner mechanism on the difference between NA PCOS and HA PCOS.

How do species richness and its component dependence vary along the natural restoration in extremely heterogeneous forest ecosystems?

Giant habitat heterogeneity is an important factor contributing to the high species richness (SR) in karst forests. Yet, the driving factor behind the alterations in SR patterns during natural restoration remains unclear. In this study, we established the forest dynamics plots along the natural restoration sequence (including shrub-tree mixed forest stage (SC), secondary forest stage (SG) and old-growth forest sage (OG)) in degraded karst forests to compare the SR and the dependence on its components (including total community abundance, species abundance distribution (SAD), and conspecific spatial aggregation (CSA)) among stages of natural restoration. By evaluating the degree of contribution of the components to local SR and rarefied SR, we found that the SG exhibited the highest local SR, while the rarefied SR remained increasing along the restoration sequence after controlling the sample size. At SC-SG stage, SAD and CSA contributed negatively to the differences in SR, while abundance made a positive contribution to SR differences. At SG-OG, abundance contributed positively to the difference in SR at all scales, while SAD contributed negatively at small scales. No significant contribution of CSA was found at observed scales. In addition, local SR varied more significantly with PIE than with abundance. Our research emphasizes the importance of eliminating the influence of abundance on species richness in forest ecology and management, as well as the significance of separately evaluating the components that shape the diversity patterns.

Crop residue burning in China (2019-2021): Spatiotemporal patterns, environmental impact, and emission dynamics.

Crop residue burning (CRB) is a major contributor to air pollution in China. Current fire detection methods, however, are limited by either temporal resolution or accuracy, hindering the analysis of CRB's diurnal characteristics. Here we explore the diurnal spatiotemporal patterns and environmental impacts of CRB in China from 2019 to 2021 using the recently released NSMC-Himawari-8 hourly fire product. Our analysis identifies a decreasing directionality in CRB distribution in the Northeast and a notable southward shift of the CRB center, especially in winter, averaging an annual southward movement of 7.5°. Additionally, we observe a pronounced skewed distribution in daily CRB, predominantly between 17:00 and 20:00. Notably, nighttime CRB in China for the years 2019, 2020, and 2021 accounted for 51.9%, 48.5%, and 38.0% respectively, underscoring its significant environmental impact. The study further quantifies the hourly emissions from CRB in China over this period, with total emissions of CO, PM10, and PM2.5 amounting to 12,236, 2,530, and 2,258 Gg, respectively. Our findings also reveal variable lag effects of CRB on regional air quality and pollutants across different seasons, with the strongest impacts in spring and more immediate effects in late autumn. This research provides valuable insights for the regulation and control of diurnal CRB before and after large-scale agricultural activities in China, as well as the associated haze and other pollution weather conditions it causes.

Transmembrane modification of tumor vascular targeting peptide A7R as molecular cargo delivery tool.

In recent years, targeting tumor angiogenesis has emerged as a prominent research focus in the treatment and prevention of tumor expansion. A7R (ATWLPPR) exhibits high affinity and specificity for VEGFR-2, which is overexpressed in various tumors. To enhance the tumor tissue and cell penetration capabilities of A7R, we substituted its non-critical amino acid with Arginine (R) and Glutamic acid (E), cyclized the mutant peptide, and linked it to the membrane permeation sequence using coordination principles. We designed and synthesized fifteen novel penetrating peptides that target tumor blood vessels and cells, followed by conducting various biological evaluations and cell imaging experiments. The results demonstrated that Cyclo-A7R-RRR and A7R-RLLRLLR exhibited excellent permeability towards tumor cells, with Cyclo-A7R-RRR showing superior serum stability compared to A7R. Furthermore, the modified peptides showed no toxicity towards HeLa cells, U251 cells, HuH-7 cells, and HEK293 cells under 10 µmol/L. Utilizing Cyclo-A7R-RRR or A7R-RLLRLLR for transmembrane delivery of drug molecules could significantly improve their efficacy. Our findings broaden the potential application scenarios of A7R in targeted tumor angiogenesis.

The diagnostic value of GICA used for intraoperative lymph node FNA-Tg measurement to evaluate thyroid cancer metastases.

Objective: It is crucial to diagnose lymph node (LN) metastases (LNM) before or during thyroid carcinoma surgery. Measurement of thyroglobulin (Tg) in the fine needle aspirate washout (FNA-Tg) is useful to assist in the diagnosis of LNM for papillary thyroid carcinoma (PTC). This study aimed to assess the diagnostic performance of a new technique based on a colloidal gold-based immunochromatographic assay (GICA) for intraoperative FNA-Tg in diagnosing LNM. Clinical trial information: This study is registered with chictr.org.cn, ID: ChiCTR2200063561 (registered 11 September, 2022). Methods: This prospective study enrolled 51 PTC patients who underwent cervical LN dissection. A total of 150 LNs dissected from the central and lateral compartments were evaluated by FNA-Tg-GICA at three different time points and compared with frozen sections and the conventional Tg measurement method electrochemiluminescence immunoassay (ECLIA). Receiver operating characteristic curve (ROC) and area under the curve (AUC), cutoff value to discriminate benign and malignant LNs, sensitivity, specificity, and accuracy were provided. Results: The cutoff value of FNA-Tg to predict LNM was 110.83 ng/mL for ECLIA and 13.19 ng/mL, 38.69 ng/mL, and 77.17 ng/mL for GICA at 3, 10, and 15 min, respectively. There was no significant difference between the AUCs of GICA at different time points compared to using ECLIA and frozen sections. Besides, the diagnostic performance of GICA and ECLIA showed no significant difference in evaluating LNM from central and lateral compartments or between the TgAb-positive subgroup and TgAb-negative subgroup. Conclusion: GICA is a promising method for intraoperative FNA-Tg measurement and has high value in predicting LNM. It may be a novel alternative or supplementary method to frozen section or ECLIA.

Functional connectome hierarchy dysfunction in Alzheimer's disease and its relationship with cognition and gene expression profiling.

Numerous researches have shown that the human brain organizes as a continuum axis crossing from sensory motor to transmodal cortex. Functional network alterations were commonly found in Alzheimer's disease (AD). Whether the hierarchy of AD brain networks has changed and how these changes related to gene expression profiling and cognition is unclear. Using resting-state functional magnetic resonance imaging data from 233 subjects (185 AD patients and 48 healthy controls), we studied the changes in the functional network gradients in AD. Moreover, we investigated the relationships between gradient alterations and cognition, and gene expression profiling, respectively. We found that the second gradient organizes as a continuum axis crossing from the sensory motor to the transmodal cortex. Compared to the healthy controls, the secondary gradient scores of the visual and somatomotor network (SOM) increased significantly in AD, and the secondary gradient scores of default mode and frontoparietal network decreased significantly in AD. The secondary gradient scores of SOM and salience network (SAL) significantly positively correlated with memory function in AD. The secondary gradient in SAL also significantly positively correlated with language function. The AD-related second gradient alterations were spatially associated with the gene expression and the relevant genes enriched in neurobiology-related pathways, specially expressed in various tissues, cell types, and developmental stages. These findings suggested the changes in the functional network gradients in AD and deepened our understanding of the correlation between macroscopic gradient structure and microscopic gene expression profiling in AD.

Surgical methods of total thyroidectomy for differentiated thyroid cancer: a systematic review and Bayesian network meta-analysis.

BACKGROUND: Emerging remote-access surgical methods are utilized to treat differentiated thyroid cancer. The study aimed to compare the surgical integrity, safety, efficacy, and postoperative experience of patients among common surgical methods. METHODS: The PubMed, Medline, Cochrane Library, Web of Science, and EMBASE databases were searched from their inception until March 2023. Pairwise meta-analysis and Bayesian network meta-analysis were performed. The surface under the cumulative ranking curve (SUCRA) was used to illuminate the probability that each method would be the best for each outcome. RESULTS: Thirty-two studies comprising 7042 patients were included. Robotic bilateral axillo-breast approach (RBABA) and robotic gasless transaxillary approach (RGAA) retrieved fewer lymph nodes (LNs) than open thyroidectomy (OT). RBABA showed a significantly lower permanent recurrent laryngeal nerve (RLN) palsy rate than OT. According to SUCRA values, endoscopic transoral approach (EOA) ranked the highest in retrieved LNs (0.84), the proportion of stimulated serum thyroglobulin less than 1.0 ng/ml (0.77), and the pain score (0.77). Endoscopic bilateral areola approach (EBAA) ranked the highest in the transient RLN palsy rate (0.72). The endoscopic gasless transaxillary approach (EGAA) ranked the highest in the transient hypoparathyroidism rate (0.78). RBABA ranked the highest in the rate of permanent RLN palsy (0.94) and hypoparathyroidism (0.77). OT ranked the highest in operative time (0.92). CONCLUSIONS: Each surgical method of total thyroidectomy has benefits and limitations. EOA performed the best in maintaining surgical integrality and reducing the pain score, while taking a long operative time. Generally, RBABA showed the best advantage in protecting parathyroid glands and RLN but with the longest operative time. OT had the best advantage in operative time. Therefore, OT and EOA are ideal methods for patients with a higher risk of central LN metastasis. RBABA and EOA may not be suitable for elderly patients or those with high anesthesia risk.

Resolving heterogeneity in Alzheimer's disease based on individualized structural covariance network.

The heterogeneity of Alzheimer's disease (AD) poses a challenge to precision medicine. We aimed to identify distinct subtypes of AD based on the individualized structural covariance network (IDSCN) analysis and to research the underlying neurobiology mechanisms. In this study, 187 patients with AD (age = 73.57 ± 6.00, 50% female) and 143 matched normal controls (age = 74.30 ± 7.80, 44% female) were recruited from the Alzheimer's Disease Neuroimaging Initiative (ADNI) project database, and T1 images were acquired. We utilized the IDSCN analysis to generate individual-level altered structural covariance network and performed k-means clustering to subtype AD based on structural covariance network. Cognition, disease progression, morphological features, and gene expression profiles were further compared between subtypes, to characterize the heterogeneity in AD. Two distinct AD subtypes were identified in a reproducible manner, and we named the two subtypes as slow progression type (subtype 1, n = 104, age = 76.15 ± 6.44, 42% female) and rapid progression type (subtype 2, n = 83, age = 71.98 ± 8.72, 47% female), separately. Subtype 1 had better baseline visuospatial function than subtype 2 (p < 0.05), whereas subtype 2 had better baseline memory function than subtype 1 (p < 0.05). Subtype 2 showed worse progression in memory (p = 0.003), language (p = 0.003), visuospatial function (p = 0.020), and mental state (p = 0.038) than subtype 1. Subtype 1 often shared increased structural covariance network, mainly in the frontal lobe and temporal lobe regions, whereas subtype 2 often shared increased structural covariance network, mainly in occipital lobe regions and temporal lobe regions. Functional annotation further revealed that all differential structural covariance network between the two AD subtypes were mainly implicated in memory, learning, emotion, and cognition. Additionally, differences in gray matter volume (GMV) between AD subtypes were identified, and genes associated with GMV differences were found to be enriched in the terms potassium ion transport, synapse organization, and histone modification and the pathways viral infection, neurodegeneration-multiple diseases, and long-term depression. The two distinct AD subtypes were identified and characterized with neuroanatomy, cognitive trajectories, and gene expression profiles. These comprehensive results have implications for neurobiology mechanisms and precision medicine.

Highly efficient MoS2/MXene aerogel for interfacial solar steam generation and wastewater treatment.

Interfacial solar steam generation using aerogels holds great promise for seawater desalination and wastewater treatment. However, to achieve aerogels with both durable, high-efficiency evaporation performance and excellent salt resistance remains challenging. Here, a molybdenum disulphide (MoS2) and MXene composite aerogel with vertical pore channels is reported, which has outstanding advantages in mechanical properties, water transportation, photothermal conversion, and recycling stability. Benefiting from the plasmon resonance effect of MXene and the excellent photothermal conversion performance of MoS2, the aerogel exhibits excellent light absorption (96.58 %). The aerogel is resistant to deformation and able to rebound after water absorption, because of the support of an ordered vertical structure. Moreover, combined with the low water evaporation enthalpy, low thermal conductivity, and super hydrophilicity, the aerogel achieves an efficient and stable evaporation rate of about 2.75 kg m-2h-1 under one sun and exhibits excellent self-cleaning ability. Notably, the evaporator achieves removal rates of 99.9 % for heavy metal ions and 100 % for organic dyes, which has great potential in applications including seawater desalination and wastewater purification.