Impact of virtual monochromatic images of different low-energy levels in dual-energy CT on radiomics models for predicting muscle invasion in bladder cancer.

OBJECTIVE: The purpose of this study was to investigate the impact of different low-energy virtual monochromatic images (VMIs) in dual-energy CT on the performance of radiomics models for predicting muscle invasive status in bladder cancer (BCa). MATERIALS AND METHODS: A total of 127 patients with pathologically proven muscle-invasive BCa (n = 49) and non-muscle-invasive BCa (n = 78) were randomly allocated into the training and test cohorts at a ratio of 7:3. Feature extraction was performed on the venous phase images reconstructed at 40, 50, 60 and 70-keV (single-energy analysis) or in combination (multi-energy analysis). Recursive feature elimination (RFE) and the least absolute shrinkage and selection operator (LASSO) were employed to select the most relevant features associated with BCa. Models were built using a support vector machine (SVM) classifier. Diagnostic performance was assessed through receiver operating characteristic curves, evaluating sensitivity, specificity, accuracy, precision, and the area-under-the curve (AUC) values. RESULTS: In the test cohort, the multi-energy model achieved the best diagnostic performance with AUC, sensitivity, specificity, accuracy, and precision of 0.917, 0.800, 0.833, 0.821, and 0.750, respectively. Conversely, the single-energy model exhibited lower AUC and sensitivity in predicting the muscle invasion status. CONCLUSIONS: By combining information from VMIs of various energies, the multi-energy model displays superior performance in preoperatively predicting the muscle invasion status of bladder cancer.

Effect of fully automatic classification model from different tube voltage images on bone density screening: A self-controlled study.

PURPOSE: To develop two bone status prediction models combining deep learning and radiomics based on standard-dose chest computed tomography (SDCT) and low-dose chest computed tomography (LDCT), and to evaluate the effect of tube voltage on reproducibility of radiomics features and predictive efficacy of these models. METHODS: A total of 1508 patients were enrolled in this retrospective study. LDCT was conducted using 80 kVp, tube current ranging from 100 to 475 mA. On the other hand, SDCT was performed using 120 kVp, tube current ranging from 100 to 520 mA. We developed an automatic thoracic vertebral cancellous bone (TVCB) segmentation model. Subsequently, 1184 features were extracted and two classifiers were developed based on LDCT and SDCT images. Based on the diagnostic results of quantitative computed tomography examination, the first-level classifier was initially developed to distinguish normal or abnormal BMD (including osteoporosis and osteopenia), while the second-level classifier was employed to identify osteoporosis or osteopenia. The Dice coefficient was used to evaluate the performance of the automated segmentation model. The Concordance Correlation Coefficients (CCC) of radiomics features were calculated between LDCT and SDCT, and the performance of these models was evaluated. RESULTS: Our automated segmentation model achieved a Dice coefficient of 0.98 ± 0.01 and 0.97 ± 0.02 in LDCT and SDCT, respectively. Alterations in tube voltage decreased the reproducibility of the extracted radiomic features, with 85.05 % of the radiomic features exhibiting low reproducibility (CCC < 0.75). The area under the curve (AUC) using LDCT-based and SDCT-based models was 0.97 ± 0.01 and 0.94 ± 0.02, respectively. Nonetheless, cross-validation with independent test sets of different tube voltage scans suggests that variations in tube voltage can impair the diagnostic efficacy of the model. Consequently, radiomics models are not universally applicable to images of varying tube voltages. In clinical settings, ensuring consistency between the tube voltage of the image used for model development and that of the acquired patient image is critical. CONCLUSIONS: Automatic bone status prediction models, utilizing either LDCT or SDCT images, enable accurate assessment of bone status. Tube voltage impacts reproducibility of features and predictive efficacy of models. It is necessary to account for tube voltage variation during the image acquisition.

Retrospective Characterization of Bone Histomorphometric Findings in Clinical Patient Specimens.

BACKGROUND: Bone histomorphometry provides comprehensive information on bone metabolism and microstructure. In this retrospective study, we aimed to obtain an overview of the typical indications, referring hospitals, and histomorphometric quantification-based diagnoses of the bone tissue in our histomorphometry laboratory, the only laboratory in Finland carrying out histomorphometric examination of clinical bone biopsies. METHODS: Between January 1, 2005 and December 31, 2020, 553 clinical bone biopsies were sent to our histomorphometry laboratory for histomorphometric examination. The median age of the patients was 55 years (range, 0.2-89.9 years), 51% of them were males, and 18% comprised pediatric patients. We received bone biopsy specimens from 23 hospitals or healthcare units. The majority of the samples we sent by nephrologists. RESULTS: The most common bone biopsy indications were suspicion of renal osteodystrophy (ROD), unknown bone turnover status in osteoporosis, and several or untypical fractures. The most common quantitative bone histomorphometry-based diagnosis was ROD. CONCLUSIONS: This study provides information on the clinical application of bone histomorphometry in Finland. Precise and quantitative ROD evaluation is the most common indication for bone histomorphometry, being crucial in clinical decision-making and targeted treatment of this patient group.

Simple Behavioral Analysis (SimBA) as a platform for explainable machine learning in behavioral neuroscience.

The study of complex behaviors is often challenging when using manual annotation due to the absence of quantifiable behavioral definitions and the subjective nature of behavioral annotation. Integration of supervised machine learning approaches mitigates some of these issues through the inclusion of accessible and explainable model interpretation. To decrease barriers to access, and with an emphasis on accessible model explainability, we developed the open-source Simple Behavioral Analysis (SimBA) platform for behavioral neuroscientists. SimBA introduces several machine learning interpretability tools, including SHapley Additive exPlanation (SHAP) scores, that aid in creating explainable and transparent behavioral classifiers. Here we show how the addition of explainability metrics allows for quantifiable comparisons of aggressive social behavior across research groups and species, reconceptualizing behavior as a sharable reagent and providing an open-source framework. We provide an open-source, graphical user interface (GUI)-driven, well-documented package to facilitate the movement toward improved automation and sharing of behavioral classification tools across laboratories.

Characterization of a novel adipose tissue located between abdominal lymph nodes and cervix/prostate in mice.

Perigonadal adipose tissue is a homogeneous white adipose tissue (WAT) in adult male mice, without any brown adipose tissue (BAT) present. However, there are congenital differences in the gonads between male and female mice. Whether heterogeneity existed in perigonadal ATs in female mice remains unknown. This study reported a perigonadal BAT located between abdominal lymph nodes and uterine cervix in female mice, termed lymph node-cervical adipose tissue (LNCAT). Its counterpart, lymph node-prostatic adipose tissue (LNPAT), exhibited white phenotype in adult virgin male mice. When exposed to cold, LNCAT/LNPAT increased UCP1 expression via activation of TH, in which abdominal lymph nodes were involved. Interestingly, the UCP1 expression in LNCAT/LNPAT varied under different reproductive stages. The UCP1 expression in LNCAT was upregulated at early pregnancy, declined at mid-late pregnancy, and reverted in weaning dams. Mating behavior stimulated LNPAT browning in male mice. We found that androgen but not estrogen or progesterone inhibited UCP1 expression in LNCAT. Androgen administration reversed the castration-induced LNPAT browning. Our results identified a perigonadal BAT in female mice and characterized its UCP1 expression patterns under various conditions.

Optimizing Antidepressant Efficacy: Multimodal Neuroimaging Biomarkers for Prediction of Treatment Response.

Major depressive disorder (MDD) is a common and often severe condition that profoundly diminishes quality of life for individuals across ages and demographic groups. Unfortunately, current antidepressant and psychotherapeutic treatments exhibit limited efficacy and unsatisfactory response rates in a substantial number of patients. The development of effective therapies for MDD is hindered by the insufficiently understood heterogeneity within the disorder and its elusive underlying mechanisms. To address these challenges, we present a target-oriented multimodal fusion framework that robustly predicts antidepressant response by integrating structural and functional connectivity data (sertraline: R2 = 0.31; placebo: R2 = 0.22). Through the model, we identify multimodal neuroimaging biomarkers of antidepressant response and observe that sertraline and placebo show distinct predictive patterns. We further decompose the overall predictive patterns into constitutive network constellations with generalizable structural-functional co-variation, which exhibit treatment-specific association with personality traits and behavioral/cognitive task performance. Our innovative and interpretable multimodal framework provides novel insights into the intricate neuropsychopharmacology of antidepressant treatment and paves the way for advances in precision medicine and development of more targeted antidepressant therapeutics.

Fully automated deep learning system for osteoporosis screening using chest computed tomography images.

Background: Osteoporosis, a disease stemming from bone metabolism irregularities, affects approximately 200 million people worldwide. Timely detection of osteoporosis is pivotal in grappling with this public health challenge. Deep learning (DL), emerging as a promising methodology in the field of medical imaging, holds considerable potential for the assessment of bone mineral density (BMD). This study aimed to propose an automated DL framework for BMD assessment that integrates localization, segmentation, and ternary classification using various dominant convolutional neural networks (CNNs). Methods: In this retrospective study, a cohort of 2,274 patients underwent chest computed tomography (CT) was enrolled from January 2022 to June 2023 for the development of the integrated DL system. The study unfolded in 2 phases. Initially, 1,025 patients were selected based on specific criteria to develop an automated segmentation model, utilizing 2 VB-Net networks. Subsequently, a distinct cohort of 902 patients was employed for the development and testing of classification models for BMD assessment. Then, 3 distinct DL network architectures, specifically DenseNet, ResNet-18, and ResNet-50, were applied to formulate the 3-classification BMD assessment model. The performance of both phases was evaluated using an independent test set consisting of 347 individuals. Segmentation performance was evaluated using the Dice similarity coefficient; classification performance was appraised using the receiver operating characteristic (ROC) curve. Furthermore, metrics such as the area under the curve (AUC), accuracy, and precision were meticulously calculated. Results: In the first stage, the automatic segmentation model demonstrated excellent segmentation performance, with mean Dice surpassing 0.93 in the independent test set. In the second stage, both the DenseNet and ResNet-18 demonstrated excellent diagnostic performance in detecting bone status. For osteoporosis, and osteopenia, the AUCs were as follows: DenseNet achieved 0.94 [95% confidence interval (CI): 0.91-0.97], and 0.91 (95% CI: 0.87-0.94), respectively; ResNet-18 attained 0.96 (95% CI: 0.92-0.98), and 0.91 (95% CI: 0.87-0.94), respectively. However, the ResNet-50 model exhibited suboptimal diagnostic performance for osteopenia, with an AUC value of only 0.76 (95% CI: 0.69-0.80). Alterations in tube voltage had a more pronounced impact on the performance of the DenseNet. In the independent test set with tube voltage at 100 kVp images, the accuracy and precision of DenseNet decreased on average by approximately 14.29% and 18.82%, respectively, whereas the accuracy and precision of ResNet-18 decreased by about 8.33% and 7.14%, respectively. Conclusions: The state-of-the-art DL framework model offers an effective and efficient approach for opportunistic osteoporosis screening using chest CT, without incurring additional costs or radiation exposure.

High-resolution melting real-time PCR assays for subtyping of five diarrheagenic Escherichia coli by a single well in milk.

Diarrheagenic Escherichia coli (DEC) is a kind of foodborne pathogen that poses a significant threat to both food safety and human health. To address the current challenges of high prevalence and difficult subtyping of DEC, this study developed a method that combined multiplex polymerase chain reaction (PCR) with high resolution melting (HRM) analysis for subtyping 5 kinds of DEC. The target genes are amplified by multiplex PCR in a single well, and HRM curve analysis was applied for distinct amplicons based on different melting temperature (Tm) values. The method enables discrimination of different DEC types based on characteristic peaks and distinct Tm values in the thermal melting curve. The assay exhibited 100% sensitivity and 100% specificity with a detection limit of 0.5-1 ng/µL. The results showed that different DNA concentrations did not influence the subtyping results, demonstrating this method owed high reliability and stability. In addition, the method was also used for the detection and subtyping of DEC in milk. This method streamlines operational procedures, shorts the detection time, and offers a novel tool for subtyping DEC.

Estimation of renal function using iodine maps in dual-energy spectral computed tomography urography: a feasibility and accuracy study.

PURPOSE: To explore the feasibility of measuring glomerular filtration rate (GFR) using iodine maps in dual-energy spectral computed tomography urography (DEsCTU) and correlate them with the estimated GFR (eGFR) based on the equation of creatinine-cystatin C. MATERIALS AND METHODS: One hundred and twenty-eight patients referred for DEsCTU were retrospectively enrolled. The DEsCTU protocol included non-contrast, nephrographic, and excretory phase imaging. The CT-derived GFR was calculated using the above 3-phase iodine maps (CT-GFRiodine) and 120 kVp-like images (CT-GFR120kvp) separately. CT-GFRiodine and CT-GFR120kvp were compared with eGFR using paired t-test, correlation analysis, and Bland-Altman plots. The receiver operating characteristic curves were used to test the renal function diagnostic performance with CT-GFR120kvp and CT-GFRiodine. RESULTS: The difference between eGFR (89.91 ± 18.45 ml·min-1·1.73 m-2) as reference standard and CT-GFRiodine (90.06 ± 20.89 ml·min-1·1.73 m-2) was not statistically significant, showing excellent correlation (r = 0.88, P < 0.001) and agreement (± 19.75 ml·min-1·1.73 m-2, P = 0.866). The correlation between eGFR and CT-GFR120kvp (66.13 ± 19.18 ml·min-1·1.73 m-2) was poor (r = 0.36, P < 0.001), and the agreement was poor (± 40.65 ml·min-1·1.73 m-2, P < 0.001). There were 62 patients with normal renal function and 66 patients with decreased renal function based on eGFR. The CT-GFRiodine had the largest area under the curve (AUC) for distinguishing between normal and decreased renal function (AUC = 0.951). CONCLUSION: The GFR can be calculated accurately using iodine maps in DEsCTU. DEsCTU could be a non-invasive and reliable one-stop-shop imaging technique for evaluating both the urinary tract morphology and renal function.

Opportunistic screening for osteoporosis using enhanced images based on dual-energy computed tomography material decomposition: a comparison with quantitative computed tomography.

Background: Many patients with malignant tumors require chemotherapy and radiation therapy, which can result in a decline in physical function and potentially influence bone mineral density (BMD). Furthermore, these treatments necessitate enhanced computed tomography (CT) scans for determining disease staging or treatment outcomes, and opportunistic screening with available imaging data is beneficial for patients at high risk for osteoporosis if existing imaging data can be used. The study aimed to investigate the feasibility of opportunistic screening for osteoporosis using enhanced CT based on a dual-energy CT (DECT) material decomposition technique. Methods: We prospectively enrolled 346 consecutive patients who underwent abdominal unenhanced and triphasic contrast-enhanced CT (arterial, portal venous, and delayed phases) between June 2021 and June 2022. The BMD, and the density of hydroxyapatite (HAP) on HAP-iodine images and calcium (Ca) on Ca-iodine images were measured on the L1-L3 vertebral bodies. The iodine intake was recorded. Pearson analysis was conducted to assess the correlation between iodine intake and the density values in three phases and the correlation between BMD and the densities of HAP and Ca. Furthermore, linear regression was employed for quantitative evaluation. Bland-Altman analysis was used to evaluate the agreement between calculated BMD derived from DECT (BMD-DECT) and reference BMD derived from quantitative CT (BMD-QCT). Receiver operating characteristic (ROC) analysis was applied to assess the diagnostic efficacy. Results: The HAP and Ca density of the L1-L3 vertebral bodies did not differ significantly among the three phases of contrast-enhanced CT (F=0.001-0.049; P>0.05). Significant positive correlations were found between HAP, Ca densities, and BMD (HAP-BMD: r=0.9472, R2=0.8973; Ca-BMD: r=0.9470, R2=0.8968; all P<0.001). Bland-Altman plots showed high agreement between BMD-DECT and BMD-QCT. The area under the curve (AUC) using HAP and Ca measurements was 0.963 [95% confidence interval (CI): 0.937-0.980] and 0.964 (95% CI: 0.939-0.981), respectively, for diagnosing osteoporosis and was 0.951 (95% CI: 0.917-0.973) and 0.950 (95% CI: 0.916-0.973), respectively, for diagnosing osteopenia. Conclusions: The HAP and Ca density measurements generated through the material decomposition technique in DECT have good diagnostic performances in assessing BMD, which offers a new perspective for opportunistic screening of osteoporosis on contrast-enhanced CT.

Automatic Osteoporosis Screening System Using Radiomics and Deep Learning from Low-Dose Chest CT Images.

OBJECTIVE: Develop two fully automatic osteoporosis screening systems using deep learning (DL) and radiomics (Rad) techniques based on low-dose chest CT (LDCT) images and evaluate their diagnostic effectiveness. METHODS: In total, 434 patients who underwent LDCT and bone mineral density (BMD) examination were retrospectively enrolled and divided into the development set (n = 333) and temporal validation set (n = 101). An automatic thoracic vertebra cancellous bone (TVCB) segmentation model was developed. The Dice similarity coefficient (DSC) was used to evaluate the segmentation performance. Furthermore, the three-class Rad and DL models were developed to distinguish osteoporosis, osteopenia, and normal bone mass. The diagnostic performance of these models was evaluated using the receiver operating characteristic (ROC) curve and decision curve analysis (DCA). RESULTS: The automatic segmentation model achieved excellent segmentation performance, with a mean DSC of 0.96 ± 0.02 in the temporal validation set. The Rad model was used to identify osteoporosis, osteopenia, and normal BMD in the temporal validation set, with respective area under the receiver operating characteristic curve (AUC) values of 0.943, 0.801, and 0.932. The DL model achieved higher AUC values of 0.983, 0.906, and 0.969 for the same categories in the same validation set. The Delong test affirmed that both models performed similarly in BMD assessment. However, the accuracy of the DL model is 81.2%, which is better than the 73.3% accuracy of the Rad model in the temporal validation set. Additionally, DCA indicated that the DL model provided a greater net benefit compared to the Rad model across the majority of the reasonable threshold probabilities Conclusions: The automated segmentation framework we developed can accurately segment cancellous bone on low-dose chest CT images. These predictive models, which are based on deep learning and radiomics, provided comparable diagnostic performance in automatic BMD assessment. Nevertheless, it is important to highlight that the DL model demonstrates higher accuracy and precision than the Rad model.

Individual deviations from normative electroencephalographic connectivity predict antidepressant response.

BACKGROUND: Antidepressant medications yield unsatisfactory treatment outcomes in patients with major depressive disorder (MDD) with modest advantages over the placebo, partly due to the elusive mechanisms of antidepressant responses and unexplained heterogeneity in patient's response to treatment. Here we develop a novel normative modeling framework to quantify individual deviations in psychopathological dimensions that offers a promising avenue for the personalized treatment for psychiatric disorders. METHODS: We built a normative model with resting-state electroencephalography (EEG) connectivity data from healthy controls of three independent cohorts. We characterized the individual deviation of MDD patients from the healthy norms, based on which we trained sparse predictive models for treatment responses of MDD patients (102 sertraline-medicated and 119 placebo-medicated). Hamilton depression rating scale (HAMD-17) was assessed at both baseline and after the eight-week antidepressant treatment. RESULTS: We successfully predicted treatment outcomes for patients receiving sertraline (r = 0.43, p < 0.001) and placebo (r = 0.33, p < 0.001). We also showed that the normative modeling framework successfully distinguished subclinical and diagnostic variabilities among subjects. From the predictive models, we identified key connectivity signatures in resting-state EEG for antidepressant treatment, suggesting differences in neural circuit involvement between sertraline and placebo responses. CONCLUSIONS: Our findings and highly generalizable framework advance the neurobiological understanding in the potential pathways of antidepressant responses, enabling more targeted and effective personalized MDD treatment. TRIAL REGISTRATION: Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC), NCT#01407094.

Combining Deep Learning and Radiomics for Automated, Objective, Comprehensive Bone Mineral Density Assessment From Low-Dose Chest Computed Tomography.

RATIONALE AND OBJECTIVES: To develop an intelligent diagnostic model for osteoporosis screening based on low-dose chest computed tomography (LDCT). The model incorporates automatic deep-learning thoracic vertebrae of cancellous bone (TVCB) segmentation model and radiomics analysis. MATERIALS AND METHODS: A total of 442 participants who underwent both LDCT and quantitative computed tomography (QCT) examinations were enrolled and were randomly allocated to the training, internal testing, and external testing cohorts. The TVCB automatic segmentation model was trained using VB-Net. The accuracy of the segmentation was evaluated using the Dice coefficient. Predictive models for assessing bone mineral density (BMD) were constructed utilizing radiomics analysis based on automatic segmentation (ASeg model) and manual segmentation (MSeg model), respectively. The BMD predictive model based on ASeg and MSeg included the identification of normal and abnormal BMD (first-level model), and osteopenia and osteoporosis (second-level model). The diagnostic performance of the radiomics models were evaluated using the area under the curve (AUC), sensitivity and specificity. RESULTS: The Dice coefficients of the TVCB segmentation model in the internal and external testing cohorts were found to be 0.988 ± 0.014 and 0.939 ± 0.034, respectively. In the first-level model, the AUC of the ASeg model exhibited comparable performance to that of the MSeg model for both the internal (0.985 vs. 0.946, P = 0.080) and external (0.965 vs. 0.955, P = 0.724) testing cohorts. Similarly, in the second-level model, the AUC of the ASeg model was found to be comparable to that of the MSeg model for both the internal (0.933 vs. 0.920, P = 0.794) and external (0.907 vs. 0.892, P = 0.805) testing cohorts. CONCLUSION: A fully automated pipeline for TVCB segmentation and BMD assessment with radiomics analysis can be used for opportunistic BMD screening in chest LDCT.

Spatiotemporal observations of host-pathogen interactions in mucosa during SARS-CoV-2 infection indicate a protective role of ILC2s.

IMPORTANCE: Our study revealed the spatial interaction between humanized ACE2 and pseudovirus expressing Spike, emphasizing the role of type 2 innate lymphoid cells during the initial phase of viral infection. These findings provide a foundation for the development of mucosal vaccines and other treatment approaches for both pre- and post-infection management of coronavirus disease 2019.

Virtual unenhanced images derived from dual-energy computed tomography for assessing bone mineral density and detecting osteoporosis.

Background: The early detection and treatment of osteoporosis can help prevent osteoporosis-related fractures, especially in patients who undergo enhanced computed tomography (CT) scans for disease diagnosis or evaluation of treatment outcomes. Although Hounsfield unit (HU) measurement of the vertebral body has been shown to have a strong positive correlation with bone mineral density (BMD), the contrast media will impact the CT value of the vertebral body and decrease the accuracy. This study is aimed to examine the distinctions in vertebral body CT attenuation measurement on true unenhanced (TUE) and virtual unenhanced (VUE) images generated from triphasic enhanced dual-energy CT (DECT) scans and to determine the feasibility of assessing BMD and detecting osteoporosis on VUE images as compared to quantitative CT (QCT). Methods: A total of 235 patients underwent abdominal CT examinations that included unenhanced (with 120 kVp and Smart mA) and triphasic enhanced DECT scans. The BMD and CT attenuation values of the L1-L2 vertebrae were measured on TUE and VUE images reconstructed from the triphasic enhanced CT. The differences and associations between TUE and VUE generated from triphasic enhanced CT were analyzed. The diagnostic performances of HU measurements obtained from TUE and VUE images were evaluated using receiver operating characteristic curve. Results: The BMD and HU measurements of the vertebrae showed good interobserver repeatability on both TUE and VUE images (all intercorrelation coefficients >0.92). The CT attenuation values of L1 and L2 and their average value showed no statistically significant difference among the triphasic VUE images (F=0.121, F=0.061, F=0.090; all P values >0.05) but were significantly lower than those obtained from the TUE images. HU measurements in both the TUE and triphasic VUE images, along with the reference BMD derived from QCT, demonstrated a strong positive correlation (rTUE =0.981, rVUEa =0.966, rVUEv =0.962, rVUEd =0.964; all P values <0.05), with excellent diagnostic performance for the diagnoses of osteoporosis and osteopenia (all areas under curve >0.95). The Bland-Altman scatter plot exhibited good agreement, as the deviations between the reference BMD and the calculated BMD were evenly distributed around 0. Conclusions: Although the attenuation values of the vertebrae on the VUE images were underestimated compared to those on the TUE images, the HU measurement on VUE image was effective in assessing BMD and detecting osteoporosis and osteopenia with good diagnostic performance.

Three-Dimensional Imaging and Quantitative Analysis of Blood Vessel Distribution in The Meniscus of Transgenic Mouse after Tissue Clearing.

OBJECTIVE: Blood supply to the meniscus determines its recovery and is a reference for treatment planning. This study aimed to apply tissue clearing and three-dimensional (3D) imaging in exploring the quantitative distribution of blood vessels in the mouse meniscus. MATERIALS AND METHODS: In this experimental study, tissue clearing was performed to treat the bilateral knee joints of transgenic mice with fluorescent vascular endothelial cells. Images were acquired using a light sheet microscope and the vascular endothelial cells in the meniscus was analysed using 3D imaging. Quantitative methods were employed to further analyse the blood vessel distribution in the mouse meniscus. RESULTS: The traditional three-equal-width division of the meniscus is as follows: the outer one-third is the red-red zone (RR), the inner one-third is the white-white zone (WW), and the transition area is the red-white zone (RW). The division revealed significant signal differences between the RW and WW (P<0.05) zones, but no significant differences between the RR and RW zones, which indicated that the division might not accurately reflect the blood supply of the meniscus. According to the modified division (4:2:1) in which significant differences were ensured between the adjacent zones, we observed that the width ratio of each zone was 38 ± 1% (RR), 24 ± 1% (RW), and 38 ± 2% (WW). Furthermore, the blood supply to each region was verified. The anterior region had the most abundant blood supply. The fluorescence count in the anterior region was significantly higher than in the central and posterior regions (P<0.05). The blood supply of the medial meniscus was superior to the lateral meniscus (P<0.05). CONCLUSION: Analysis of the blood supply to the mouse meniscus under tissue clearing and 3D imaging reflect quantitative blood vessel distribution, which would facilitate future evaluations of the human meniscus and provide more anatomical references for clinicians.

Neural dynamics in the limbic system during male social behaviors.

Sexual and aggressive behaviors are vital for species survival and individual reproductive success. Although many limbic regions have been found relevant to these behaviors, how social cues are represented across regions and how the network activity generates each behavior remains elusive. To answer these questions, we utilize multi-fiber photometry (MFP) to simultaneously record Ca2+ signals of estrogen receptor alpha (Esr1)-expressing cells from 13 limbic regions in male mice during mating and fighting. We find that conspecific sensory information and social action signals are widely distributed in the limbic system and can be decoded from the network activity. Cross-region correlation analysis reveals striking increases in the network functional connectivity during the social action initiation phase, whereas late copulation is accompanied by a "dissociated" network state. Based on the response patterns, we propose a mating-biased network (MBN) and an aggression-biased network (ABN) for mediating male sexual and aggressive behaviors, respectively.

Iron-Catalyzed Reductive Cross-Coupling of Alkyl Electrophiles with Olefins.

In terms of its abundance and its minimal toxicity, iron has advantages relative to other transition metals. Although alkyl-alkyl bond construction is central to organic synthesis, examples of iron-catalyzed alkyl-alkyl couplings of alkyl electrophiles are relatively sparse. Herein we report an iron catalyst that achieves cross-coupling reactions of alkyl electrophiles wherein olefins, in the presence of a hydrosilane, are used in place of alkylmetal reagents. Carbon-carbon bond formation proceeds at room temperature, and the method employs commercially available components (Fe(OAc)2 , Xantphos, and Mg(OEt)2 ); interestingly, this set of reagents can be applied directly to a distinct hydrofunctionalization of olefins, hydroboration. Mechanistic studies are consistent with the generation of an alkyl radical from the alkyl electrophile, as well as with reversibility for elementary steps that precede carbon-carbon bond formation (olefin binding to iron and ß-migratory insertion).

Comparative pharmacokinetics of six bioactive components of Shen-Wu-Yi-Shen tablets in normal and chronic renal failure rats based on UPLC-TSQ-MS/MS.

ETHNOPHARMACOLOGICAL RELEVANCE: Shen-Wu-Yi-Shen tablets (SWYST), a Chinese patent medicine consisting of 12 herbal medicines, was formulated by a famous TCM nephrologist, Zou Yunxiang. It is clinically used to improve the symptoms of nausea, vomiting, poor appetite, dry mouth and throat, and dry stool in patients with chronic renal failure (CRF) accompanied by qi and yin deficiency, dampness, and turbidity. SWYST can reduce urea nitrogen, blood creatinine, and urinary protein loss, and increase the endogenous creatinine clearance rate. However, little is known about its pharmacokinetics. AIM OF STUDY: To compare the pharmacokinetics of six bioactive components after oral administration of SWYST in normal and adenine-induced CRF rats. MATERIALS AND METHODS: A method based on ultra-performance liquid chromatography coupled with a triple-stage quadrupole mass spectrometer (UPLC-TSQ-MS/MS) was developed and validated to determine the six bioactive compounds (albiflorin, paeoniflorin, plantagoguanidinic acid, rhein, aloe-emodin, and emodin) in rat plasma. Rat plasma samples were prepared using protein precipitation. Chromatography was performed on an Agilent Eclipse Plus C18 column (3.0 × 50 mm, 1.8 µm) using gradient elution with a mobile phase composed of acetonitrile and water containing 0.1% (v/v) formic acid, while detection was achieved by electrospray ionization MS under the multiple selective reaction monitoring modes. After SWYST administration, rat plasma was collected at different time points, and the pharmacokinetic parameters of six analytes were calculated and analyzed based on the measured plasma concentrations. RESULTS: The UPLC-TSQ-MS/MS method was fully validated for its satisfactory linearity (r ≥ 0.9913), good precisions (RSD <11.5%), and accuracy (RE: -13.4∼13.1%), as well as acceptable limits in the extraction recoveries, matrix effects, and stability (RSD <15%). In normal rats, the six analytes were rapidly absorbed (Tmax ≤ 2 h), and approximately 80% of their total exposure was eliminated within 10 h. Moreover, in normal rats, the AUC0-t and Cmax of albiflorin, plantagoguanidinic acid, and rhein exhibited linear pharmacokinetics within the dose ranges, while that of paeoniflorin is non-linear. However, in CRF rats, the six analytes exhibited reduced elimination and significantly different AUC or Cmax values. These changes may reflect a decreased renal clearance rate or inhibition of drug-metabolizing enzymes and transporters in the liver and gastrointestinal tract caused by CRF. CONCLUSIONS: A sensitive UPLC-TSQ-MS/MS method was validated and used to investigate the pharmacokinetics of SWYST in normal and CRF rats. This is the first study to investigate the pharmacokinetics of SWYST, and our findings elucidate the causes of their different pharmacokinetic behaviors in CRF rats. Furthermore, the results provide useful information to guide further research on the pharmacokinetic-pharmacodynamic correlation and clinical application of SWYST.

Dissecting Symptom-linked Dimensions of Resting-State Electroencephalographic Functional Connectivity in Autism with Contrastive Learning.

Autism spectrum disorder (ASD) is a common neurodevelopmental disorder characterized by social interaction deficits, communication difficulties, and restricted/repetitive behaviors or fixated interests. Despite its high prevalence, development of effective therapy for ASD is hindered by its symptomatic and neurophysiological heterogeneities. To collectively dissect the ASD heterogeneity in neurophysiology and symptoms, we develop a new analytical framework combining contrastive learning and sparse canonical correlation analysis to identify resting-state EEG connectivity dimensions linked to ASD behavioral symptoms within 392 ASD samples. Two dimensions are successfully identified, showing significant correlations with social/communication deficits (r = 0.70) and restricted/repetitive behaviors (r = 0.45), respectively. We confirm the robustness of these dimensions through cross-validation and further demonstrate their generalizability using an independent dataset of 223 ASD samples. Our results reveal that the right inferior parietal lobe is the core region displaying EEG activity associated with restricted/repetitive behaviors, and functional connectivity between the left angular gyrus and the right middle temporal gyrus is a promising biomarker of social/communication deficits. Overall, these findings provide a promising avenue to parse ASD heterogeneity with high clinical translatability, paving the way for treatment development and precision medicine for ASD.