Preparation and characterization of tea tree essential oil microcapsule-coated packaging paper with bacteriostatic effect.

We prepared tea tree essential oil microcapsules, and the microcapsules and pullulan were coated on kraft paper to prepare an antibacterial paper. The antibacterial activity, structural characterization, and thermal stability of the prepared microcapsules and packaging paper were then tested. We found that the retention rate of microcapsules reached 87.1% after a 70 min of high-temperature treatment. The minimum inhibitory concentrations of microcapsules to S. aureus and E. coli were 112 mg/mL and 224 mg/mL, and the bacteriostatic zones of the packaging paper to E. coli and S. aureus were 17.49 mm and 22.75 mm, respectively. The prepared microcapsules were irregular. The paper coating was formed via hydrogen bonding, which filled the pores of paper fibers. When compared with the base paper, the roughness of the paper was reduced to 7.16 nm (Rq) and 5.61 nm (Ra), and no thermal decomposition occurred at <288 °C, which together implies a good application prospect.

PLUNC inhibits invasion and metastasis in nasopharyngeal carcinoma by inhibiting NLRP3 inflammasome activation.

Nasopharyngeal carcinoma (NPC) is a malignant tumor that occurs in the nasopharynx. Palate, lung, and nasal epithelium clone (PLUNC) has been identified as an early secreted protein that is specifically expressed in the nasopharynx. The aim of this study was to determine the role and mechanism of PLUNC in NPC. We used mRNA sequencing (seq) combined with ribosome-nascent chain complex (RNC)-seq to determine the biological role of PLUNC. The expression of epithelial-to-mesenchymal transition (EMT)-related molecules was detected by western blotting. Then, cell migration and invasion were detected by wound healing and Transwell chamber assays. NPC cells were injected into the tail vein of nude mice to explore the biological role of PLUNC in vivo. The sequencing results showed that PLUNC inhibited the progression of NPC and its expression was correlated with that of NOD-like receptors. Experiments confirmed that PLUNC inhibited the invasion and metastasis of NPC cells by promoting the ubiquitination degradation of NLRP3. PLUNC overexpression in combination with the treatment by MCC950, an inhibitor of NLRP3 inflammasome activation, was most effective in inhibiting NPC invasion and metastasis. In vivo experiments also confirmed that the combination of PLUNC overexpression and MCC950 treatment effectively inhibited the lung metastasis of NPC cells. In summary, our research suggested that PLUNC inhibited the invasion and metastasis of NPC by inhibiting NLRP3 inflammasome activation, and targeting the PLUNC-NLRP3 inflammasome axis could provide a new strategy for the diagnosis and treatment of NPC patients.

A hypothalamic-amygdala circuit underlying sexually dimorphic aggression.

Male animals often display higher levels of aggression than females. However, the neural circuitry mechanisms underlying this sexually dimorphic aggression remain elusive. Here, we identify a hypothalamic-amygdala circuit that mediates male-biased aggression in mice. Specifically, the ventrolateral part of the ventromedial hypothalamus (VMHvl), a sexually dimorphic region associated with eliciting male-biased aggression, projects densely to the posterior substantia innominata (pSI), an area that promotes similar levels of attack in both sexes of mice. Although the VMHvl innervates the pSI unidirectionally through both excitatory and inhibitory connections, it is the excitatory VMHvl-pSI projections that are strengthened in males to promote aggression, whereas the inhibitory connections that reduce aggressive behavior are strengthened in females. Consequently, the convergent hypothalamic input onto the pSI leads to heightened pSI activity in males, resulting in male-biased aggression. Our findings reveal a sexually distinct excitation-inhibition balance of a hypothalamic-amygdala circuit that underlies sexually dimorphic aggression.

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.

Nomogram based on dual-energy CT-derived extracellular volume fraction for the prediction of microsatellite instability status in gastric cancer.

Purpose: To develop and validate a nomogram based on extracellular volume (ECV) fraction derived from dual-energy CT (DECT) for preoperatively predicting microsatellite instability (MSI) status in gastric cancer (GC). Materials and methods: A total of 123 patients with GCs who underwent contrast-enhanced abdominal DECT scans were retrospectively enrolled. Patients were divided into MSI (n=41) and microsatellite stability (MSS, n=82) groups according to postoperative immunohistochemistry staining, then randomly assigned to the training (n=86) and validation cohorts (n=37). We extracted clinicopathological characteristics, CT imaging features, iodine concentrations (ICs), and normalized IC values against the aorta (nICs) in three enhanced phases. The ECV fraction derived from the iodine density map at the equilibrium phase was calculated. Univariate and multivariable logistic regression analyses were used to identify independent risk predictors for MSI status. Then, a nomogram was established, and its performance was evaluated by ROC analysis and Delong test. Its calibration performance and clinical utility were assessed by calibration curve and decision curve analysis, respectively. Results: The ECV fraction, tumor location, and Borrmann type were independent predictors of MSI status (all P < 0.05) and were used to establish the nomogram. The nomogram yielded higher AUCs of 0.826 (0.729-0.899) and 0.833 (0.675-0.935) in training and validation cohorts than single variables (P<0.05), with good calibration and clinical utility. Conclusions: The nomogram based on DECT-derived ECV fraction has the potential as a noninvasive biomarker to predict MSI status in GC patients.

Protein-Protein Interaction Prediction via Structure-Based Deep Learning.

Protein-protein interactions (PPIs) play an essential role in life activities. Many artificial intelligence algorithms based on protein sequence information have been developed to predict PPIs. However, these models have difficulty dealing with various sequence lengths and suffer from low generalization and prediction accuracy. In this study, we proposed a novel end-to-end deep learning framework, RSPPI, combining residual neural network (ResNet) and spatial pyramid pooling (SPP), to predict PPIs based on the protein sequence physicochemistry properties and spatial structural information. In the RSPPI model, ResNet was employed to extract the structural and physicochemical information from the protein three-dimensional structure and primary sequence; the SPP layer was used to transform feature maps to a single vector and avoid the fixed-length requirement. The RSPPI model possessed excellent cross-species performance and outperformed several state-of-the-art methods based either on protein sequence or gene ontology in most evaluation metrics. The RSPPI model provides a novel strategy to develop an AI PPI prediction algorithm.

Mixed representations of choice and outcome by GABA/glutamate cotransmitting neurons in the entopeduncular nucleus.

The basal ganglia (BG) are an evolutionarily conserved and phylogenetically old set of sub-cortical nuclei that guide action selection, evaluation, and reinforcement. The entopeduncular nucleus (EP) is a major BG output nucleus that contains a population of GABA/glutamate cotransmitting neurons (EP Sst+ ) that specifically target the lateral habenula (LHb) and whose function in behavior remains mysterious. Here we use a probabilistic switching task that requires an animal to maintain flexible relationships between action selection and evaluation to examine when and how GABA/glutamate cotransmitting neurons contribute to behavior. We find that EP Sst+ neurons are strongly engaged during this task and show bidirectional changes in activity during the choice and outcome periods of a trial. We then tested the effects of either permanently blocking cotransmission or modifying the GABA/glutamate ratio on behavior in well-trained animals. Neither manipulation produced detectable changes in behavior despite significant changes in synaptic transmission in the LHb, demonstrating that the outputs of these neurons are not required for on-going action-outcome updating in a probabilistic switching task.

Online inspection of blackheart in potatoes using visible-near infrared spectroscopy and interpretable spectrogram-based modified ResNet modeling.

Introduction: Blackheart is one of the most common physiological diseases in potatoes during storage. In the initial stage, black spots only occur in tissues near the potato core and cannot be detected from an outward appearance. If not identified and removed in time, the disease will seriously undermine the quality and sale of theentire batch of potatoes. There is an urgent need to develop a method for early detection of blackheart in potatoes. Methods: This paper used visible-near infrared (Vis/NIR) spectroscopy to conduct online discriminant analysis on potatoes with varying degrees of blackheart and healthy potatoes to achieve real-time detection. An efficient and lightweight detection model was developed for detecting different degrees of blackheart in potatoes by introducing the depthwise convolution, pointwise convolution, and efficient channel attention modules into the ResNet model. Two discriminative models, the support vector machine (SVM) and the ResNet model were compared with the modified ResNet model. Results and discussion: The prediction accuracy for blackheart and healthy potatoes test sets reached 0.971 using the original spectrum combined with a modified ResNet model. Moreover, the modified ResNet model significantly reduced the number of parameters to 1434052, achieving a substantial 62.71% reduction in model complexity. Meanwhile, its performance was evidenced by a 4.18% improvement in accuracy. The Grad-CAM++ visualizations provided a qualitative assessment of the model's focus across different severity grades of blackheart condition, highlighting the importance of different wavelengths in the analysis. In these visualizations, the most significant features were predominantly found in the 650-750 nm range, with a notable peak near 700 nm. This peak was speculated to be associated with the vibrational activities of the C-H bond, specifically the fourth overtone of the C-H functional group, within the molecular structure of the potato components. This research demonstrated that the modified ResNet model combined with Vis/NIR could assist in the detection of different degrees of black in potatoes.

Prehabilitation of surgical patients: a bibliometric analysis from 2005 to 2023.

BACKGROUND: Good preoperative conditions help patients to counteract surgical injury. Prehabilitation is a multimodal preoperative management strategy, including physical, nutritional, psychological, and other interventions, which can improve the functional reserve of patients and enhance postoperative recovery. The purpose of this study is to show the evolution trend and future directions of research related to the prehabilitation of surgical patients. METHODS: The global literature regarding prehabilitation was identified from The Web of Science Core Collection database. Bibliometric methods of the Bibliometrix package of R (version 4.2.1) and VOSviewer were used to analyze publication trends, cooperative networks, study themes, and co-citation relationships in the field. RESULTS: A total of 638 publications were included and the number of publications increased rapidly since 2016, with an average annual growth rate of 41.0%. "Annals of Surgery", "British Journal of Surgery" and "British Journal of Anesthesia" were the most cited journals. Experts from the USA, Canada, the UK, and the Netherlands contributed the most in this field, and an initial cooperative network among different countries and clinical teams was formed. Malnutrition, older patients, frailty, and high-risk patients were the hotspots of recent studies. However, among the top 10 cited articles, the clinical effects of prehabilitation were conflicting. CONCLUSION: This bibliometric review summarized the most influential publications as well as the publication trends and clarified the progress and future directions of prehabilitation, which could serve as a guide for developing evidence-based practices.

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.

A Random Forest Algorithm for Assessing Risk Factors Associated With Chronic Kidney Disease: Observational Study.

BACKGROUND: The prevalence and mortality rate of chronic kidney disease (CKD) are increasing year by year, and it has become a global public health issue. The economic burden caused by CKD is increasing at a rate of 1% per year. CKD is highly prevalent and its treatment cost is high but unfortunately remains unknown. Therefore, early detection and intervention are vital means to mitigate the treatment burden on patients and decrease disease progression. OBJECTIVE: In this study, we investigated the advantages of using the random forest (RF) algorithm for assessing risk factors associated with CKD. METHODS: We included 40,686 people with complete screening records who underwent screening between January 1, 2015, and December 22, 2020, in Jing'an District, Shanghai, China. We grouped the participants into those with and those without CKD by staging based on the glomerular filtration rate staging and grouping based on albuminuria. Using a logistic regression model, we determined the relationship between CKD and risk factors. The RF machine learning algorithm was used to score the predictive variables and rank them based on their importance to construct a prediction model. RESULTS: The logistic regression model revealed that gender, older age, obesity, abnormal index estimated glomerular filtration rate, retirement status, and participation in urban employee medical insurance were significantly associated with the risk of CKD. On RF algorithm-based screening, the top 4 factors influencing CKD were age, albuminuria, working status, and urinary albumin-creatinine ratio. The RF model predicted an area under the receiver operating characteristic curve of 93.15%. CONCLUSIONS: Our findings reveal that the RF algorithm has significant predictive value for assessing risk factors associated with CKD and allows the screening of individuals with risk factors. This has crucial implications for early intervention and prevention of CKD.

Sodium Crotonate Alleviates Diabetic Kidney Disease Partially Via the Histone Crotonylation Pathway.

Diabetic kidney disease (DKD) is a common microvascular complication of diabetes, inflammation and fibrosis play an important role in its progression. Histone lysine crotonylation (Kcr) was first identified as a new type of post-translational modification in 2011. In recent years, prominent progress has been made in the study of sodium crotonate (NaCr) and histone Kcr in kidney diseases. However, the effects of NaCr and NaCr-induced Kcr on DKD remain unclear. In this study, db/db mice and high glucose-induced human tubular epithelial cells (HK-2) were used respectively, and exogenous NaCr and crotonoyl-coenzyme A (Cr-CoA) as intervention reagents, histone Kcr and DKD-related indicators were detected. The results confirmed that NaCr had an antidiabetic effect and decreased blood glucose and serum lipid levels and alleviated renal function and DKD-related inflammatory and fibrotic damage. NaCr also induced histone Kcr and histone H3K18 crotonylation (H3K18cr). However, NaCr and Cr-CoA-induced histone Kcr and protective effects were reversed by inhibiting the activity of Acyl-CoA synthetase short-chain family member 2 (ACSS2) or histone acyltransferase P300 in vitro. In summary, our data reveal that NaCr may mitigate DKD via an antidiabetic effect as well as through ACSS2 and P300-induced histone Kcr, suggesting that Kcr may be the potential molecular mechanism and prevention target of DKD.

Gold-Catalyzed Cascade Reaction of Yne-Enones with Iminooxindoles, Access to 3,2'-Pyrrolidinyl-Spirooxindole Derivatives.

Herein, a gold-catalyzed cascade reaction of yne-enones with iminooxindoles has been developed through a cascade cycloisomerization/(3 + 2) annulation process. This approach provides a straightforward and efficient route for the synthesis of functionalized 3,2'-pyrrolidinyl-spirooxindoles in high reactivity and broad substrate scope with excellent cis-selectivity. Moreover, the subsequent functionalization of furan units allows for the diverse synthesis of spirooxindole derivatives, which have demonstrated good antitumoral activity.

Targeting starvation therapy for diabetic bacterial infections with endogenous enzyme-triggered hyaluronan-modified nanozymes in the infection microenvironment.

The high-glycemic microenvironment of diabetic wounds promotes bacterial proliferation, leading to persistent infections and delayed wound healing. This poses a significant threat to human health, necessitating the development of new nanodrug visualization platforms. In this study, we designed and synthesized cascade nano-systems modified with targeted peptide and hyaluronic acid for diabetic infection therapy. The nano-systems were able to target the site of infection using LL-37, and in the microenvironment of wound infection, the hyaluronic acid shell of the nano-systems was degraded by endogenous hyaluronidase. This precise degradation released a cascade of nano-enzymes on the surface of the bacteria, effectively destroying their cytoskeleton. Additionally, the metals in the nano-enzymes provided a photo-thermal effect, accelerating wound healing. The cascade nano-visualization platform demonstrated excellent bactericidal efficacy in both in vitro antimicrobial assays and in vivo diabetic infection models. In conclusion, this nano-system employs multiple approaches including targeting, enzyme-catalyzed therapy, photothermal therapy, and chemodynamic therapy to kill bacteria and promote healing. The Ag@Pt-Au-LYZ/HA-LL-37 formulation shows great potential for the treatment of diabetic wounds.

Iguratimod suppresses plasma cell differentiation and ameliorates experimental Sjögren's syndrome in mice by promoting TEC kinase degradation.

Primary Sjögren's syndrome (pSS) is a chronic inflammatory autoimmune disease with an unclear pathogenesis, and there is currently no approved drug for the treatment of this disease. Iguratimod, as a novel clinical anti-rheumatic drug in China and Japan, has shown remarkable efficacy in improving the symptoms of patients with pSS in clinical studies. In this study we investigated the mechanisms underlying the therapeutic effect of iguratimod in the treatment of pSS. Experimental Sjögren's syndrome (ESS) model was established in female mice by immunizing with salivary gland protein. After immunization, ESS mice were orally treated with iguratimod (10, 30, 100 mg·kg-1·d-1) or hydroxychloroquine (50 mg·kg-1·d-1) for 70 days. We showed that iguratimod administration dose-dependently increased saliva secretion, and ameliorated ESS development by predominantly inhibiting B cells activation and plasma cell differentiation. Iguratimod (30 and 100 mg·kg-1·d-1) was more effective than hydroxychloroquine (50 mg·kg-1·d-1). When the potential target of iguratimod was searched, we found that iguratimod bound to TEC kinase and promoted its degradation through the autophagy-lysosome pathway in BAFF-activated B cells, thereby directly inhibiting TEC-regulated B cells function, suggesting that the action mode of iguratimod on TEC was different from that of conventional kinase inhibitors. In addition, we found a crucial role of TEC overexpression in plasma cells of patients with pSS. Together, we demonstrate that iguratimod effectively ameliorates ESS via its unique suppression of TEC function, which will be helpful for its clinical application. Targeting TEC kinase, a new regulatory factor for B cells, may be a promising therapeutic option.

High-efficiency crystalline white organic light-emitting diodes.

Crystalline white organic light-emitting diodes (C-WOLEDs) are promising candidates for lighting and display applications. It is urgently necessary, however, to develop energy-saving and high-efficiency C-WOLEDs that have stable and powerful emission to meet commercial demands. Here, we report a crystalline host matrix (CHM) with embedded nanoaggregates (NA) structure for developing high-performance C-WOLEDs by employing a thermally activated delayed fluorescence (TADF) material and orange phosphorescent dopants (Phos.-D). The CHM-TADFNA-D WOLED exhibit a remarkable EQE of 12.8%, which is the highest performance WOLEDs based on crystalline materials. The device has a quick formation of excitons and a well-designed energy transfer process, and possesses a fast ramping of luminance and current density. Compared to recently reported high-performance WOLEDs based on amorphous material route, the C-WOLED achieves a low series-resistance Joule-heat loss ratio and an enhanced photon output, demonstrating its significant potential in developing the next-generation WOLEDs.

Ternary Photoanodes with AgAu Nanoclusters and CoNi-LDH for Enhanced Photoelectrochemical Water Oxidation.

Atomically precise metal nanoclusters (NCs) present new opportunities for creating innovative solar-powered photoanodes due to their extraordinary physicochemical properties. Nevertheless, ultrasmall metal NCs tend to aggregate and lack active sites under light irradiation, which severely limits their widespread application. We have developed a strategy to design efficient ternary photoanodes by successively modifying AgAu NCs and CoNi-LDH on BiVO4 substrates using versatile impregnation and electrodeposition. The electronic properties of AgAu NCs facilitate the rapid transfer of photogenerated carriers on BiVO4 and CoNi-LDH. Additionally, ultrathin CoNi-LDH acts as a hole-collecting layer, which quickly extracts holes to the electrode/electrolyte interface. The synergistic effect and the matched energy levels between the ternary heterostructures promote the OER process, which significantly improved the photoelectrochemical (PEC) water oxidation performance. This study presents a new idea for further exploration of metal nanocluster-based PEC systems.

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.

Control of defensive behavior by the nucleus of Darkschewitsch GABAergic neurons.

The nucleus of Darkschewitsch (ND), mainly composed of GABAergic neurons, is widely recognized as a component of the eye-movement controlling system. However, the functional contribution of ND GABAergic neurons (NDGABA) in animal behavior is largely unknown. Here, we show that NDGABA neurons were selectively activated by different types of fear stimuli, such as predator odor and foot shock. Optogenetic and chemogenetic manipulations revealed that NDGABA neurons mediate freezing behavior. Moreover, using circuit-based optogenetic and neuroanatomical tracing methods, we identified an excitatory pathway from the lateral periaqueductal gray (lPAG) to the ND that induces freezing by exciting ND inhibitory outputs to the motor-related gigantocellular reticular nucleus, ventral part (GiV). Together, these findings indicate the NDGABA population as a novel hub for controlling defensive response by relaying fearful information from the lPAG to GiV, a mechanism critical for understanding how the freezing behavior is encoded in the mammalian brain.