Multi-feature Fusion Network on Gray Scale Ultrasonography: Effective Differentiation of Adenolymphoma and Pleomorphic Adenoma.

RATIONALE AND OBJECTIVES: to develop a deep learning radiomics graph network (DLRN) that integrates deep learning features extracted from gray scale ultrasonography, radiomics features and clinical features, for distinguishing parotid pleomorphic adenoma (PA) from adenolymphoma (AL) MATERIALS AND METHODS: A total of 287 patients (162 in training cohort, 70 in internal validation cohort and 55 in external validation cohort) from two centers with histologically confirmed PA or AL were enrolled. Deep transfer learning features and radiomics features extracted from gray scale ultrasound images were input to machine learning classifiers including logistic regression (LR), support vector machines (SVM), KNN, RandomForest (RF), ExtraTrees, XGBoost, LightGBM, and MLP to construct deep transfer learning radiomics (DTL) models and Rad models respectively. Deep learning radiomics (DLR) models were constructed by integrating the two features and DLR signatures were generated. Clinical features were further combined with the signatures to develop a DLRN model. The performance of these models was evaluated using receiver operating characteristic (ROC) curve analysis, calibration, decision curve analysis (DCA), and the Hosmer-Lemeshow test. RESULTS: In the internal validation cohort and external validation cohort, comparing to Clinic (AUC=0.767 and 0.777), Rad (AUC=0.841 and 0.748), DTL (AUC=0.740 and 0.825) and DLR (AUC=0.863 and 0.859), the DLRN model showed greatest discriminatory ability (AUC=0.908 and 0.908) showed optimal discriminatory ability. CONCLUSION: The DLRN model built based on gray scale ultrasonography significantly improved the diagnostic performance for benign salivary gland tumors. It can provide clinicians with a non-invasive and accurate diagnostic approach, which holds important clinical significance and value. Ensemble of multiple models helped alleviate overfitting on the small dataset compared to using Resnet50 alone.

Acoustic assessment in mandarin-speaking Parkinson's disease patients and disease progression monitoring and brain impairment within the speech subsystem.

Approximately 90% of Parkinson's patients (PD) suffer from dysarthria. However, there is currently a lack of research on acoustic measurements and speech impairment patterns among Mandarin-speaking individuals with PD. This study aims to assess the diagnosis and disease monitoring possibility in Mandarin-speaking PD patients through the recommended speech paradigm for non-tonal languages, and to explore the anatomical and functional substrates. We examined total of 160 native Mandarin-speaking Chinese participants consisting of 80 PD patients, 40 healthy controls (HC), and 40 MRI controls. We screened the optimal acoustic metric combination for PD diagnosis. Finally, we used the objective metrics to predict the patient's motor status using the Naïve Bayes model and analyzed the correlations between cortical thickness, subcortical volumes, functional connectivity, and network properties. Comprehensive acoustic screening based on prosodic, articulation, and phonation abnormalities allows differentiation between HC and PD with an area under the curve of 0.931. Patients with slowed reading exhibited atrophy of the fusiform gyrus (FDR p = 0.010, R = 0.391), reduced functional connectivity between the fusiform gyrus and motor cortex, and increased nodal local efficiency (NLE) and nodal efficiency (NE) in bilateral pallidum. Patients with prolonged pauses demonstrated atrophy in the left hippocampus, along with decreased NLE and NE. The acoustic assessment in Mandarin proves effective in diagnosis and disease monitoring for Mandarin-speaking PD patients, generalizing standardized acoustic guidelines beyond non-tonal languages. The speech impairment in Mandarin-speaking PD patients not only involves motor aspects of speech but also encompasses the cognitive processes underlying language generation.

Prognostic prediction model of colorectal cancer based on preoperative serum tumor markers.

BACKGROUND: Preoperative serum tumor markers not only play a role in the auxiliary diagnosis and postoperative monitoring in colorectal cancer (CRC), but also have been found to have potential prognostic value. AIM: To analyze whether preoperative serum tumor markers, including carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9), affect the prognosis of CRC. METHODS: This was a retrospective study conducted in a single center. Patients with nonmetastatic CRC who underwent initial surgery between January 2011 and January 2020 were enrolled and divided into development site and validation site groups at a ratio of 7:3. The independent prognostic factors were screened by Cox regression analysis, and finally, a prognostic nomogram model was established. The newly developed model was tested by internal validation. RESULTS: Eventually, 3526 postoperative patients with nonmetastatic CRC were included in the study. There were 2473 patients at the development site and 1056 patients at the validation site. Age (P < 0.01, HR = 1.042, 95%CI = 1.033-1.051), tumor node metastasis (TNM) classification (P < 0.01, HR = 1.938, 95%CI = 1.665-2.255), preoperative CEA (P = 0.001, HR = 1.393, 95%CI = 1.137-1.707) and CA19-9 (P < 0.01, HR = 1.948, 95%CI = 1.614-2.438) levels were considered independent prognostic factors for patients with nonmetastatic CRC and were used as variables in the nomogram model. The areas under the curve of the development and validation sites were 0.655 and 0.658, respectively. The calibration plot also showed the significant performance of the newly established nomogram. CONCLUSION: We successfully constructed a nomogram model based on age, TNM stage, preoperative CEA, and CA19-9 levels to evaluate the overall survival of patients with nonmetastatic CRC.

Development and validation of a nomogram to predict the risk factors of major complications after radical rectal cancer surgery.

Purpose: The aim of this study was to establish a validated nomogram to predict risk factors for major post-operative complications in patients with rectal cancer (RC) by analyzing the factors contributing to major post-operative complications in RC patients. Methods: We retrospectively collected baseline and surgical information on patients who underwent RC surgery between December 2012 and December 2022 at a single-center teaching hospital. The entire cohort was randomly divided into two subsets (60% of the data for development, 40% for validation). Independent risk factors for major post-operative complications were identified using multivariate logistic regression analyses, and predictive models were developed. Area under the curve (AUC) was calculated using receiver operating characteristic curve (ROC) to assess predictive probability, calibration curves were plotted to compare the predicted probability of the nomogram with the actual probability, and the clinical efficacy of the nomogram was assessed using decision curve analysis (DCA). Results: Our study included 3151 patients who underwent radical surgery for RC, including 1892 in the development set and 1259 in the validation set. Forty (2.1%) patients in the development set and 26 (2.1%) patients in the validation set experienced major post-operative complications. Through multivariate logistic regression analysis, age (p<0.01, OR=1.044, 95% CI=1.016-1.074), pre-operative albumin (p<0.01, OR=0.913, 95% CI=0.866-0.964), and open surgery (p<0.01, OR=2.461, 95% CI=1.284-4.761) were identified as independent risk factors for major post-operative complications in RC, and a nomogram prediction model was established. The AUC of the ROC plot for the development set was 0.7161 (95% Cl=0.6397-0.7924), and the AUC of the ROC plot for the validation set was 0.7191 (95% CI=0.6182-0.8199). The predicted probabilities in the calibration curves were highly consistent with the actual probabilities, which indicated that the prediction model had good predictive ability. The DCA also confirmed the good clinical performance of the nomogram. Conclusion: In this study, a validated nomogram containing three predictors was created to identify risk factors for major complications after radical RC surgery. Due to its accuracy and convenience, it could contribute to personalized management of patients in the perioperative period.

Spinal Lymphatic Dysfunction Aggravates the Recovery Process After Spinal Cord Injury.

We aimed to evaluate the role of the spinal lymphatic system in spinal cord injury and whether it has an impact on recovery after spinal cord injury. Flow cytometry was used to evaluate the changes in the number of microvesicles after spinal cord injury. Evans blue extravasation was used to evaluate the function of the lymphatic system. Evans blue extravasation and immunofluorescence were used to evaluate the permeability of blood spinal cord barrier. The spinal cord edema was evaluated by dry and wet weight.Terminal deoxynucleotidyl transferase dUTP nick-end labeling (TUNEL) assay was used to evaluate apoptosis after spinal cord injury. Nuclear factor-kappa B pathway was detected by Western blot. Behavioral tests were used to evaluate limb function. Microvesicles released after spinal cord injury can enter the thoracic duct and then enter the blood through the lymph around the spine. After ligation of the thoracic duct, it can aggravate the neuropathological manifestations and limb function after spinal cord injury. The potential mechanism may involve nuclear factor-kappa B pathway.

The 90% effective concentration of alfentanil combined with 0.075% ropivacaine for epidural labor analgesia: a single-center, prospective, double-blind sequential allocation biased-coin design.

PURPOSE: More literature studies have reported that alfentanil is safe and effective for labor analgesia. However, there is no unified consensus on the optimal dosage of alfentanil used for epidural analgesia. This study explored the concentration at 90% of minimum effective concentration (EC90) of alfentanil combined with 0.075% ropivacaine in patients undergoing epidural labor analgesia to infer reasonable drug compatibility and provide guidance for clinical practice. METHODS: In this prospective, single-center, double-blind study, a total of 45 singleton term primiparas with vaginal delivery who volunteered for epidural labor analgesia were recruited. The first maternal was administered with 3 µg/mL alfentanil combined with 0.075% ropivacaine with the infusion of 10 mL of the mixture every 50 min at a background dose of 3 mL/h. In the absence of PCEA, a total of 15 mL of the mixture is injected per hour. The subsequent alfentanil concentration was determined on the block efficacy of the previous case, using an up-down sequential allocation with a bias-coin design. 30 min after epidural labor analgesia, the block of patient failed with visual analog score (VAS) > 3, the alfentanil concentration was increased in a 0.5 µg/mL gradient for the next patient, while the block was successful with VAS ≤ 3, the alfentanil concentration was remained or decreased in a gradient according to a randomized response list for the next patient. EC90 and 95% confidence interval were calculated by linear interpolation and prediction model with R statistical software. RESULTS: In this study, the estimated EC90 of alfentanil was 3.85 µg/mL (95% confidence interval, 3.64-4.28 µg/mL). CONCLUSION: When combined with ropivacaine 0.075%, the EC90 of alfentanil for epidural labor analgesia is 3.85 µg/mL in patients undergoing labor analgesia.

Individual Structural Covariance Network Predicts Long-Term Motor Improvement in Parkinson's Disease with STN-DBS.

BACKGROUND AND PURPOSE: The efficacy of long-term chronic subthalamic nucleus deep brain stimulation (STN-DBS) in treating Parkinson's Disease (PD) exhibits substantial variability among individuals. The preoperative identification of suitable DBS candidates through predictive means becomes crucial. Our study aims to investigate the predictive value of characterizing individualized structural covariance networks for long-term efficacy of DBS, offering patients a precise and cost-effective preoperative screening tool. MATERIALS AND METHODS: We included 138 PD patients and 40 healthy controls. We developed individualized structural covariance networks from T1-weighted images utilizing Network Template Perturbation, and computed the networks' topological characteristics. Patients were categorized according to their long-term motor improvement following STN-DBS. Intergroup analyses were conducted on individual network edges and topological indices, alongside correlation analyses with long-term outcomes for the entire patient cohort. Finally, machine learning (ML) algorithms were employed for regression and classification to predict post-DBS motor improvement. RESULTS: Among the PD patients, six edges (left Middle Frontal and left Caudate Nucleus, right Olfactory and right Insula, left Superior Medial Frontal Gyrus and right Insula, right Middle Frontal and left Paracentral Lobule, right Middle Frontal and Cerebellum, left Lobule VIIb of the Cerebellum and the vermis of the Cerebellum) exhibited significant results in intergroup comparisons and correlation analyses. Increased degree centrality and local efficiency of the cerebellum, parahippocampal gyrus, and postcentral gyrus were associated with DBS improvement. A regression model constructed from these six edges revealed a significant correlation between predicted and observed changes in the unified Parkinson's disease rating scale (R=0.671, P<0.001), and receiver operating characteristic analysis demonstrated an area under the curve of 0.802, effectively distinguishing between patients with good and moderate improvement post-DBS. CONCLUSIONS: Our findings reveal the link between individual structural covariance network fingerprints in PD patients and long-term motor outcome following STN-DBS. Additionally, binary and continuous cerebellum-basal ganglia-frontal structural covariance network edges have emerged as potential predictive biomarkers for DBS motor outcome. ABBREVIATIONS: subthalamic nucleus deep brain stimulation = STN-DBS; Parkinson's Disease = PD; machine learning = ML); Network Template Perturbation = NTP.

Quantitative study on the photoemission of AlGaN nanoarrays based on the three-dimensional transportation within a four-step process.

Photocathodes play a crucial role in photoelectronic imaging and vacuum electronic devices. The quantum efficiency of photocathodes, which determines their performance, can be enhanced through materials engineering. However, the quantum efficiency of conventional planar photocathodes remains consistently low, at around 25%. In this paper, we propose what we believe is a novel structure of AlGaN nanowire array to address this issue. We investigate the photoemission characteristics of the nanowire array using the "four-step" process, which takes into account optical absorption, electron transportation, electron emission, and electron collection. We compare the quantum efficiency of nanowire arrays with different structure sizes and Al components. After studying the effect of incident light at various angles on the nanowire array photocathode, we identify the optimal dimensional parameters: a height of 400∼500 nm and a wire width of 200∼300 nm. Furthermore, we improved the collection efficiency of the photocathode by introducing a built-in/external electric field, and obtained a 104.4% enhancement of the collection current with the built-in electric field, meanwhile the photocurrent was increased by 87% compared to the case without the external electric field. These findings demonstrate the potential of optimizing photocathode performance through the development of a novel model and adjustment of parameters, offering a promising approach for photocathode applications.

Deep brain stimulation of the subthalamic nucleus for primary Meige syndrome: clinical outcomes and predictive factors.

OBJECTIVE: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has demonstrated efficacy against multiple types of dystonia, but only a few case reports and small-sample studies have investigated the clinical utility of STN-DBS for Meige syndrome, a rare but distressing form of craniofacial dystonia. Furthermore, the effects of DBS on critical neuropsychological sequelae, such as depression and anxiety, are rarely examined. In this study, the authors investigated the therapeutic efficacy of STN-DBS for both motor and psychiatric symptoms of Meige syndrome. METHODS: The authors retrospectively reviewed consecutive patients with Meige syndrome receiving bilateral STN-DBS at their institution from January 2016 to June 2023. Motor performance and nonmotor features including mood, cognitive function, and quality of life (QOL) were evaluated using standardized rating scales at baseline and at final postoperative follow-up. Clinical and demographic factors influencing postoperative motor outcome were evaluated by uni- and multivariable linear regression models. RESULTS: Fifty-one patients were ultimately included, with a mean ± SD follow-up duration of 27.3 ± 18.0 months. The mean Burke-Fahn-Marsden Dystonia Rating Scale (BFMDRS) movement score improved from 12.9 ± 5.2 before surgery to 5.3 ± 4.2 at the last follow-up (mean improvement 58.9%, p < 0.001) and the mean BFMDRS disability score improved from 5.6 ± 3.3 to 2.9 ± 2.9 (mean improvement 44.6%, p < 0.001). Hamilton Depression and Anxiety Rating Scale scores also improved by 35.3% and 34.2%, respectively, and the postoperative 36-item Short-Form Health Survey score indicated substantial QOL enhancement. Global cognition remained stable after treatment. Multiple linear regression analysis identified disease duration (ß = -0.241, p = 0.027), preoperative anxiety severity (ß = -0.386, p = 0.001), and volume of activated tissue within the dorsolateral (sensorimotor) STN (ß = 0.483, p < 0.001) as independent predictors of motor outcome. CONCLUSIONS: These findings support STN-DBS as an effective and promising therapy for both motor and nonmotor symptoms of Meige syndrome. Timely diagnosis, treatment of preoperative anxiety, and precise electrode placement within the dorsolateral STN are essential for optimal clinical outcome.

Metal-Oxide Heterojunction: From Material Process to Neuromorphic Applications.

As technologies like the Internet, artificial intelligence, and big data evolve at a rapid pace, computer architecture is transitioning from compute-intensive to memory-intensive. However, traditional von Neumann architectures encounter bottlenecks in addressing modern computational challenges. The emulation of the behaviors of a synapse at the device level by ionic/electronic devices has shown promising potential in future neural-inspired and compact artificial intelligence systems. To address these issues, this review thoroughly investigates the recent progress in metal-oxide heterostructures for neuromorphic applications. These heterostructures not only offer low power consumption and high stability but also possess optimized electrical characteristics via interface engineering. The paper first outlines various synthesis methods for metal oxides and then summarizes the neuromorphic devices using these materials and their heterostructures. More importantly, we review the emerging multifunctional applications, including neuromorphic vision, touch, and pain systems. Finally, we summarize the future prospects of neuromorphic devices with metal-oxide heterostructures and list the current challenges while offering potential solutions. This review provides insights into the design and construction of metal-oxide devices and their applications for neuromorphic systems.

Premature drug reduction after subthalamic nucleus deep brain stimulation leading to worse depression in patients with Parkinson's disease.

Background: Reduction of medication in Parkinson's disease (PD) following subthalamic nucleus deep brain stimulation (STN-DBS) has been recognized, but the optimal timing for medication adjustments remains unclear, posing challenges in postoperative patient management. Objective: This study aimed to provide evidence for the timing of medication reduction post-DBS using propensity score matching (PSM). Methods: In this study, initial programming and observation sessions were conducted over 1 week for patients 4-6 weeks postoperatively. Patients were subsequently categorized into medication reduction or non-reduction groups based on their dyskinesia evaluation using the 4.2-item score from the MDS-UPDRS-IV. PSM was employed to maintain baseline comparability. Short-term motor and neuropsychiatric symptom assessments for both groups were conducted 3-6 months postoperatively. Results: A total of 123 PD patients were included. Baseline balance in motor and non-motor scores was achieved between the two groups based on PSM. Short-term efficacy revealed a significant reduction in depression scores within the non-reduction group compared to baseline (P < 0.001) and a significant reduction compared to the reduction group (P = 0.037). No significant differences were observed in UPDRS-III and HAMA scores between the two groups. Within-group analysis showed improvements in motor symptoms, depression, anxiety, and subdomains in the non-reduction group, while the reduction group exhibited improvements only in motor symptoms. Conclusion: This study provides evidence for the timing of medication reduction following DBS. Our findings suggest that early maintenance of medication stability is more favorable for improving neuropsychiatric symptoms.

[Expression and significance of jumonji domain-containing protein 2B and hypoxia inducible factor-1α in non-Hodgkin lymphoma tissues in children]. / 含jumonji结构域的蛋白2Bå’Œç¼ºæ°§è¯±å¯¼å› å­1α在儿童非霍奇金淋巴瘤组织中的表达及意义.

OBJECTIVES: To investigate the expression and significance of jumonji domain-containing protein 2B (JMJD2B) and hypoxia-inducible factor-1α (HIF-1α) in non-Hodgkin's lymphoma (NHL) tissues in children. METHODS: Immunohistochemistry was used to detect the expression of JMJD2B and HIF-1α in lymph node tissue specimens from 46 children with NHL (observation group) and 24 children with reactive hyperplasia (control group). The relationship between JMJD2B and HIF-1α expression with clinicopathological characteristics and prognosis in children with NHL, as well as the correlation between JMJD2B and HIF-1α expression in NHL tissues, were analyzed. RESULTS: The positive expression rates of JMJD2B (87% vs 21%) and HIF-1α (83% vs 42%) in the observation group were higher than those in the control group (P<0.05). The expression of JMJD2B and HIF-1α was correlated with serum lactate dehydrogenase levels and the risk of international prognostic index in children with NHL (P<0.05). The expression of JMJD2B was positively correlated with the HIF-1α expression in children with NHL (rs=0.333, P=0.024). CONCLUSIONS: JMJD2B and HIF-1α are upregulated in children with NHL, and they may play a synergistic role in the development of pediatric NHL. JMJD2B can serve as a novel indicator for auxiliary diagnosis, evaluation of the severity, treatment guidance, and prognosis assessment in pediatric NHL.

Optimal subthalamic stimulation sites and related networks for freezing of gait in Parkinson's disease.

Freezing of gait is a common and debilitating symptom in Parkinson's disease. Although high-frequency subthalamic deep brain stimulation is an effective treatment for Parkinson's disease, post-operative freezing of gait severity has been reported to alleviate, deteriorate or remain constant. We conducted this study to explore the optimal stimulation sites and related connectivity networks for high-frequency subthalamic deep brain stimulation treating freezing of gait in Parkinson's disease. A total of 76 Parkinson's disease patients with freezing of gait who underwent bilateral high-frequency subthalamic stimulation were retrospectively included. The volumes of tissue activated were estimated based on individual electrode reconstruction. The optimal and sour stimulation sites were calculated at coordinate/voxel/mapping level and mapped to anatomical space based on patient-specific images and stimulation settings. The structural and functional predictive connectivity networks for the change of the post-operative Freezing of Gait-Questionnaire were also identified based on normative connectomes derived from the Parkinson's Progression Marker Initiative database. Leave-one-out cross-validation model validated the above results, and the model remained significant after including covariates. The dorsolateral two-thirds of the subthalamic nucleus was identified as the optimal stimulation site, while the ventrocentral portion of the right subthalamic nucleus and internal capsule surrounding the left central subthalamic nucleus were considered as the sour stimulation sites. Modulation of the fibre tracts connecting to the supplementary motor area, pre-supplementary motor area and pedunculopontine nucleus accounted for the alleviation of freezing of gait, whereas tracts connecting to medial and ventrolateral prefrontal cortices contributed to the deterioration of freezing of gait. The optimal/sour stimulation sites and structural/functional predictive connectivity networks for high-frequency subthalamic deep brain stimulation treating freezing of gait are identified and validated through sizable Parkinson's disease patients in this study. With the growing understanding of stimulation sites and related networks, individualized deep brain stimulation treatment with directional leads will become an optimal choice for Parkinson's disease patients with freezing of gait in the future.

Formulation and Performance of Model Concrete in Reduced-Scale Physical Model Tests.

The utility of geotechnical centrifuge tests depends on how correctly they predict the physical and mechanical behaviour of concrete. In this study, a model concrete material that consisted of α-gypsum plaster, fine silica sand, and water was developed. An orthogonal test design was used to evaluate the effect of the mix proportion on the model concrete performance. The physical (i.e., flowability and bleeding rate) and mechanical (i.e., compressive and flexural strength) characteristics were considered as indices. Various mix ratios resulted in remarkable relative contributions to model concrete performance, and each raw material dosage exhibited positive or negative synergy. The water-plaster ratio (W/P) and aggregate-plaster ratio (A/P) strongly influenced the mechanical and physical characteristics, respectively. Multiple linear regression analysis (MLRA) was carried out to determine a forecast model for various small-scale test demands. Finally, the applicability and outlines of the presented forecasting method in proportioning design were evaluated by typical use of model concrete in small-scale model tests.

A Simplified Model for Shear Behavior of Mortar Using Biomimetic Carbonate Precipitation.

As a common molecule in biomineralization, L-aspartic acid (L-Asp) has been proven to be able to induce in vitro CaCO3 precipitation, but its application in sand reinforcement has never been studied. In this study, L-Asp was employed in sand reinforcement for the first time through the newly developed biomimetic carbonate precipitation (BCP) technique. Specimens with different number of BCP spray cycles were prepared, and a series of direct shear tests were conducted to investigate the impact of spray number on shear strength, critical displacement, and residual strength. Then a simplified power model for shear stress-displacement behavior was established and calibrated with the measured data. The results show that BCP can significantly improve the shear strength of sand. As the number of spray cycles increases, both the shear strength and residual strength increase, while the critical displacement decreases. Such variations can be described with two sigmoid models and a linear model, respectively. The simplified power model performs well in most cases, especially at higher spray numbers. This study is expected to provide a practical model for the shear behavior of BCP-treated mortar.

Identification of retinal thickness and blood flow in age-related macular degeneration with reticular pseudodrusen.

AIM: To investigate thickness characteristics and vascular plexuses in retinas with reticular pseudodrusen (RPD) as an early detection strategy for age-related macular degeneration (AMD). METHODS: This retrospective study included 24 subjects (33 eyes) with RPD and 25 heathy control subjects (34 eyes). The superficial capillary plexus (SCP) and the deep capillary plexus (DCP) of the retinal posterior poles were investigated with optical coherence tomography angiography (OCTA). Retinal thicknesses and vessel densities were analyzed statistically. RESULTS: The general retinal thicknesses of RPD eyes were significantly decreased (95%CI -14.080, -0.655; P=0.032). The vessel densities of DCP in RPD eyes were significantly increased in the global (95%CI 1.067, 7.312; P=0.027), parafoveal (95%CI 0.417, 5.241; P=0.022), and perifoveal (95%CI 0.181, 6.842; P=0.039) quadrants. However, the vessel densities of the SCP were rarely increased in the eyes with RPD. CONCLUSION: The thinning of retinas in the RPD group suggests a reduction in the number of cells. Additionally, the increased vessel density of the DCP in retinas with RPD indicates a greater demand for blood supply, possibly due to the hypoxia induced RPD compensation caused by RPD in the outer retina. This study highlights the pathological risks associated with RPD and emphasizes the importance of early intervention to retard the progression of AMD.

The clinical application of neuro-robot in the resection of epileptic foci: a novel method assisting epilepsy surgery.

During surgery for foci-related epilepsy, neurosurgeons face significant difficulties in identifying and resecting MRI-negative or deep-seated epileptic foci. Here, we present a neuro-robotic navigation system that is specifically designed for resection of MRI negative epileptic foci. We recruited 52 epileptic patients, and randomly assigned them to treatment group with either neuro-robotic navigation or conventional neuronavigation system. For each patient, in the neuro-robotic navigation group, we integrated multimodality imaging including MRI and PET-CT into the robotic workstation and marked the boundary of foci from the fused image. During surgery, this boundary was delineated by the robotic laser device with high accuracy, guiding resection for the surgeon. For deeply seated foci, we exploited the neuro-robotic navigation system to localize the deepest point with biopsy needle insertion and methylene dye application to locate the boundary of the foci. Our results show that, compared with the conventional neuronavigation, the neuro-robotic navigation system performs equally well in MRI positive epilepsy patients (ENGEL I ratio: 71.4% vs 100%, p = 0.255) systems and show better performance in patients with MRI-negative focal cortical dysplasia (ENGEL I ratio: 88.2% vs 50%, p = 0.0439). At present, there are no documented neurosurgery robots with similar function and application in the field of epilepsy. Our research highlights the added value of using neuro-robotic navigation systems in resection surgery for epilepsy, particularly in cases that involve MRI-negative or deep-seated epileptic foci.

Reuse of Drilling Waste Slurry as the Grouting Material for the Real-Time Capsule Grouting Technique.

A large amount of waste slurry is generated during construction, but direct sedimentation and transportation increase construction costs. Improper treatment leads to ecological and environmental pollution. This paper proposes to reuse drilling waste slurry (DWS) as a raw material from a particular project as a grouting material for the real-time capsule grouting technique (RCG) to replace cement grouting material. This not only deals with DWS but also solves the material demand of RCG. An orthogonal experimental design evaluated the performance of the DWS grouting material (DWS-GM). The five levels for the three factors of this experiment were selected, including the fluidity, bleeding rate, initial setting time, and compression strength. A linear model, support vector machines, and neural networks were used to construct regression models, and the effects of different contents of cement, bentonite, and fly ash on the DWS-GM performance were analyzed. The SVM regression model had better performance in describing the laws of fluidity, bleeding rate, and 28-day compressive strength. Furthermore, the optimization model is proposed to obtain the optimal formulation of the DWS-GM under specific constraints. The optimization results show that the optimal formulation of the DWS-GM was 5.6% cement and 6.9% bentonite. The BL, FL, IST, and 28DCS were 1.61%, 21.87 cm, 27.05 h, and 0.22 MPa to meet the functional requirements of the DWS-GM. The above research fully proves the feasibility of the DWS reuse application. We will further reuse DWS to develop other multifunctional material applications in combination with the control needs of RCG technology and technology from other fields.

Ageing threatens sustainability of smallholder farming in China.

Rapid demographic ageing substantially affects socioeconomic development1-4 and presents considerable challenges for food security and agricultural sustainability5-8, which have so far not been well understood. Here, by using data from more than 15,000 rural households with crops but no livestock across China, we show that rural population ageing reduced farm size by 4% through transferring cropland ownership and land abandonment (approximately 4 million hectares) in 2019, taking the population age structure in 1990 as a benchmark. These changes led to a reduction of agricultural inputs, including chemical fertilizers, manure and machinery, which decreased agricultural output and labour productivity by 5% and 4%, respectively, further lowering farmers' income by 15%. Meanwhile, fertilizer loss increased by 3%, resulting in higher pollutant emissions to the environment. In new farming models, such as cooperative farming, farms tend to be larger and operated by younger farmers, who have a higher average education level, hence improving agricultural management. By encouraging the transition to new farming models, the negative consequences of ageing can be reversed. Agricultural input, farm size and farmer's income would grow by approximately 14%, 20% and 26%, respectively, and fertilizer loss would reduce by 4% in 2100 compared with that in 2020. This suggests that management of rural ageing will contribute to a comprehensive transformation of smallholder farming to sustainable agriculture in China.

A role of GABAA receptor α1 subunit in the hippocampus for rapid-acting antidepressant-like effects of ketamine.

Ketamine can produce rapid-acting antidepressant effects in treatment-resistant patients with depression. Although alterations in glutamatergic and GABAergic neurotransmission in the brain play a role in depression, the precise molecular mechanisms in these neurotransmission underlying ketamine's antidepressant actions remain largely unknown. Mice exposed to FSS (forced swimming stress) showed depression-like behavior and decreased levels of GABA (γ-aminobutyric acid), but not glutamate, in the hippocampus. Ketamine increased GABA levels and decreased glutamate levels in the hippocampus of mice exposed to FSS. There was a correlation between GABA levels and depression-like behavior. Furthermore, ketamine increased the levels of enzymes and transporters on the GABAergic neurons (SAT1, GAD67, GAD65, VGAT and GAT1) and astrocytes (EAAT2 and GAT3), without affecting the levels of enzymes and transporters (SAT2, VGluT1 and GABAAR γ2) on glutamatergic neurons. Moreover, ketamine caused a decreased expression of GABAAR α1 subunit, which was specifically expressed on GABAergic neurons and astrocytes, an increased GABA synthesis and metabolism in GABAergic neurons, a plasticity change in astrocytes, and an increase in ATP (adenosine triphosphate) contents. Finally, GABAAR antagonist bicuculline or ATP exerted a rapid antidepressant-like effect whereas pretreatment with GABAAR agonist muscimol blocked the antidepressant-like effects of ketamine. In addition, pharmacological activation and inhibition of GABAAR modulated the synthesis and metabolism of GABA, and the plasticity of astrocytes in the hippocampus. The present data suggest that ketamine could increase GABA synthesis and astrocyte plasticity through downregulation of GABAAR α1, increases in GABA, and conversion of GABA into ATP, resulting in a rapid-acting antidepressant-like action. This article is part of the Special Issue on 'Ketamine and its Metabolites'.