Modulating reward and aversion: Insights into addiction from the paraventricular nucleus.

BACKGROUND: Drug addiction, characterized by compulsive drug use and high relapse rates, arises from complex interactions between reward and aversion systems in the brain. The paraventricular nucleus (PVN), located in the anterior hypothalamus, serves as a neuroendocrine center and is a key component of the hypothalamic-pituitary-adrenal axis. OBJECTIVE: This review aimed to explore how the PVN impacts reward and aversion in drug addiction through stress responses and emotional regulation and to evaluate the potential of PVN as a therapeutic target for drug addiction. METHODS: We review the current literature, focusing on three main neuron types in the PVN-corticotropin-releasing factor, oxytocin, and arginine vasopressin neurons-as well as other related neurons, to understand their roles in modulating addiction. RESULTS: Existing studies highlight the PVN as a key mediator in addiction, playing a dual role in reward and aversion systems. These findings are crucial for understanding addiction mechanisms and developing targeted therapies. CONCLUSION: The role of PVN in stress response and emotional regulation suggests its potential as a therapeutic target in drug addiction, offering new insights for addiction treatment.

Triple therapy boosts survival in NSCLC patients with brain metastases: a retrospective cohort study of chemotherapy, ICIs, and antiangiogenic agents.

BACKGROUND: Treatment of brain metastases (BMs) in non-small cell lung cancer (NSCLC) patients, especially those with non-sensitive genetic mutations, is hindered by limited drug delivery through the blood-brain barrier (BBB). This retrospective study explores the efficacy of systemic treatments during brain metastasis to radiotherapy evaluation window in improving patient survival. METHODS: In this retrospective cohort study, we evaluated 209 NSCLC patients with non-sensitive mutations and BMs, treated between 2016 and 2023 at two tertiary medical centers (Chongqing University Cancer Hospital and Guangxi Medical University Cancer Hospital). The patients were divided into three groups, namely chemotherapy alone (C; n = 95), chemotherapy plus immune checkpoint inhibitors (ICIs) (C + I; n = 62), and chemotherapy with ICIs and antiangiogenic therapy (A) (C + I + A; n = 52). Statistical analyses were performed using R software, version 4.3.3. Categorical variables were compared using Fisher's exact test, and survival curves were estimated with the Kaplan-Meier method and compared via the log-rank test. Univariate and multivariate Cox regression models were used to assess factors associated with overall survival (OS). Bayesian model averaging (BMA) was employed to address model uncertainty and improve result robustness. Subgroup analyses evaluated treatment-related mortality risk. RESULTS: From an initial cohort of 658 NSCLC patients with BMs, 209 were analyzed with a median age of 59; the majority were male (80.9%) and diagnosed with adenocarcinoma (78.9%). Univariate analysis identified significant variables influencing outcomes, including BMs radiotherapy EQD2, BMs count, local thoracic treatment, BMs radiotherapy field, intracranial response, and systemic treatment post-BMs diagnosis. The C + I + A regimen significantly improved median OS to 23.6 months compared to 11.4 months with C and 16.2 months with C + I, with a hazard ratio (HR) of 0.60 (95% CI: 0.43-0.82; P < 0.0001). The two-year OS rate was highest in the C + I + A group at 38.5%, versus 10.5% in C and 20.4% in C + I (P < 0.001). Cox regression and BMA analyses confirmed the stability of BMA in providing HR estimates, yielding area under the curve (AUC) values of 0.785 for BMA and 0.793 for the Cox model, with no significant difference in predictive performance. Subgroup analysis revealed a 71% mortality risk reduction with C + I + A (HR: 0.29; 95% CI: 0.18-0.47; P < 0.0001), showing consistent benefits regardless of patient sex, BMs count, extracranial metastases presence, and local thoracic treatments. Treatment sequence analysis indicated a median OS of 33.4 months for patients starting with A, though not statistically significant (HR: 0.59; P = 0.36). The overall incidence of radiation-induced brain injury was low at 3.3%, with rates in the C, C + I, and C + I + A groups being 3.2%, 4.8%, and 1.9%, respectively (P = 0.683). CONCLUSION: Our study demonstrates the significant benefit of the C + I + A combination therapy in improving OS and reducing mortality risk in NSCLC patients with non-sensitive gene-mutated BMs. The sequential administration of A followed by ICIs shows a promising synergistic effect with cranial radiotherapy, highlighting the potential for optimized treatment sequencing. These findings emphasize the efficacy of tailored combination therapies in complex oncological care and suggest that our approach could lead to meaningful improvements in clinical outcomes for this challenging patient population.

The role of technology-based dance intervention for enhancing wellness: A systematic scoping review and meta-synthesis.

BACKGROUND: Dance represents a promising alternative to traditional physical activity (PA), appealing due to its ease of implementation and its associated health benefits. By incorporating technology-based dance interventions into the development of PA programs, there is potential to significantly increase PA participation and improve fitness levels across diverse population groups. This systematic scoping review and meta-synthesis aimed to investigate the effectiveness of technology-based dance interventions as a means of advancing public health objectives. METHODS: A comprehensive literature review was conducted using various databases ( PubMed, Web of Science, ProQuest, MEDLINE, and SPORTDiscus) to identify pertinent publications. We specifically focused on studies evaluated the impact of technology-based dance interventions on health-related outcomes and PA levels. Methodological quality assessment was carried out using the Cochrane RoB 2 and ROBINS-I tools. Data analysis and theme identification were facilitated using NVivo 14. Additionally, this study was registered on the Open Science Framework at https://osf.io/rynce/registrations. RESULTS: A total of 3135 items identified through the literature search. Following screening, twelve items met the study's inclusion criteria, with an additional three articles located through manual searching. These 15 studies examined on three types of technology-based dance intervention: mobile health (mHealth) combination, online /telerehabilitation classes, and exergaming dance programs. The analysis included 344 participants, with mean ages ranging from 15.3 ± 1.2-73.6 ± 2.2 years. There were five population groups across the studies: middle-aged and older adults, individuals with Parkinson's disease (PD), individuals with stroke, overweight adults, and overweight adolescents. The meta-synthesis revealed three primary themes: Acceptability, Intervention effects, and Technology combinations. CONCLUSION: The advantages highlighted in this scoping review and meta-synthesis of technology-based dance interventions indicating that this type of PA could provide an effective solution to the growing issue of physical inactivity. It also presents a promising strategy for systematically improving fitness and health across populations, particularly among older individuals.

Low-illumination and noisy bridge crack image restoration by deep CNN denoiser and normalized flow module.

When applying deep learning and image processing techniques for bridge crack detection, the obtained images in real-world scenarios have severe image degradation problem. This study focuses on restoring low-illumination bridge crack images corrupted by noise to improve the accuracy of subsequent crack detection and semantic segmentation. The proposed algorithm consists of a deep CNN denoiser and a normalized flow-based brightness enhancement module. By taking the noise spectrum as an input, the deep CNN denoiser restores image at a broad range of noise levels. The normalized flow module, employs a conditional encoder and a reversible network to map the distribution of normally exposed images to a Gaussian distribution, effectively improving the image brightness. Extensive experiments have demonstrated the approach can usefully recover low-illumination images corrupted by noise compared to the state-of-the-art methods. Furthermore, the algorithm presented in this study can also be applied to other image quality restoration with high generalization and robust abilities. And the semantic segmentation accuracy of the restored image is significantly improved.

Prediction of pathological complete response and prognosis in locally advanced rectal cancer.

BACKGROUND: Colorectal cancer is currently the third most common malignant tumor and the second leading cause of cancer-related death worldwide. Neoadjuvant chemoradiotherapy (nCRT) is standard for locally advanced rectal cancer (LARC). Except for pathological examination after resection, it is not known exactly whether LARC patients have achieved pathological complete response (pCR) before surgery. To date, there are no clear clinical indicators that can predict the efficacy of nCRT and patient outcomes. AIM: To investigate the indicators that can predict pCR and long-term outcomes following nCRT in patients with LARC. METHODS: Clinical data of 128 LARC patients admitted to our hospital between September 2013 and November 2022 were retrospectively analyzed. Patients were categorized into pCR and non-pCR groups. Univariate analysis (using the χ 2 test or Fisher's exact test) and logistic multivariate regression analysis were used to study clinical predictors affecting pCR. The 5-year disease-free survival (DFS) and overall survival (OS) rates were calculated using Kaplan-Meier analysis, and differences in survival curves were assessed with the log-rank test. RESULTS: Univariate analysis showed that pretreatment carcinoembryonic antigen (CEA) level, lymphocyte-monocyte ratio (LMR), time interval between neoadjuvant therapy completion and total mesorectal excision, and tumor size were correlated with pCR. Multivariate results showed that CEA ≤ 5 ng/mL (P = 0.039), LMR > 2.73 (P = 0.023), and time interval > 10 wk (P = 0.039) were independent predictors for pCR. Survival analysis demonstrated that patients in the pCR group had significantly higher 5-year DFS rates (94.7% vs 59.7%, P = 0.002) and 5-year OS rates (95.8% vs 80.1%, P = 0.019) compared to the non-pCR group. Tumor deposits (TDs) were significantly correlated with shorter DFS (P = 0.002) and OS (P < 0.001). CONCLUSION: Pretreatment CEA, LMR, and time interval contribute to predicting nCRT efficacy in LARC patients. Achieving pCR demonstrates longer DFS and OS. TDs correlate with poor prognosis.

KEMoS: A knowledge-enhanced multi-modal summarizing framework for Chinese online meetings.

The demand for "online meetings" and "collaborative office work" keeps surging recently, producing an abundant amount of relevant data. How to provide participants with accurate and fast summarizing service has attracted extensive attention. Existing meeting summarizing models overlook the utilization of multi-modal information and the information offsetting during summarizing. In this paper, we develop a knowledge-enhanced multi-modal summarizing framework. Firstly, we construct a three-layer multi-modal meeting knowledge graph, including basic, knowledge, and multi-modal layer, to integrate meeting information thoroughly. Then, we raise a topic-based hierarchical clustering approach, which considers information entropy and difference simultaneously, to capture the semantic evolution of meetings. Next, we devise a multi-modal enhanced encoding strategy, including a sentence-level cross-modal encoder, a joint loss function, and a knowledge graph embedding module, to learn the meeting and topic-level presentations. Finally, when generating summaries, we design a topic-enhanced decoding strategy for the Transformer decoder which mitigates semantic offsetting with the aid of topic information. Extensive experiments show that our proposed work consistently outperforms state-of-the-art solutions on the Chinese meeting dataset, where the ROUGE-1, ROUGE-2, and ROUGE-L are 49.98%, 21.03%, and 32.03% respectively.

Evaluation of the Efficacy of Postoperative Adjuvant Therapy for HER2-Positive Ductal Carcinoma in Situ with Microinvasion.

A retrospective study on 90 eligible HER2+ ductal carcinoma in situ with microinvasion (DCIS-MI) patients was performed with a median follow-up time of 57 months. The baseline was consistent between the 4-cycle and 6-cycle chemotherapy groups. There were more patients with multiple foci of micrometastasis in the target therapy group in the two groups with or without target therapy (p < 0.01). Postoperative chemotherapy with a 4-cycle regimen can achieve the expected therapeutic effect in patients with HER2+ DCIS-MI, but the role of target therapy in HER2+ DCIS-MI patients has not been confirmed.

Green Fruit Detection with a Small Dataset under a Similar Color Background Based on the Improved YOLOv5-AT.

Green fruit detection is of great significance for estimating orchard yield and the allocation of water and fertilizer. However, due to the similar colors of green fruit and the background of images, the complexity of backgrounds and the difficulty in collecting green fruit datasets, there is currently no accurate and convenient green fruit detection method available for small datasets. The YOLO object detection model, a representative of the single-stage detection framework, has the advantages of a flexible structure, fast inference speed and excellent versatility. In this study, we proposed a model based on the improved YOLOv5 model that combined data augmentation methods to detect green fruit in a small dataset with a background of similar color. In the improved YOLOv5 model (YOLOv5-AT), a Conv-AT block and SA and CA blocks were designed to construct feature information from different perspectives and improve the accuracy by conveying local key information to the deeper layer. The proposed method was applied to green oranges, green tomatoes and green persimmons, and the mAPs were higher than those of other YOLO object detection models, reaching 84.6%, 98.0% and 85.1%, respectively. Furthermore, taking green oranges as an example, a mAP of 82.2% was obtained on the basis of retaining 50% of the original dataset (163 images), which was only 2.4% lower than that obtained when using 100% of the dataset (326 images) for training. Thus, the YOLOv5-AT model combined with data augmentation methods can effectively achieve accurate detection in small green fruit datasets under a similar color background. These research results could provide supportive data for improving the efficiency of agricultural production.

A Smartphone-Based M-Health Monitoring System for Arrhythmia Diagnosis.

Deep learning technology has been widely adopted in the research of automatic arrhythmia detection. However, there are several limitations in existing diagnostic models, e.g., difficulties in extracting temporal information from long-term ECG signals, a plethora of parameters, and sluggish operation speed. Additionally, the diagnosis performance of arrhythmia is prone to mistakes from signal noise. This paper proposes a smartphone-based m-health system for arrhythmia diagnosis. First, we design a cycle-GAN-based ECG denoising model which takes real-world noise signals as input and aims to produce clean ECG signals. In order to train its two generators and two discriminators simultaneously, we explore an unsupervised pre-training strategy to initialize the generator and accelerate the convergence speed during training. Second, we propose an arrhythmia diagnosis model based on the time convolution network (TCN). This model can identify 34 common arrhythmia events using eight-lead ECG signals, and we deploy such a model on the Android platform to develop an at-home ECG monitoring system. Experimental results have demonstrated that our approach outperforms the existing noise reduction methods and arrhythmia diagnosis models in terms of denoising effect, recognition accuracy, model size, and operation speed, making it more suitable for deployment on mobile devices for m-health monitoring services.

Comparison of Powered versus Manual Tooth Brushing for Safety and Efficacy in Patients with Gingivitis: A Randomised, Multicentre Clinical Trial in China.

OBJECTIVE: To evaluate the effects of powered and manual tooth brushing on gingival inflammation in a Chinese population with mild to moderate gingivitis. METHODS: The present randomised, single-blind, parallel clinical trial was conducted in five cities in China. Generally healthy participants aged 18 to 65 years, who were non-smokers and had at least 20 sites of gingival bleeding, were included as eligible subjects. The subjects were randomly assigned to either the powered tooth brushing (PTB) group or standard manual tooth brushing (MTB) group. All subjects were supplied with a fluoride-containing toothpaste, Gingival Bleeding Index (GBI), Modified Gingival Index (MGI) and the Turesky modification of the Quigley-Hein Plaque Index (MPI) were used to evaluate the outcomes. RESULTS: A total of 235 subjects completed the study, 118 in the PTB group and 117 in the MTB group. The mean age and sex distribution for the PTB and MTB groups were 34.40 ± 9.99 years, 89 women and 29 men, and 34.20 ± 10.14 years, 82 women and 35 men, respectively. After 6 months, the percentage decrease in MGI was 26.150% ± 26.897% for the PTB group and 14.768% ± 38.544% for the MTB group (P = 0.0092). Statistically significant differences between types of tooth brushing were also observed at 6 months for GBI, and at all time points for MPI. CONCLUSION: Tooth brushing with a powered toothbrush twice a day was shown to be more effective than use of a manual toothbrush in reducing gingival inflammation, gingival bleeding and surface plaque after a 6-month period. Both kinds of toothbrushes were safe for the oral tissues.