Inflammation and hypoxia are involved in numerous cancer progressions. Reportedly, the toll-like receptor 4 (TLR4)/nuclear factor kappa B (NF-κB) pathway and hypoxia-inducible factor-1α (HIF-1α) are activated and closely related to the chemoresistance and poor prognosis of epithelial ovarian cancer (EOC). However, the potential correlation between TLR4/NF-κB and HIF-1α remains largely unknown in EOC. In our study, the possible positive correlation among TLR4, NF-κB, and HIF-1α proteins was investigated in the EOC tissues. Our in vitro results demonstrated that LPS can induce and activate HIF-1α through the TLR4/NF-κB signaling in A2780 and SKOV3 cells. Moreover, hypoxia-induced TLR4 expression and the downstream transcriptional activity of NF-κB were HIF-1α-dependent. The cross talk between the TLR4/NF-κB signaling pathway and HIF-1α was also confirmed in the nude mice xenograft model. Therefore, we first proposed the formation of a TLR4/NF-κB/HIF-1α loop in EOC. The positive feedback loop enhanced the susceptibility and responsiveness to inflammation and hypoxia, which synergistically promote the initiation and progression of EOC. The novel mechanism may act as a future therapeutic candidate for the treatment of EOC.

Introduction: It is well-known that maize and wheat are main food crops in the world. Thus, promoting high quality and abundant maize and wheat crops guarantees the development of the grain industry, which is needed to support world hunger. Weeds seriously affect the growing environment of maize, wheat, and their seedlings, resulting in low crop yields and poor seedling quality. This paper focuses on the identification of maize and wheat seedlings and field weeds using deep learning.Methods: Maize and wheat seedlings and field weeds are the research objects. A weed identification model based on the UNet network model and ViT classification algorithm is proposed. The model uses UNet to segment images. A Python Imaging Library algorithm is used to segment green plant leaves from binary images, to enhance the feature extraction of green plant leaves. The segmented image is used to construct a ViT classification model, which improves the recognition accuracy of maize and wheat seedlings and weeds in the field.Results: This paper uses average accuracy, average recall, and F1 score to evaluate the performance of the model. The accuracy rate (for accurately identifying maize and wheat seedlings and weeds in the field) reaches 99.3%. Compared with Alexnet, VGG16, and MobileNet V3 models, the results show that the recognition effect of the model trained using the method presented in this paper is better than other existing models.Discussion: Thus, this method, which accurately disambiguates maize and wheat seedlings from field weeds can provide effective information support for subsequent field pesticide spraying and mechanical weeding.

Crush syndrome (CS), also known as traumatic rhabdomyolysis, is a syndrome with a wide clinical spectrum; it is caused by external compression, which often occurs in earthquakes, wars, and traffic accidents, especially in large-scale disasters. Crush syndrome is the second leading cause of death after direct trauma in earthquakes. A series of clinical complications caused by crush syndrome, including hyperkalemia, myoglobinuria, and, in particular, acute kidney injury (AKI), is the main cause of death in crush syndrome. The early diagnosis of crush syndrome, the correct evaluation of its severity, and accurate predictions of a poor prognosis can provide personalized suggestions for rescuers to carry out early treatments and reduce mortality. This review summarizes various methods for the diagnostic and predictive evaluation of crush syndrome, including urine dipstick tests for a large number of victims, traditional and emerging biomarkers, imaging-assisted diagnostic methods, and developed evaluation models, with the aim of providing materials for scholars in this research field.

Fire smoke enters the human lungs through the respiratory tract. The damage to the respiratory tract and lung tissue is known as smoke inhalation injury (SII). Fire smoke can irritate airway epithelium cells, weaken endothelial cell adhesion and lyse alveolar type II epithelia cells, leading to emphysema, decreased lung function, pneumonia and risk of acute lung injury/acute respiratory distress syndrome (ARDS). The purpose of the present study was to analyze the clinical characteristics of patients with SII and the risk factors affecting their prognosis. A total of 103 patients with SII admitted between January 2016 to December 2021 to the Burns Unit of the Characteristic Medical Center of Chinese People's Armed Police Force and 983 Hospital of the Joint Logistics Support Force of the Chinese People's Liberation Army were selected for the present study. The demographics and clinical features between different severities of SII were analyzed. Univariate/multivariate logistic regression was used to analyze the potential predictors for severity, ARDS and mortality of patients with SII. Receiver operating characteristic (ROC) curves were used to screen independent risk factors and identify their prediction accuracy. It was concluded that total body surface area (TBSA), III burn area (of total %TBSA), cases of respiratory infections, ARDS morbidity, mortality, acute physiology and chronic health evaluation II, lung injury prediction score, lactic acid, white blood cells (WBC), alanine transaminase, blood urea nitrogen, serum creatinine and uric acid were indicators that were raised with increasing severity of SII. However red blood cells, hemoglobin, platelet count, total protein, albumin, and albumin/globulin were decreased with the increasing severity of SII (P20.91 (109/l) was a reliable indicator for severe SII. Lactic acid >9.60 (mmol/l) demonstrated a high degree of accuracy in predicting ARDS development in patients with SII. Hemoglobin <83.00 (g/l) showed a high degree of accuracy in predicting mortality. In summary, the highlighted assessment parameters could be used to contribute to devising improved treatment plans to preempt worsening conditions (such as shock, ARDS, multiple organ dysfunction syndrome and death).

Purpose: Digital interventions for adolescent mental health are emerging in high-income countries, but have faced challenges and are scarce in China. This study investigated the effect of a short video-based mental health intervention on depressive symptoms in Chinese adolescents. Methods: A three-arm cluster randomized controlled trial was conducted in four junior high schools in Shanghai from December 2020 to December 2021 with the measurement at baseline, 6 months after study entry, and 12 months. Outcomes were collected by self-completed questionnaires administered by teachers masked to allocation. The primary outcome was depressive symptoms assessed by the Depression Self-Rating Scale for Children (DSRSC). Mixed effects models were used to compare psychologist-led intervention (n=428 students) and teacher-led intervention (n=385) including six short video-based sessions to usual school provision (n=751). Results: Using intention-to-treat analyses, psychologist-led intervention showed more reduction in depressive symptoms compared to usual school provision at 6 months (coefficient − 1.00, 95% CI − 1.94 to − 0.05), but not at 12 months. Using per-protocol analyses among participants who watched at least three video episodes, both psychologist-led (− 1.14, − 2.20 to − 0.09) and teacher-led intervention (− 1.23, − 2.45 to − 0.02) reduced depressive symptoms compared to usual school provision at 6 months, and the effect of teacher-led intervention persisted at 12 months (− 1.58, − 3.13 to − 0.03). Further exploration found that compared with urban students, the between-group differences for depressive symptoms in rural students were more significant (p< 0.05 for interaction) and the effects were maintained at 12 months. Conclusion: The short video-based mental health intervention showed potential to reduce depressive symptoms among Chinese adolescents, and the effects were more significant if the minimum video viewing frequency was reached.

We reported an outbreak of tuberculosis in internet cafes amongst young migrants without fixed abode in Shanghai. Our results implied these populations of young migrants who get lost in the internet cafes of the big cities were at high risk for TB transmission and need more attention from health-care system.