The \u3ci\u3eDrosophila\u3c/i\u3e T-box Transcription Factor Midline Functions within the Insulin/AKT and c-Jun-N-terminal Kinase Signaling Pathways to Regulate Interomatidial Bristle Formation and Cell Survival

From a genetic and allelic modifier screen, we report that the Drosophila melanogaster T-box transcription factor midline (mid), a homolog to the human TBX20 gene, interacts with dFOXO within the insulin receptor (InR) and the c-Jun-N-terminal kinase (JNK) signaling pathways to regulate interommatidial bristle (IOB) formation. Previous studies have identified mid’s role in cell fate specification of sensory organ precursor cells in conjunction with the Notch-Delta signaling pathway (Das et al., 2013). The Notch, InR, and JNK signaling pathways regulate dFOXO activity under conditions of stress. Thus, we determined the effects of oxidative stress and metabolic stress by exposing mid-RNAi flies to paraquat and starvation conditions, respectively. We found that oxidative stress suppressed the mid-RNAi phenotype while starvation had no significant effect. We next assayed Mid and H15, a paralog of Mid, via Western blot analysis and report that Mid exhibits a nucleocytoplasmic distribution pattern that is altered within the mid-RNAi mutant while H15 was found exclusively within the cytoplasmic fraction. This opens the possibility that Mid and/or H15 may regulate cytoplasmic targets upstream of dFOXO. The evidence suggests that Mid utilizes the InR, JNK, and Notch signaling pathways to regulate cell fate specification, differentiation, and survival during third instar larval development

Hydrological Variation Characteristics of Rivers in Humid Region: Oujiang River, China

AbstractOujiang River was selected as the case study, and a dataset of daily flow series at Xuren Station was used to explore the hydrologic characteristics of rivers in humid areas, by using the ‘Indicators of Hydrologic Alteration’ approach and ‘Range of Variability Approach’. Results showed that the overall alteration of the hydrological regime for Oujiang River belonged to the low alteration category, and some key eco-hydrological characteristics should be protected in certain key periods to maintain the integrality and health status of river ecosystems

Microrna Expression Profile and Differentially-Expressed Genes in Prolactinomas Following Bromocriptine Treatment

Little is known about the function of microRNAs in prolactinomas treated with bromocriptine. The aim of the study was to explore the microRNAs associated with bromocriptine-treated prolactinomas. Six prolactinoma samples were selected according to whether they received bromocriptine treatment or not before microsurgery, and microRNA expression profiles of bromocriptine-treated and untreated prolactinomas were screened by the miRCURY LNA Array. The differentially expressed microRNAs in microarrays were further validated by stem-loop real-time PCR and subjected to gene ontology analysis and KEGG pathway analysis. In addition, related genes of microRNAs were analyzed by qRT-PCR in 15 prolactinoma samples. The initial analysis by microarrays generated a list of 80 upregulated microRNAs and 71 downregulated microRNAs in treated prolactinomas compared to untreated prolactinomas. miR-206, miR-516b and miR-550 were confirmed to be significantly upregulated, while miR-671-5p was confirmed to be significantly downregulated in treated prolactinomas by qRT-PCR. microRNA-mRNA network analysis integrating GO and KEGG pathway annotation displayed some critical factors. Platelet-derived growth factor α polypeptide (PDGFA) and bone morphogenetic protein 4 (BMP4), were verified to be differentially expressed between the two groups. PDGFA was significantly upregulated in treated prolactinomas, while BMP4 was significantly downregulated in treated prolactinomas. Our study reveals differential expression of microRNAs in prolactinoma after pharmacotherapy. Specific microRNAs may be involved in the inhibition or promotion of prolactinoma tumor growth impacted by bromocriptine pharmacotherapy. PDGFA and BMP4 may be involved in the pharmacotherapy mechanism of prolactinoma

A review of Brucea javanica: metabolites, pharmacology and clinical application

This review examines advances in the metabolites, pharmacological research, and therapeutic applications of the medicinal fruit of Brucea javanica (L.) Merr. Brucea javanica (BJ) is derived from the fruit of the Brucea javanica (L.) Merr. There are nearly 200 metabolites present in BJ, and due to the diversity of its metabolites, BJ has a wide range of pharmacological effects. The traditional pharmacological effects of BJ include anti-dysentery, anti-malaria, etc. The research investigating the contemporary pharmacological impacts of BJ mainly focuses on its anti-tumor properties. In the article, the strong monomeric metabolites among these pharmacological effects were preliminarily screened. Regarding the pharmacological mechanism of action, current research has initially explored BJ’s pharmacological agent and molecular signaling pathways. However, a comprehensive system has yet to be established. BJ preparations have been utilized in clinical settings and have demonstrated effectiveness. Nevertheless, clinical research is primarily limited to observational studies, and there is a need for higher-quality research evidence to support its clinical application. There are still many difficulties and obstacles in studying BJ. However, it is indisputable that BJ is a botanical drugs with significant potential for application, and it is expected to have broader global usage

Development of a deep learning model for early gastric cancer diagnosis using preoperative computed tomography images

BackgroundGastric cancer is a highly prevalent and fatal disease. Accurate differentiation between early gastric cancer (EGC) and advanced gastric cancer (AGC) is essential for personalized treatment. Currently, the diagnostic accuracy of computerized tomography (CT) for gastric cancer staging is insufficient to meet clinical requirements. Many studies rely on manual marking of lesion areas, which is not suitable for clinical diagnosis.MethodsIn this study, we retrospectively collected data from 341 patients with gastric cancer at the First Affiliated Hospital of Wenzhou Medical University. The dataset was randomly divided into a training set (n=273) and a validation set (n=68) using an 8:2 ratio. We developed a two-stage deep learning model that enables fully automated EGC screening based on CT images. In the first stage, an unsupervised domain adaptive segmentation model was employed to automatically segment the stomach on unlabeled portal phase CT images. Subsequently, based on the results of the stomach segmentation model, the image was cropped out of the stomach area and scaled to a uniform size, and then the EGC and AGC classification models were built based on these images. The segmentation accuracy of the model was evaluated using the dice index, while the classification performance was assessed using metrics such as the area under the curve (AUC) of the receiver operating characteristic (ROC), accuracy, sensitivity, specificity, and F1 score.ResultsThe segmentation model achieved an average dice accuracy of 0.94 on the hand-segmented validation set. On the training set, the EGC screening model demonstrated an AUC, accuracy, sensitivity, specificity, and F1 score of 0.98, 0.93, 0.92, 0.92, and 0.93, respectively. On the validation set, these metrics were 0.96, 0.92, 0.90, 0.89, and 0.93, respectively. After three rounds of data regrouping, the model consistently achieved an AUC above 0.9 on both the validation set and the validation set.ConclusionThe results of this study demonstrate that the proposed method can effectively screen for EGC in portal venous CT images. Furthermore, the model exhibits stability and holds promise for future clinical applications

DataSheet_1_Development of a deep learning model for early gastric cancer diagnosis using preoperative computed tomography images.docx

BackgroundGastric cancer is a highly prevalent and fatal disease. Accurate differentiation between early gastric cancer (EGC) and advanced gastric cancer (AGC) is essential for personalized treatment. Currently, the diagnostic accuracy of computerized tomography (CT) for gastric cancer staging is insufficient to meet clinical requirements. Many studies rely on manual marking of lesion areas, which is not suitable for clinical diagnosis.MethodsIn this study, we retrospectively collected data from 341 patients with gastric cancer at the First Affiliated Hospital of Wenzhou Medical University. The dataset was randomly divided into a training set (n=273) and a validation set (n=68) using an 8:2 ratio. We developed a two-stage deep learning model that enables fully automated EGC screening based on CT images. In the first stage, an unsupervised domain adaptive segmentation model was employed to automatically segment the stomach on unlabeled portal phase CT images. Subsequently, based on the results of the stomach segmentation model, the image was cropped out of the stomach area and scaled to a uniform size, and then the EGC and AGC classification models were built based on these images. The segmentation accuracy of the model was evaluated using the dice index, while the classification performance was assessed using metrics such as the area under the curve (AUC) of the receiver operating characteristic (ROC), accuracy, sensitivity, specificity, and F1 score.ResultsThe segmentation model achieved an average dice accuracy of 0.94 on the hand-segmented validation set. On the training set, the EGC screening model demonstrated an AUC, accuracy, sensitivity, specificity, and F1 score of 0.98, 0.93, 0.92, 0.92, and 0.93, respectively. On the validation set, these metrics were 0.96, 0.92, 0.90, 0.89, and 0.93, respectively. After three rounds of data regrouping, the model consistently achieved an AUC above 0.9 on both the validation set and the validation set.ConclusionThe results of this study demonstrate that the proposed method can effectively screen for EGC in portal venous CT images. Furthermore, the model exhibits stability and holds promise for future clinical applications.</p

The Inactivation of JAK2/STAT3 Signaling and Desensitization of M1 mAChR in Minimal Hepatic Encephalopathy (MHE) and the Protection of Naringin Against MHE

Background: We previously reported that elevation of intracranial dopamine (DA) levels from cirrhotic livers is implicated in the pathogenesis of minimal hepatic encephalopathy (MHE). Intracellular events in neurons, which lead to memory loss in MHE by elevated DA, however, remain elusive. Methods: In our present study, an MHE rat model, a DA - intracerebroventricularly (i.c.v.) injected rat model and DA-treated primary cortical neurons (PCNs) were used to study this issue using behavioral tests, double-labeled fluorescent staining, immunoblotting, and semi-quantitative RT-PCR. Results: Cognitive impairment was observed in MHE rats and DA (10 µg, i.c.v.)-treated rats. The levels of DA in the cerebral cortex of both MHE and DA (10 µg)-treated rats were increased. DA conversely modulated the p-JAK2/p-STAT3 levels in PCNs. In accordance, DA downregulated an anacetylcholine-producing enzyme, choline acetyltransferase (ChAT), and desensitized the M1-type muscarinic acetylcholine receptor (M1 mAChR). Furthermore, naringin completely restored cognitive function in MHE/DA (10 µg)-treated models by activating the JAK2/STAT3 axis, paralleling the upregulation of ChAT and sensitization of M1 mAChR. Conclusions: We propose a hypothesis accounting for memory impairment related to MHE: DA-dependent inactivation of the JAK2/STAT3 axis causes memory loss through cholinergic dysfunction. Our findings provide not only a novel pathological hallmark in MHE but also a novel target in MHE therapy

Calpain inhibitor MDL28170 improves the transplantation-mediated therapeutic effect of bone marrow-derived mesenchymal stem cells following traumatic brain injury

Abstract Background Studies have shown that transplantation of bone marrow-derived mesenchymal stem cells (BMSCs) protects against brain damage. However, the low survival number of transplanted BMSCs remains a pertinent challenge and can be attributed to the unfavorable microenvironment of the injured brain. It is well known that calpain activation plays a critical role in traumatic brain injury (TBI)-mediated inflammation and cell death; previous studies showed that inhibiting calpain activation is neuroprotective after TBI. Thus, we investigated whether preconditioning with the calpain inhibitor, MDL28170, could enhance the survival of BMSCs transplanted at 24 h post TBI to improve neurological function. Methods TBI rat model was induced by the weight-drop method, using the gravitational forces of a free falling weight to produce a focal brain injury. MDL28170 was injected intracranially at the lesion site at 30 min post TBI, and the secretion levels of neuroinflammatory factors were assessed 24 h later. BMSCs labeled with green fluorescent protein (GFP) were locally administrated into the lesion site of TBI rat brains at 24 h post TBI. Immunofluorescence and histopathology were performed to evaluate the BMSC survival and the TBI lesion volume. Modified neurological severity scores were chosen to evaluate the functional recovery. The potential mechanisms by which MDL28170 is involved in the regulation of inflammation signaling pathway and cell apoptosis were determined by western blot and immunofluorescence staining. Results Overall, we found that a single dose of MDL28170 at acute phase of TBI improved the microenvironment by inhibiting the inflammation, facilitated the survival of grafted GFP-BMSCs, and reduced the grafted cell apoptosis, leading to the reduction of lesion cavity. Furthermore, a significant neurological function improvement was observed when BMSCs were transplanted into a MDL28170-preconditioned TBI brains compared with the one without MDL28170-precondition group. Conclusions Taken together, our data suggest that MDL28170 improves BMSC transplantation microenvironment and enhances the neurological function restoration after TBI via increased survival rate of BMSCs. We suggest that the calpain inhibitor, MDL28170, could be pursued as a new combination therapeutic strategy to advance the effects of transplanted BMSCs in cell-based regenerative medicine