Protective effect and pharmacokinetics of dihydromyricetin nanoparticles on oxidative damage of myocardium.

PURPOSE: This study aims to investigate the protective mechanism of dihydromyricetin PLGA nanoparticles (DMY-PLGA NPs) against myocardial ischemia-reperfusion injury (MIRI) in vitro and the improvement of oral bioavailability in vivo. METHODS: DMY-PLGA NPs was prepared and characterized by emulsifying solvent volatilization, and the oxidative stress model of rat H9c2 cardiomyocyte induced by H2O2 was established. After administration, cell survival rate, lactate dehydrogenase (LDH), malondialdehyde (MDA) and superoxide dismutase (SOD) were detected, and the expressions of PGC1α and PPARα were detected by western blot (WB). At the same time, the pharmacokinetics in rats were studied to explore the improvement of bioavailability. RESULTS: DMY-PLGA NPs can significantly increase cell survival rate, decrease LDH and MDA content, increase SOD content and PGC1α、PPARα protein expression. Compared with DMY, the peak time of DMY-PLGA NPs was extended (P<0.1), and the bioavailability was increased by 2.04 times. CONCLUSION: DMY-PLGA NPs has a significant protective effect on H9c2 cardiomyocytes, which promotes the absorption of DMY and effectively improves bioavailability.

M3YOLOv5: Feature enhanced YOLOv5 model for mandibular fracture detection.

BACKGROUND: It is very important to detect mandibular fracture region. However, the size of mandibular fracture region is different due to different anatomical positions, different sites and different degrees of force. It is difficult to locate and recognize fracture region accurately. METHODS: To solve these problems, M3YOLOv5 model is proposed in this paper. Three feature enhancement strategies are designed, which improve the ability of model to locate and recognize mandibular fracture region. Firstly, Global-Local Feature Extraction Module (GLFEM) is designed. By effectively combining Convolutional Neural Network (CNN) and Transformer, the problem of insufficient global information extraction ability of CNN is complemented, and the positioning ability of the model to the fracture region is improved. Secondly, in order to improve the interaction ability of context information, Deep-Shallow Feature Interaction Module (DSFIM) is designed. In this module, the spatial information in the shallow feature layer is embedded to the deep feature layer by the spatial attention mechanism, and the semantic information in the deep feature layer is embedded to the shallow feature layer by the channel attention mechanism. The fracture region recognition ability of the model is improved. Finally, Multi-scale Multi receptive-field Feature Mixing Module (MMFMM) is designed. Deep separate convolution chains are used in this modal, which is composed by multiple layers of different scales and different dilation coefficients. This method provides richer receptive field for the model, and the ability to detect fracture region of different scales is improved. RESULTS: The precision rate, mAP value, recall rate and F1 value of M3YOLOv5 model on mandibular fracture CT data set are 97.18%, 96.86%, 94.42% and 95.58% respectively. The experimental results show that there is better performance about M3YOLOv5 model than the mainstream detection models. CONCLUSION: The M3YOLOv5 model can effectively recognize and locate the mandibular fracture region, which is of great significance for doctors' clinical diagnosis.

Prediction and analysis of quality markers for Chuantieling gel patches based on HPLC fingerprinting and network pharmacology.

The Chuantieling gel patch (CGP), a traditional Chinese medicine compound, is an external treatment for asthma. It has shown remarkable effectiveness in alleviating asthma-related airway hyperresponsiveness and inflammation. Nevertheless, there is currently no information available regarding the analysis of quality markers for CGP, and there is a need for further improvement in quality control research. In this study, we developed an HPLC fingerprinting method for CGP and conducted a comprehensive methodological investigation. We assessed the similarity among 10 batches of CGP, identified common peaks, and quantified the content of seven major quality markers. Furthermore, we built a network pharmacology-based 'active ingredients-targets-pathways-diseases' network to forecast the potential mechanisms of action for the primary active components in asthma treatment. Our findings demonstrated that the developed CGP fingerprinting and content determination methods were consistent and trustworthy. We verified the existence of 25 shared peaks and successfully identified 7 chromatographic peaks, including sinigrin thiocyanate, ephedrine hydrochloride, methyleugenol, imperatorin, cinnamaldehyde, emodin, and 6-gingerol, using reference standards. The network pharmacology analysis suggested that these seven active components may target proteins such as STAT3 (signal transducer and activator of transcription 3), MAPK3 (mitogen-activated protein kinase 3), and TP53 (tumor protein P53) and influence various diseases through pathways including cancer pathways, hepatitis B, and PI3K-Akt (phosphoinositide 3-kinase-protein kinase B) signaling. This study provides insight into the complex multicomponent composition of CGP, and the predictive analysis through network pharmacology sets the stage for uncovering the mechanisms responsible for the therapeutic effects of CGP.

Therapeutic Potential of Ginsenoside Rb1-PLGA Nanoparticles for Heart Failure Treatment via the ROS/PPARα/PGC1α Pathway.

(1) Background: Ginsenoside Rb1-PLGA nanoparticles (GRb1@PLGA@NPs) represent a novel nanotherapeutic system, yet their therapeutic efficacy and underlying mechanisms for treating heart failure (HF) remain unexplored. This study aims to investigate the potential mechanisms underlying the therapeutic effects of GRb1@PLGA@NPs in HF treatment; (2) Methods: The left anterior descending coronary artery ligation was employed to establish a HF model in Sprague-Dawley rats, along with an in vitro oxidative stress model using H9c2 myocardial cells. Following treatment with GRb1@PLGA@NPs, cardiac tissue pathological changes and cell proliferation were observed. Additionally, the serum levels of biomarkers such as NT-proBNP, TNF-α, and IL-1ß were measured, along with the expression of the ROS/PPARα/PGC1α pathway; (3) Results: GRb1@PLGA@NPs effectively ameliorated the pathological status of cardiac tissues in HF rats, mitigated oxidative stress-induced myocardial cell damage, elevated SOD and MMP levels, and reduced LDH, MDA, ROS, NT-proBNP, TNF-α, and IL-1ß levels. Furthermore, the expression of PPARα and PGC1α proteins was upregulated; (4) Conclusions: GRb1@PLGA@NPs may attenuate myocardial cell injury and treat HF through the ROS/PPARα/PGC1α pathway.

Photocatalytic Three-Component Reaction for the Synthesis of Multifunctional Diaryl Sulfides.

A photocatalytic three-component reaction of a nitroarene, a thiophenol, and a ketone for the synthesis of multifunctional diaryl sulfides was reported using a nitro group as the nitrogen source and thiophenol as the sulfur source. Thiophenol also serves as a proton donor to reduce nitroarene to arylamine as a key intermediate for the formation of C-N and C-S bonds. Good functional group tolerance and mild reaction conditions make this method have practical synthetic value for diversified multifunctional diaryl sulfides.

GMRE-iUnet: Isomorphic Unet fusion model for PET and CT lung tumor images.

Lung tumor PET and CT image fusion is a key technology in clinical diagnosis. However, the existing fusion methods are difficult to obtain fused images with high contrast, prominent morphological features, and accurate spatial localization. In this paper, an isomorphic Unet fusion model (GMRE-iUnet) for lung tumor PET and CT images is proposed to address the above problems. The main idea of this network is as following: Firstly, this paper constructs an isomorphic Unet fusion network, which contains two independent multiscale dual encoders Unet, it can capture the features of the lesion region, spatial localization, and enrich the morphological information. Secondly, a Hybrid CNN-Transformer feature extraction module (HCTrans) is constructed to effectively integrate local lesion features and global contextual information. In addition, the residual axial attention feature compensation module (RAAFC) is embedded into the Unet to capture fine-grained information as compensation features, which makes the model focus on local connections in neighboring pixels. Thirdly, a hybrid attentional feature fusion module (HAFF) is designed for multiscale feature information fusion, it aggregates edge information and detail representations using local entropy and Gaussian filtering. Finally, the experiment results on the multimodal lung tumor medical image dataset show that the model in this paper can achieve excellent fusion performance compared with other eight fusion models. In CT mediastinal window images and PET images comparison experiment, AG, EI, QAB/F, SF, SD, and IE indexes are improved by 16.19%, 26%, 3.81%, 1.65%, 3.91% and 8.01%, respectively. GMRE-iUnet can highlight the information and morphological features of the lesion areas and provide practical help for the aided diagnosis of lung tumors.

Preparation, characterization and in vivo pharmacokinetic study of ginsenoside Rb1-PLGA nanoparticles.

This study aimed to construct a Ginsenoside Rb1-PLGA nano drug delivery system, optimize its preparation process, characterize and evaluate the resulting Ginsenoside Rb1-PLGA Nanoparticles (GRb1@PLGA@NPs). GRb1@PLGA@NPs were prepared using the emulsion solvent evaporation method. The optimal preparation process was determined using Plackett-Burman design combined with Box-Behnken experiments. Physical characterization and in vitro release studies were conducted. LC-MS/MS technique was employed to investigate the pharmacokinetic characteristics of GRb1 and GRb1@PLGA@NPs in rat plasma. The optimal preparation process yielded GRb1@PLGA@NPs with a particle size of 120.63 nm, polydispersity index (PDI) of 0.172, zeta potential of - 22.67 mV, encapsulation efficiency of 75%, and drug loading of 11%. In vitro release demonstrated sustained drug release. Compared to GRb1, GRb1@PLGA@NPs exhibited a shortened time to peak concentration by approximately 0.72-fold. The area under the plasma concentration-time curve significantly increased to 4.58-fold of GRb1. GRb1@PLGA@NPs formulated using the optimal process exhibited uniform distribution and stable quality, its relative oral bioavailability was significantly improved compared to free GRb1.

Diagnostic value of exfoliated tumor cells combined with DNA methylation in bronchoalveolar lavage fluid for lung cancer.

BACKGROUND: To evaluate the diagnostic value of exfoliated tumor cells (ETCs) numbers combined with DNA methylation levels in bronchoalveolar lavage fluid (BALF) in lung cancer. METHODS: BALF samples were collected from 43 patients with lung cancer and 23 with benign lung disease. ETCs were detected by the nano-enrichment method, and the methylation status of the short stature homeobox gene 2 (SHOX2) and the RAS association domain family 1, isoform A (RASSF1A) gene were detected by RT-PCR. The diagnostic value of each metric was evaluated by receiver operating characteristic curve analysis, specificity and sensitivity. RESULTS: The sensitivity/specificity of RASSF1A and SHOX2 methylation detection were 44.12%/76.47% and 93.75%/87.50%, respectively. When "RASSF1A/SHOX2 methylation" was used as a positive result, the sensitivity increased to 88.24%, and the specificity decreased to 81.25%. When "RASSF1A + SHOX methylation" was used as positive, the sensitivity was reduced to 32.35%, but the specificity was increased to 100.00%. The sensitivity and specificity of ETCs detection in BALF were 89.47% and 16.67%, respectively. When "SHOX2/RASSF1A methylation + ETCs was used as a positive result, the sensitivity and specificity of the detection were 79.31% and 81.82%, respectively. When "SHOX2 + RASSF1A + ETCs" was used as positive, the sensitivity was 34.48% and the specificity was 90.91%. Receiver operating characteristic curve analysis showed that when SHOX2, RASSF1A methylation and ETCs were combined, the diagnostic sensitivity increased to 0.778. CONCLUSION: ETCs counting in combination with SHOX2 and RASSF1A methylation assays in BALF samples has demonstrated excellent sensitivity for lung cancer diagnosis and is an effective complementary tool for clinical diagnosis of lung cancer.

CCGL-YOLOV5:A cross-modal cross-scale global-local attention YOLOV5 lung tumor detection model.

BACKGROUND: Multimodal medical image detection is a key technology in medical image analysis, which plays an important role in tumor diagnosis. There are different sizes lesions and different shapes lesions in multimodal lung tumor images, which makes it difficult to effectively extract key features of lung tumor lesions. METHODS: A Cross-modal Cross-scale Clobal-Local Attention YOLOV5 Lung Tumor Detection Model (CCGL-YOLOV5) is proposed in this paper. The main works are as follows: Firstly, the Cross-Modal Fusion Transformer Module (CMFTM) is designed to improve the multimodal key lesion feature extraction ability and fusion ability through the interactive assisted fusion of multimodal features; Secondly, the Global-Local Feature Interaction Module (GLFIM) is proposed to enhance the interaction ability between multimodal global features and multimodal local features through bidirectional interactive branches. Thirdly, the Cross-Scale Attention Fusion Module (CSAFM) is designed to obtain rich multi-scale features through grouping multi-scale attention for feature fusion. RESULTS: The comparison experiments with advanced networks are done. The Acc, Rec, mAP, F1 score and FPS of CCGL-YOLOV5 model on multimodal lung tumor PET/CT dataset are 97.83%, 97.39%, 96.67%, 97.61% and 98.59, respectively; The experimental results show that the performance of CCGL-YOLOV5 model in this paper are better than other typical models. CONCLUSION: The CCGL-YOLOV5 model can effectively use the multimodal feature information. There are important implications for multimodal medical image research and clinical disease diagnosis in CCGL-YOLOV5 model.

Transcriptome Data Reveal Geographic Heterogeneity in Gene Expression in Patients with Prostate Cancer.

Background: The incidence of prostate cancer (PC) exhibits geographical heterogeneity. However, the metabolic mechanisms underlying this geographic heterogeneity remain unclear. This study aimed to reveal the metabolic mechanism of the geographic heterogeneity in the incidence of PC.This study aimed to investigate the anti-cancer effects of different gum extracts on metabolic changes and their impact on gene expression in HT-29 cell. Methods: Transcriptomic data from public databases were obtained and analyzed to screen geographic-differentially expressed genes and metabolic pathways. Associations between these differentially expressed genes and the incidence of PC were determined to identify genes that were highly associated with PC incidence. A co-expression network analysis was performed to identify geographic-specific regulatory pathways. Results: A total of 175 differentially expressed genes were identified in four countries and were associated with the regulation of DNA replication and the metabolism of pyrimidine, nucleotides, purines, and galactose.Additionally, the expression of the genes CLVS2, SCGB1A1, KCNK3, HHIPL2, MMP26, KCNJ15, and PNMT was highly correlated with the incidence of PC. Geographic-specific differentially expressed genes in low-incidence areas were highly correlated with KCNJ15, MMP26, KCNK3, and SCCB1A1, which play a major role in ion channel-related functions. Conclusions: This study suggests that geographic heterogeneity in PC incidence is associated with the expression levels of genes associated with amino acid metabolism, lipid metabolism, and ion channels.

Calycosin attenuates the inflammatory damage of microglia induced by oxygen and glucose deprivation through the HMGB1/TLR4/NF-κB signaling pathway.

Stroke seriously threatens human life and health worldwide, but only very few effective stroke medicines are currently available. Our previous studies have indicated that the phytoestrogen calycosin exerts neuroprotective effects in cerebral ischemia and reperfusion injury rats. Therefore, the objective of this study is to further explore the protective effect of calycosin on inflammatory injury in microglia after oxygen-glucose deprivation/reoxygenation (OGD/R) and to clarify whether its protective effect is related to the HMGB1/TLR4/NF-κB signaling pathway. Here, the OGD/R model of rodent microglia is established in vitro to simulate cerebral ischemia-reperfusion injury. Through the CCK-8 test, ELISA, qRT-PCR, and western blot analysis, we find that the activity of microglia is decreased, the expressions of HMGB1 and TLR4 and the phosphorylation of NF-κB (p-NF-κB) are increased, and the releases of the inflammatory factors IL-6, IL-1ß, and TNF-α are increased after OGD/R. Pretreatment with calycosin could ameliorate these states, increase cell viability, reduce HMGB1, TLR4 and p-NF-κB expression, and reduce inflammatory cytokine production. In addition, the effect of calycosin is similar to that of TAK-242 (an inhibitor of TLR4), and the effect of the combined treatment is better than that of the single treatment. The results indicate that calycosin protects microglia from OGD/R injury and reduces the inflammatory response. Calycosin might alleviate cerebral ischemia-reperfusion injury by inhibiting the HMGB1/TLR4/NF-κB pathway.

Assessment of Naples prognostic score in predicting survival for small cell lung cancer patients treated with chemoradiotherapy.

BACKGROUNDS: The Naples prognosis score (NPS) is a novel prognostic biomarker-based immune and nutritional status and that can be used to evaluate prognosis. Our study aimed to investigate the prognostic role of NPS in SCLC patients. METHODS: Patients treated with chemoradiotherapy were retrospectively analyzed between June 2012 and August 2017. We divided patients into three groups depending on the NPS: group 0, n = 31; group 1, n = 100; and group 2, n = 48, and associations between clinical characteristics and NPS group were analyzed. The univariable and multivariable Cox analyses were used to evaluate the prognostic value of clinicopathological characteristics and laboratory indicators for overall survival (OS) and progression-free survival (PFS). RESULTS: Data from 179 patients were analyzed. Treatment modality (p < 0.001) and serum CEA (p = 0.03) were significantly different among the NPS groups. The age, sex, smoking status, KPS, Karnofsky performance score (KPS), disease extent, and number of metastatic sites were not correlated with NPS (all p > 0.05). KPS, disease extent, prophylactic cranial irradiation, treatment response and NPS Group were associated with OS. In addition, KPS, disease extent, prophylactic cranial irradiation, treatment response and NPS Group were associated with PFS. Multivariate analysis results showed that NPS was identified as an independent prognostic factor for OS (Group 1: hazard ratio [HR] = 2.704, 95% confidence interval [CI] = 1.403-5.210; p = 0.003; Group 2: HR = 5.154, 95% CI = 2.614-10.166; p < 0.001) and PFS (Group 1: HR = 2.018, 95% CI = 1.014-4.014; p = 0.045; Group 2: HR = 3.339, 95% CI = 1.650-6.756; p = 0.001). CONCLUSIONS: NPS is related to clinical outcomes in patients with SCLC.

Despite the high clinical curative effect to radiation therapy and chemotherapy in SCLC, most patients subsequently experience tumor recurrence or metastasis.Whether NPS has prognostic values in SCLC has not been investigated to date.NPS is related to clinical outcomes in patients with SCLC.NPS as an innovative scoring system, can improves prediction of survival in SCLC patients.

Deep learning methods for medical image fusion: A review.

The image fusion methods based on deep learning has become a research hotspot in the field of computer vision in recent years. This paper reviews these methods from five aspects: Firstly, the principle and advantages of image fusion methods based on deep learning are expounded; Secondly, the image fusion methods are summarized in two aspects: End-to-End and Non-End-to-End, according to the different tasks of deep learning in the feature processing stage, the non-end-to-end image fusion methods are divided into two categories: deep learning for decision mapping and deep learning for feature extraction. According to the different types of the networks, the end-to-end image fusion methods are divided into three categories: image fusion methods based on Convolutional Neural Network, Generative Adversarial Network, and Encoder-Decoder Network; Thirdly, the application of the image fusion methods based on deep learning in medical image field is summarized from two aspects: method and data set; Fourthly, evaluation metrics commonly used in the field of medical image fusion are sorted out from 14 aspects; Fifthly, the main challenges faced by the medical image fusion are discussed from two aspects: data sets and fusion methods. And the future development direction is prospected. This paper systematically summarizes the image fusion methods based on the deep learning, which has a positive guiding significance for the in-depth study of multi modal medical images.

Nested multi-scale transform fusion model: The response evaluation of chemoradiotherapy for patients with lung tumors.

BACKGROUND AND OBJECTIVE: The response evaluation of chemoradiotherapy is an important method of precision treatment for patients with malignant lung tumors. In view of the existing evaluation criteria for chemoradiotherapy, it is difficult to synthesize the geometric and shape characteristics of lung tumors. In the present, the response evaluation of chemoradiotherapy is limited. Therefore, this paper constructs a response evaluation system of chemoradiotherapy based on PET/CT images. METHODS: There are two parts in the system: a nested multi-scale fusion model and an attribute sets for the Response evaluation of chemoradiotherapy (AS-REC). In the first part, a new nested multi-scale transform method, i.e., latent low-rank representation (LATLRR) and non-subsampled contourlet transform (NSCT), is proposed. Then, the average gradient self-adaptive weighting is used for the low-frequency fusion rule, and the regional energy fusion rule is used for the high-frequency fusion rule. Further, the low-rank part fusion image is obtained by the inverse NSCT, and the fusion image is generated by adding the low-rank part fusion image and the significant part fusion image. In the second part, AS-REC is constructed to evaluate the growth direction of the tumor, the degree of tumor metabolic activity, and the tumor growth state. RESULTS: the numerical results clearly show that the performance of our proposed method outperforms in comparison with several existing methods, among them, the value of Qabf increased by up to 69%. CONCLUSIONS: Through the experiment of three reexamination patients, the effectiveness of the evaluation system of radiotherapy and chemotherapy are proved.

Phenols from Potentilla anserina L. Improve Insulin Sensitivity and Inhibit Differentiation in 3T3-L1 Adipocytes in Vitro.

Potentilla anserina L., a well-known perennial herb, is widely used in traditional Tibetan medicine and used as a delicious food in humans. The present investigation reports on the activity of P. anserina phenols (PAP) in regulating glycolipid metabolism in 3T3-L1 adipocytes. Insulin sensitivity tests showed that PAP improved insulin-stimulated glucose uptake by promoting the phosphorylation of serine/threonine kinase Akt. Moreover, an assay involving the differentiation of 3T3-L1 preadipocytes demonstrated that PAP also decreased the accumulation of lipid droplets by suppressing the expression of adipokines during the differentiation process. In addition, the underlying mechanism from the aspects of energy metabolism and oxidative stress is also discussed. The improvement in energy metabolism was supported by an increase in mitochondrial membrane potential (MMP) and intracellular ATP. Amelioration of oxidative stress was supported by decreased levels of intracellular reactive oxygen species (ROS). In summary, our findings suggest that PAP can ameliorate the disorder of glycolipid metabolism in insulin resistant 3T3-L1 adipocytes by improving energy metabolism and oxidative stress and might be an attractive candidate for the treatment of diabetes.

Teeth U-Net: A segmentation model of dental panoramic X-ray images for context semantics and contrast enhancement.

BACKGROUND AND OBJECTIVE: It is very significant in orthodontics and restorative dentistry that the teeth are segmented from dental panoramic X-ray images. Nevertheless, there are some problems in panoramic X-ray images of teeth, such as blurred interdental boundaries, low contrast between teeth and alveolar bone. METHODS: In this paper, The Teeth U-Net model is proposed in this paper to resolve these problems. This paper makes the following contributions: Firstly, a Squeeze-Excitation Module is utilized in the encoder and the decoder. And proposing a dense skip connection between encoder and decoder to reduce the semantic gap. Secondly, due to the irregular shape of the teeth and the low contrast of the dental panoramic X-ray images. A Multi-scale Aggregation attention Block (MAB) in the bottleneck layer is designed to resolve this problem, which can effectively extract teeth shape features and fuse multi-scale features adaptively. Thirdly, in order to capture dental feature information in a larger field of perception, this paper designs a Dilated Hybrid self-Attentive Block (DHAB) at the bottleneck layer. This module effectively suppresses the task-irrelevant background region information without increasing the network parameters. Finally, the effectiveness of the algorithm is validated using a clinical dental panoramic X-ray image datasets. RESULTS: The results of the three comparison experiments are shown that Accuracy, Precision, Recall, Dice, Volumetric Overlap Error and Relative Volume Difference for dental panoramic X-ray teeth segmentation are 98.53%, 95.62%, 94.51%, 94.28%, 88.92% and 95.97% by the proposed model respectively. CONCLUSION: The proposed modules complement each other in processing every detail of the dental panoramic X-ray images, which can effectively improve the efficiency of preoperative preparation and postoperative evaluation, and promote the application of dental panoramic X-ray in medical image segmentation. There are more accuracy about Teeth U-Net than others model in dental panoramic X-ray teeth segmentation. That is very important to clinical doctors to cure in orthodontics and restorative dentistry.

[Medical image instance segmentation: from candidate region to no candidate region].

In recent years, the task of object detection and segmentation in medical image is the research hotspot and difficulty in the field of image processing. Instance segmentation provides instance-level labels for different objects belonging to the same class, so it is widely used in the field of medical image processing. In this paper, medical image instance segmentation was summarized from the following aspects: First, the basic principle of instance segmentation was described, the instance segmentation models were classified into three categories, the development context of the instance segmentation algorithm was displayed in two-dimensional space, and six classic model diagrams of instance segmentation were given. Second, from the perspective of the three models of two-stage instance segmentation, single-stage instance segmentation and three-dimensional (3D) instance segmentation, we summarized the ideas of the three types of models, discussed the advantages and disadvantages, and sorted out the latest developments. Third, the application status of instance segmentation in six medical images such as colon tissue image, cervical image, bone imaging image, pathological section image of gastric cancer, computed tomography (CT) image of lung nodule and X-ray image of breast was summarized. Fourth, the main challenges in the field of medical image instance segmentation were discussed and the future development direction was prospected. In this paper, the principle, models and characteristics of instance segmentation are systematically summarized, as well as the application of instance segmentation in the field of medical image processing, which is of positive guiding significance to the study of instance segmentation.

[Exploring and analyzing the improvement mechanism of U-Net and its application in medical image segmentation].

Remarkable results have been realized by the U-Net network in the task of medical image segmentation. In recent years, many scholars have been researching the network and expanding its structure, such as improvement of encoder and decoder and improvement of skip connection. Based on the optimization of U-Net structure and its medical image segmentation techniques, this paper elucidates in the following: First, the paper elaborates on the application of U-Net in the field of medical image segmentation; Then, the paper summarizes the seven improvement mechanism of U-Net: dense connection mechanism, residual connection mechanism, multi-scale mechanism, ensemble mechanism, dilated mechanism, attention mechanism, and transformer mechanism; Finally, the paper states the ideas and methods on the U-Net structure improvement in a bid to provide a reference for later researches, which plays a significant part in advancing U-Net.

Photocatalytic Synthesis of Sulfinamides and Sulfoxides from Nitroarenes and Thiophenols.

We present an efficient and versatile visible light-driven methodology for synthesizing sulfinamides and sulfoxides using nitroarenes as the nitrogen source and thiophenols as the sulfur source. The switch-over of the two reaction pathways was achieved by changing the type of photocatalyst and the amount of thiophenol in the reaction mixture. The reaction proceeds under mild conditions with good functional group tolerance and can easily be scaled up.

Case Report: A Novel Intronic Mutation in AIFM1 Associated With Fatal Encephalomyopathy and Mitochondrial Disease in Infant.

Background: The AIFM1 gene is located on chromosome Xq26.1 and encodes a flavoprotein essential for nuclear disassembly in apoptotic cells. Mutations in this gene can cause variable clinical phenotypes, but genotype-phenotype correlations of AIFM1-related disorder have not yet been fully determined because of the clinical scarcity. Case Presentation: We describe a 4-month-old infant with mitochondrial encephalopathy, carrying a novel intronic variant in AIFM1 (NM_004208.4: c.1164 + 5G > A). TA cloning of the complementary DNA (cDNA) and Sanger sequencing revealed the simultaneous presence of an aberrant transcript with exon 11 skipping (89 bp) and a normal transcript through analysis of mRNA extracted from the patient's fibroblasts, which is consistent with direct RNA sequencing results. Conclusion: We verified the pathogenic effect of the AIFM1 c.1164 + 5G > A splicing variant, which disturbed normal mRNA splicing. Our findings expand the mutation spectrum of AIFM1 and point out the necessity of intronic sequence analysis and the importance for integrative functional studies in the interpretation of sequence variants.