Achieve fairness without demographics for dermatological disease diagnosis.

In medical image diagnosis, fairness has become increasingly crucial. Without bias mitigation, deploying unfair AI would harm the interests of the underprivileged population and potentially tear society apart. Recent research addresses prediction biases in deep learning models concerning demographic groups (e.g., gender, age, and race) by utilizing demographic (sensitive attribute) information during training. However, many sensitive attributes naturally exist in dermatological disease images. If the trained model only targets fairness for a specific attribute, it remains unfair for other attributes. Moreover, training a model that can accommodate multiple sensitive attributes is impractical due to privacy concerns. To overcome this, we propose a method enabling fair predictions for sensitive attributes during the testing phase without using such information during training. Inspired by prior work highlighting the impact of feature entanglement on fairness, we enhance the model features by capturing the features related to the sensitive and target attributes and regularizing the feature entanglement between corresponding classes. This ensures that the model can only classify based on the features related to the target attribute without relying on features associated with sensitive attributes, thereby improving fairness and accuracy. Additionally, we use disease masks from the Segment Anything Model (SAM) to enhance the quality of the learned feature. Experimental results demonstrate that the proposed method can improve fairness in classification compared to state-of-the-art methods in two dermatological disease datasets.

Effect of memory therapy on enhancing postoperative cognitive function recovery and alleviating mood disturbances in brain glioma patients.

OBJECTIVE: To assess the impact of memory therapy on enhancing recovery of postoperative cognitive function and alleviating mood disturbances in brain glioma patients. METHODS: This retrospective study included 160 brain glioma patients who met the inclusion criteria from August 2019 to July 2022. They were divided into a control group and an observation group according to according to different treatment method, with 80 cases in each group. The control group was given routine rehabilitation, while the observation group received additional memory therapy. The study compared complications between the two groups, focusing on the changes in cognitive function [using the Neurobehavioral Cognitive Status Check Scale (NCSE), Clinical Dementia Score (CDR)], mood disturbances [measured by the State Anxiety Scale (S-AI), Trait Anxiety Scale (T-AI), and Hospital Stress Scale score], health-promoting behaviors [evaluated with the Chinese Version of Health Promotion Lifestyle Scale-II (HPLP-II)], coping styles [assessed through the Medical Response Questionnaire (MCQM)], and cancer-related fatigue [using the Cancer-Related Fatigue Scale (CFS)] before and after intervention were observed. A total of 160 glioma cases were classified into either a good or poor prognosis category, based on their prognosis 12 months post-surgery. Baseline data from both groups were compared, and multivariate logistic regression was employed to analyze the factors influencing outcomes in glioma patients. RESULTS: After intervention, the observation group exhibited higher scores of NCSE, HPLP-II, and CFS, but lower scores on the CDR, S-AI, T-AI and hospital stress scale compared to the control group (all P<0.05). Additionally, within the MCQM, the observation group showed reduced avoidance and yield scores, and an increased facing score, compared to the control group (all P<0.05). No significant difference was observed between the complication rates of the control (8.75%) and observation groups (3.75%) (P>0.05). However, the incidence of adverse prognosis was significantly lower in the observation group compared to the control group (8.75% vs 22.50%) (P<0.05). There were no significant differences in age, maximum tumor diameter, preoperative Karnofsky Performance Status score, gender or lesion location between the poor prognosis group and the good prognosis group (all P>0.05). The poor prognosis group had a higher proportion of patients in clinical stages III-IV and a lower proportion receiving recall therapy compared to good prognosis group (P<0.05). Multivariate logistic regression analysis identified clinical stage (III-IV stage) [OR=3.562 (95% CI: 1.476-8.600)] as a risk factor for poor prognosis after brain glioma surgery (P<0.05), while undergoing memory therapy [ß=0.330 (95% CI: 0.99-0.842)] acted as a protective factor against poor prognosis (P<0.05). CONCLUSION: Memory therapy has been shown to promote postoperative cognitive function recovery in glioma patients, reduce anxiety and stress response, bolster coping mechanisms and health-promoting behavior, diminish cancer-related fatigue, and improve patient prognosis.

Effectiveness of exercise-based cardiac rehabilitation for patients with left ventricular assist device: A systematic review and meta-analysis.

PURPOSE: Exercise-based cardiac rehabilitation (EBCR) improves functional capacity in heart failure (HF). However, data on the effect of EBCR in patients with advanced HF and left ventricular assist devices (LVADs) are limited. This meta-analysis aimed to evaluate the impact of EBCR on the functional ability of LVAD patients by comparing the corresponding outcome indicators between the EBCR and ST groups. METHODS: PubMed, Embase, Clinical Trials, and Cochrane Library databases were searched for studies assessing and comparing the effects of EBCR and standard therapy (ST) in patients following LVAD implantation. Using pre-defined criteria, appropriate studies were identified and selected. Data from selected studies were extracted in a standardized fashion, and a meta-analysis was performed using a fixed-effects model. The protocol was registered on INPLASY (202340073). RESULTS: In total, 12 trials involving 477 patients were identified. The mean age of the participants was 52.9 years, and 78.6% were male. The initiation of EBCR varied from LVAD implantation during the index hospitalization to 11 months post-LVAD implantation. The median rehabilitation period ranged from 2 weeks to 18 months. EBCR was associated with improved peak oxygen uptake (VO2) in all trials. Quantitative analysis was performed in six randomized studies involving 214 patients (EBCR: n = 130, ST: n = 84). EBCR was associated with a significantly high peak VO2 (weighted mean difference [WMD] = 1.64 mL/kg/min; 95% confidence interval [CI], 0.20-3.08; p = .03). Similarly, 6-min walk distance (6MWD) showed significantly greater improvement in the EBCR group than in the ST group (WMD = 34.54 m; 95% CI, 12.47-56.42; p = .002) in 266 patients (EBCR, n = 140; ST, n = 126). Heterogeneity was low among the included trials. None of the included studies reported serious adverse events related to EBCR, indicating the safety of EBCR after LVAD implantation. CONCLUSION: This study demonstrated that EBCR following LVAD implantation is associated with greater improvement in functional capacity compared with ST as reflected by the improved peak VO2 and 6MWD values. Considering the small number of patients in this analysis, further research on the clinical impact of EBCR in LVAD patients is warranted.

One-Step Construction of Closed Pores Enabling High Plateau Capacity Hard Carbon Anodes for Sodium-Ion Batteries: Closed-Pore Formation and Energy Storage Mechanisms.

Closed pores play a crucial role in improving the low-voltage (<0.1 V) plateau capacity of hard carbon anodes for sodium-ion batteries (SIBs). However, the lack of simple and effective closed-pore construction strategies, as well as the unclear closed-pore formation mechanism, has severely hindered the development of high plateau capacity hard carbon anodes. Herein, we present an effective closed-pore construction strategy by one-step pyrolysis of zinc gluconate (ZG) and elucidate the corresponding mechanism of closed-pore formation. The closed-pore formation mechanism during the pyrolysis of ZG mainly involves (i) the precipitation of ZnO nanoparticles and the ZnO etching on carbon under 1100 °C to generate open pores of 0.45-4 nm and (ii) the development of graphitic domains and the shrinkage of the partial open pores at 1100-1500 °C to convert the open pores to closed pores. Benefiting from the considerable closed-pore content and suitable microstructure, the optimized hard carbon achieves an ultrahigh reversible specific capacity of 481.5 mA h g-1 and an extraordinary plateau capacity of 389 mA h g-1 for use as the anode of SIBs. Additionally, some in situ and ex situ characterizations demonstrate that the high-voltage slope capacity and the low-voltage plateau capacity stem from the adsorption of Na+ at the defect sites and Na-cluster formation in closed pores, respectively.

The mediating effect of internet addiction and the moderating effect of physical activity on the relationship between alexithymia and depression.

There is a certain relationship between alexithymia and depression, but further investigation is needed to explore their underlying mechanisms. The aims of this study was to explore the mediating role of internet addiction between alexithymia and depression and the moderating role of physical activity. A total of 594 valid responses were included in the analysis, with a mean age of 18.72 years (SD = 1.09). The sample comprised 250 males (42.09%) and 344 females (57.91%). These responses were utilized for descriptive analysis, correlation analysis, regression analysis, and the development of mediation and moderation models. Alexithymia showed positive correlations with depression and internet addiction, and physical activity was negatively correlated with internet addiction and depression. Internet addiction partially mediated the relationship between alexithymia and depression, while physical activity weakened the association between internet addiction and depression, acting as a moderator. Our findings suggest that excessive Internet engagement may mediate the relationship between alexithymia and depression as an emotional regulatory coping strategy, and that physical activity attenuates the predictive effect of Internet addiction on depression.

Exosomal miR-1470 is a diagnostic biomarker and promotes cell proliferation and metastasis in colorectal cancer.

BACKGROUND: In recent years,the lack of specific markers for the diagnosis of colorectal cancer has led to an upward trend in both morbidity and mortality from this condition. There is an urgent need to identify molecular biomarkers that contribute to early cancer detection. This study aimed to identify specific exosomal microRNAs that hold potential as diagnostic biomarkers for CRC. METHODS: We screened for differentially expressed miRNAs using the CRC exosome dataset GSE39833. To validate the results in the public database, we collected serum from 168 CRC patients and 168 healthy volunteers. The expression levels of exosomal miR-1470 in healthy volunteers and CRC patients were analyzed using qRT-PCR. To evaluate the diagnostic potential of the selected miR-1470 in distinguishing CRC patients from healthy controls, we analyzed its receiver operating characteristic curve. To explore the biological functions of miR-1470 in CRC cell lines, we detected the miR-1470's ability to regulate the growth and metastasis of CRC cells by CCK8, transwell and other assays after transfection of miR-1470 in SW480, HCT-116 cells. RESULTS: Exosomal miR-1470 exhibited significant up-regulation in CRC patients compared to healthy volunteers. The ROC curve analysis revealed an area under the curve (AUC) of 0.74 (95% confidence interval: 0.6876-0.7920) for exosomal miR-1470, indicating its potential as a diagnostic biomarker. Furthermore, the expression level of miR-1470 in CRC patients showed correlations with age, metastasis, and HDL content. We overexpressed miR-1470 in CRC cell lines. CCK8 proliferation assay showed that miR-1470 promoted the proliferation ability of SW480 and HCT-116 cells. Transwell assay showed that miR-1470 promoted the migration and invasion ability of SW480 and HCT-116 cells. CONCLUSION: This suggested that non-invasive diagnosis of CRC is possible by detecting the level of miR-1470 in exosomes, which has important implications for early detection and treatment of this disease.

Challenges of mesenchymal stem cells in the clinical treatment of COVID-19.

The 2019 coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has brought an enormous public health burden to the global society. The duration of the epidemic, the number of infected people, and the widespread of the epidemic are extremely rare in modern society. In the initial stage of infection, people generally show fever, cough, and dyspnea, which can lead to pneumonia, acute respiratory syndrome, kidney failure, and even death in severe cases. The strong infectivity and pathogenicity of SARS-CoV-2 make it more urgent to find an effective treatment. Mesenchymal stem cells (MSCs) are a kind of pluripotent stem cells with the potential for self-renewal and multi-directional differentiation. They are widely used in clinical experiments because of their low immunogenicity and immunomodulatory function. Mesenchymal stem cell-derived exosomes (MSC-Exo) can play a physiological role similar to that of stem cells. Since the COVID-19 pandemic, a series of clinical trials based on MSC therapy have been carried out. The results show that MSCs are safe and can significantly improve patients' respiratory function and prognosis of COVID-19. Here, the effects of MSCs and MSC-Exo in the treatment of COVID-19 are reviewed, and the clinical challenges that may be faced in the future are clarified.

TEP SNORD12B, SNORA63, and SNORD14E as novel biomarkers for hepatitis B virus-related hepatocellular carcinoma (HBV-related HCC).

PURPOSE: The alterations of RNA profile in tumor-educated platelets (TEPs) have been described as a novel biosource for cancer diagnostics. This study aimed to explore the potential snoRNAs in TEP as biomarkers for diagnostics of hepatitis B virus-related hepatocellular carcinoma (HBV-related HCC). METHODS: Platelets were isolated using low-speed centrifugation and subjected to a quantitative polymerase chain reaction (qPCR) for snoRNAs detection. RESULTS: Down-regulated SNORD12B and SNORD14E as well as up-regulated SNORA63 were identified in TEP from HBV-related HCC, which could act as diagnostic biomarkers for HBV-related HCC as well as the early disease. Besides, TEP SNORD12B, SNORD14E, and SNORA63 facilitate the diagnostic performance of AFP and achieve favorable diagnostics efficiency for HBV-related HCC when combined with platelet parameters. CONCLUSIONS: Aberrant expression of SNORD12B, SNORA63, and SNORD14E in TEPs could serve as the novel and non-invasive biomarkers for HBV-related HCC diagnosis.

Fluorine-tailed glass fibers for adsorption of volatile perfluorinated compounds via F-F interaction.

Perfluorinated compounds (PFCs) and their short-chain derivatives are contaminants found globally. Adsorption research on volatile perfluorinated compounds (VPFCs), which are the main PFCs substances that undergo transfer and migration, is particularly important. In this study, new fluorine-containing tail materials (FCTMs) were prepared by combining fluorine-containing tail organic compounds with modified glass fibers. The adsorption effects of these FCTMs were generally stronger than that of pure activated glass fibers without fluorine- tailed, with an adsorption efficiency of up to 86% based on F-F interactions. The results showed that the FCTMs had improved desorption efficiency and reusability, and higher adsorption efficiency compared with that of polyurethane foam. FTGF was applied to the active sampler, and the indoor adsorption of perfluorovaleric acid was up to 2.45 ng/m3. The adsorption kinetics and isotherm simulation results showed that the adsorption process of typical perfluorinated compounds conformed to the second-order kinetics and Langmuir model. Furthermore, Nuclear Magnetic Resonance (NMR) results showed that the chemical shift in the fluorine spectrum was significantly changed by F-F interactions. This research provides basic theoretical data for the study of VPFCs, especially short-chain VPFCs, facilitating improved scientific support for the gas phase analysis of VPFCs in the environment.

Characterizing the Survival-Associated Interactions Between Tumor-Infiltrating Lymphocytes and Tumors From Pathological Images and Multi-Omics Data.

The tumor-infiltrating lymphocytes (TILs) and its correlation with tumors have shown significant values in the development of cancers. Many observations indicated that the combination of the whole-slide pathological images (WSIs) and genomic data can better characterize the immunological mechanisms of TILs. However, the existing image-genomic studies evaluated the TILs by the combination of pathological image and single-type of omics data (e.g., mRNA), which is difficulty in assessing the underlying molecular processes of TILs holistically. Additionally, it is still very challenging to characterize the intersections between TILs and tumor regions in WSIs and the high dimensional genomic data also brings difficulty for the integrative analysis with WSIs. Based on the above considerations, we proposed an end-to-end deep learning framework i.e., IMO-TILs that can integrate pathological image with multi-omics data (i.e., mRNA and miRNA) to analyze TILs and explore the survival-associated interactions between TILs and tumors. Specifically, we firstly apply the graph attention network to describe the spatial interactions between TILs and tumor regions in WSIs. As to genomic data, the Concrete AutoEncoder (i.e., CAE) is adopted to select survival-associated Eigengenes from the high-dimensional multi-omics data. Finally, the deep generalized canonical correlation analysis (DGCCA) accompanied with the attention layer is implemented to fuse the image and multi-omics data for prognosis prediction of human cancers. The experimental results on three cancer cohorts derived from the Cancer Genome Atlas (TCGA) indicated that our method can both achieve higher prognosis results and identify consistent imaging and multi-omics bio-markers correlated strongly with the prognosis of human cancers.

Neutrophil extracellular traps in acute coronary syndrome.

Acute coronary syndrome (ACS) is a group of clinical syndromes caused by acute myocardial ischemia, which can cause heart failure, arrhythmia and even sudden death. It is the major cause of disability and death worldwide. Neutrophil extracellular traps (NETs) are reticular structures released by neutrophils activation and have various biological functions. NETs are closely related to the occurrence and development of ACS and also the subsequent damage after myocardial infarction. The mechanisms are complex and interdependent on various pathways, which require further exploration. This article reviewed the role and mechanism of NETs in ACS, thereby providing a valuable reference for the diagnosis and clinical treatment of ACS.

Recent Progress in Modifications, Properties, and Practical Applications of Glass Fiber.

As a new member of the silica-derivative family, modified glass fiber (MGF) has attracted extensive attention because of its excellent properties and potential applications. Surface modification of glass fiber (GF) greatly changes its performance, resulting in a series of changes to its surface structure, wettability, electrical properties, mechanical properties, and stability. This article summarizes the latest research progress in MGF, including the different modification methods, the various properties, and their advanced applications in different fields. Finally, the challenges and possible solutions were provided for future investigations of MGF.

Transcriptomic Analysis Reveals Panicle Heterosis in an Elite Hybrid Rice ZZY10 and Its Parental Lines.

Heterosis is the phenomenon in which some hybrid traits are superior to those of their parents. Most studies have analyzed the heterosis of agronomic traits of crops; however, heterosis of the panicles can improve yield and is important for crop breeding. Therefore, a systematic study of panicle heterosis is needed, especially during the reproductive stage. RNA sequencing (RNA Seq) and transcriptome analysis are suitable for further study of heterosis. Using the Illumina Nova Seq platform, the transcriptome of ZhongZheYou 10 (ZZY10), an elite rice hybrid, the maintainer line ZhongZhe B (ZZB), and the restorer line Z7-10 were analyzed at the heading date in Hangzhou, 2022. 581 million high-quality short reads were obtained by sequencing and were aligned against the Nipponbare reference genome. A total of 9000 differential expression genes were found between the hybrids and their parents (DGHP). Of the DGHP, 60.71% were up-regulated and 39.29% were down-regulated in the hybrid. Comparative transcriptome analysis revealed that 5235 and 3765 DGHP were between ZZY10 and ZhongZhe B and between ZZY10 and Z7-10, respectively. This result is consistent with the transcriptome profile of ZZY10 and was similar to Z7-10. The expression patterns of DGHP mainly exhibited over-dominance, under-dominance, and additivity. Among the DGHP-involved GO terms, pathways such as photosynthesis, DNA integration, cell wall modification, thylakoid, and photosystem were significant. 21 DGHP, which were involved in photosynthesis, and 17 random DGHP were selected for qRT-PCR validation. The up-regulated PsbQ and down-regulated subunits of PSI and PSII and photosynthetic electron transport in the photosynthesis pathway were observed in our study. Extensive transcriptome data were obtained by RNA-Seq, providing a comprehensive overview of panicle transcriptomes at the heading stage in a heterotic hybrid.

Prediction of the Nitrogen Content of Rice Leaf Using Multi-Spectral Images Based on Hybrid Radial Basis Function Neural Network and Partial Least-Squares Regression.

This paper's novel focus is predicting the leaf nitrogen content of rice during growing and maturing. A multispectral image processing-based prediction model of the Radial Basis Function Neural Network (RBFNN) model was proposed. Moreover, this paper depicted three primary points as the following: First, collect images of rice leaves (RL) from a controlled condition experimental laboratory and new shoot leaves in different stages in the visible light spectrum, and apply digital image processing technology to extract the color characteristics of RL and the morphological characteristics of the new shoot leaves. Secondly, the RBFNN model, the General Regression Model (GRL), and the General Regression Method (GRM) model were constructed based on the extracted image feature parameters and the nitrogen content of rice leaves. Third, the RBFNN is optimized by and Partial Least-Squares Regression (RBFNN-PLSR) model. Finally, the validation results show that the nitrogen content prediction models at growing and mature stages that the mean absolute error (MAE), the Mean Absolute Percentage Error (MAPE), and the Root Mean Square Error (RMSE) of the RFBNN model during the rice-growing stage and the mature stage are 0.6418 (%), 0.5399 (%), 0.0652 (%), and 0.3540 (%), 0.1566 (%), 0.0214 (%) respectively, the predicted value of the model fits well with the actual value. Finally, the model may be used to give the best foundation for achieving exact fertilization control by continuously monitoring the nitrogen nutrition status of rice. In addition, at the growing stage, the RBFNN model shows better results compared to both GRL and GRM, in which MAE is reduced by 0.2233% and 0.2785%, respectively.

Plasma exosomal tRNA-derived fragments as diagnostic biomarkers in non-small cell lung cancer.

Background: tRNA derived small RNAs (tRFs) have recently received extensive attention; however, the effects of tRFs in exosome as biomarkers has been less studied. The objective of this study was to validate novel diagnostic exosomal tRFs with sensitivity and specificity for non-small cell lung cancer (NSCLC). Methods: Exosomes extracted from plasma of NSCLC patients and healthy individuals were identified by transmission electron microscopy (TEM), qNano and western blots. The differentially expressed tRFs were screened by high-throughput sequencing in plasma exosomes of NSCLC patients and healthy individuals, and further verified by Quantitative Real-Time PCR (qRT-PCR). To assess the diagnostic efficacy of exosomal tRFs for NSCLC, receiver operating characteristic (ROC) curves were used next. Results: The expression levels of exosomal tRF-Leu-TAA-005, tRF-Asn-GTT-010, tRF-Ala-AGC-036, tRF-Lys-CTT-049, and tRF-Trp-CCA-057 were significantly decreased in NSCLC patients and early-stage NSCLC patients compared to healthy individuals. Notably, the exepression of tRF-Leu-TAA-005, tRF-Asn-GTT-010, tRF-Ala-AGC-036, tRF-Lys-CTT-049, and tRF-Trp-CCA-057 in the exosomes were higher than the exosome depleted supernatant (EDS). Conclusions: Our results showed that the levels of exosomal tRF-Leu-TAA-005, tRF-Asn-GTT-010, tRF-Ala-AGC-036, tRF-Lys-CTT-049, and tRF-Trp-CCA-057 were significantly downregulated in NSCLC patients. This suggests that these five exosomal tRFs may be promising diagnostic biomarkers for NSCLC.

Intestinal Flora Affect Alzheimer's Disease by Regulating Endogenous Hormones.

Alzheimer's disease (AD) is a central nervous system disease that can lead to cognitive impairment and progressive memory loss. An increasing number of studies have shown that intestinal flora play a crucial role in regulating the brain-gut axis. Short-chain fatty acids are metabolites of intestinal flora that regulate hormone synthesis and play an essential role in microbial-intestinal-brain communication. An imbalance of intestinal flora can promote microglia to secrete proinflammatory factors, cause nerve inflammation, and then affect cognitive and learning ability. However, the mechanism is not clear. From this, we infer that endogenous hormones may be the medium for intestinal flora to affect the process of AD. This review of the relationships among AD, endogenous hormones, and intestinal flora expounds on the critical role of various hormones in the brain-gut axis. It discusses intervention measures aimed at intestinal flora to prevent or delay AD occurrence. Finally, the potential development prospects of fecal microbiota transplantation in treating AD are put forward, which provide potential ideas for future AD research.

[Protective effects of Polygonatum odoratum polysaccharides on alcohol-induced injury of HepG2 cells and its mechanisms].

Objective: To investigate the protective effects of Polygonatum odoratum polysaccharides (POP) on alcohol-induced injury of HepG2 cells and its potential molecular mechanisms. Methods: After screening the appropriate concentration of alcohol-treated HepG2 cells and the intervention concentration of POP by MTT method, HepG2 cells were divided into three groups according to different intervention concentrations (200 µg/L, 400 µg/L and 600 µg/L) of POP, and the blank group without POP. After pretreated for 1 h, HepG2 cells were treated with 4% alcohol for 24 h. The activities of intracellular alanine aminotransferase (ALT) and aspartate aminotransferase (AST) were measured, and the levels of intracellular reactive oxygen species (ROS), malondialdehyde (MDA), glutathione (GSH), interleukin-1ß (IL-1ß) and tumor necrosis factor α (TNF- α) were measured. The protein expressions of Kelch-like epichlorohydrin-associated protein-1 (Keap1), phosphorylated nuclear factor E2-related factor 2 (p-Nrf2), phosphoamide adenine dinucleotide quinone oxidoreductase -1 (NQO1), B lymphocyte tumor-2 (Bcl-2), Bcl-2-associated X protein (Bax) and caspase 3 were detected. Results: Compared with the HepG2 cells treated with 4% alcohol, POP at the various concentrations could effectively down-regulate the activities of ALT and AST in HepG2 cells induced by alcohol (P＜0.05). The levels of IL-1ß and TNF-α in the 200 µg/L POP treated group were decreased significantly (P＜0.05), while the level of GSH was increased significantly (P＜0.01). The levels of ROS, MDA, IL-1ß and TNF-α in the 400 µg/L and 600 µg/L POP treated groups were decreased significantly (P＜0.05 or P＜0.01), while the GSH level was increased significantly (P＜0.01). POP effectively up-regulated the expressions of p-Nrf2 and NQO1 protein in HepG2 cells induced by alcohol, and also down-regulated the Bax/Bcl-2 index (P＜0.05), and inhibited the protein expressions of Keap1 and cleaved-caspase-3 (P＜0.05). Conclusion: POP can improve alcohol-induced oxidative stress injury in HepG2 cells by regulating the Nrf2/Keap1 pathway, thereby reducing the inflammatory index and apoptosis level of HepG2 cells. Among them, 400 µg/L and 600 µg/L POP have better intervention effects.

Case Report and Review of Literature: Camrelizumab Combined with Fuzuloparib and Apatinib for Platinum-Resistant Recurrent Ovarian Cancer.

Background: The mortality rate of ovarian cancer (OC) ranks first among female genital tract malignant tumors, which seriously threatens women's life and health. Because of its insidious onset and poor prognosis, it has become a thorny problem in the clinic, especially for patients with platinum-resistant recurrent ovarian cancer (PROC). In recent years, the medical treatment of OC has made gratifying results, bringing hope to the patients. Case Description: A 54-year-old OC patient who has failed previous neoadjuvant chemotherapy, cytoreductive surgery, and postoperative chemotherapy was diagnosed with PROC. Then she received combination treatment of fuzuloparib (100mg PO BID), apatinib (250mg PO QD), and camrelizumab (200mg IV Q3W) for every 3-week cycle in a Phase II study for PROC patients. In the phase II study, her condition stabilized, responded well to treatment with a sharp decrease by 91.14% of target lesions and disappearances of non-target lesions, and continued to receive regular treatment with progression-free survival exceeding 15 months and no serious adverse events. Conclusion: The present case proves PROC patients might have a sustained response to triplet combination with camrelizumab, combined with fuzuloparib and apatinib.

Distributed contrastive learning for medical image segmentation.

Supervised deep learning needs a large amount of labeled data to achieve high performance. However, in medical imaging analysis, each site may only have a limited amount of data and labels, which makes learning ineffective. Federated learning (FL) can learn a shared model from decentralized data. But traditional FL requires fully-labeled data for training, which is very expensive to obtain. Self-supervised contrastive learning (CL) can learn from unlabeled data for pre-training, followed by fine-tuning with limited annotations. However, when adopting CL in FL, the limited data diversity on each site makes federated contrastive learning (FCL) ineffective. In this work, we propose two federated self-supervised learning frameworks for volumetric medical image segmentation with limited annotations. The first one features high accuracy and fits high-performance servers with high-speed connections. The second one features lower communication costs, suitable for mobile devices. In the first framework, features are exchanged during FCL to provide diverse contrastive data to each site for effective local CL while keeping raw data private. Global structural matching aligns local and remote features for a unified feature space among different sites. In the second framework, to reduce the communication cost for feature exchanging, we propose an optimized method FCLOpt that does not rely on negative samples. To reduce the communications of model download, we propose the predictive target network update (PTNU) that predicts the parameters of the target network. Based on PTNU, we propose the distance prediction (DP) to remove most of the uploads of the target network. Experiments on a cardiac MRI dataset show the proposed two frameworks substantially improve the segmentation and generalization performance compared with state-of-the-art techniques.

Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications.

With the remarkable success of digital histopathology, we have witnessed a rapid expansion of the use of computational methods for the analysis of digital pathology and biopsy image patches. However, the unprecedented scale and heterogeneous patterns of histopathological images have presented critical computational bottlenecks requiring new computational histopathology tools. Recently, deep learning technology has been extremely successful in the field of computer vision, which has also boosted considerable interest in digital pathology applications. Deep learning and its extensions have opened several avenues to tackle many challenging histopathological image analysis problems including color normalization, image segmentation, and the diagnosis/prognosis of human cancers. In this paper, we provide a comprehensive up-to-date review of the deep learning methods for digital H&E-stained pathology image analysis. Specifically, we first describe recent literature that uses deep learning for color normalization, which is one essential research direction for H&E-stained histopathological image analysis. Followed by the discussion of color normalization, we review applications of the deep learning method for various H&E-stained image analysis tasks such as nuclei and tissue segmentation. We also summarize several key clinical studies that use deep learning for the diagnosis and prognosis of human cancers from H&E-stained histopathological images. Finally, online resources and open research problems on pathological image analysis are also provided in this review for the convenience of researchers who are interested in this exciting field.