Endothelial dysfunction in vascular complications of diabetes: a comprehensive review of mechanisms and implications.

Diabetic vascular complications are prevalent and severe among diabetic patients, profoundly affecting both their quality of life and long-term prospects. These complications can be classified into macrovascular and microvascular complications. Under the impact of risk factors such as elevated blood glucose, blood pressure, and cholesterol lipids, the vascular endothelium undergoes endothelial dysfunction, characterized by increased inflammation and oxidative stress, decreased NO biosynthesis, endothelial-mesenchymal transition, senescence, and even cell death. These processes will ultimately lead to macrovascular and microvascular diseases, with macrovascular diseases mainly characterized by atherosclerosis (AS) and microvascular diseases mainly characterized by thickening of the basement membrane. It further indicates a primary contributor to the elevated morbidity and mortality observed in individuals with diabetes. In this review, we will delve into the intricate mechanisms that drive endothelial dysfunction during diabetes progression and its associated vascular complications. Furthermore, we will outline various pharmacotherapies targeting diabetic endothelial dysfunction in the hope of accelerating effective therapeutic drug discovery for early control of diabetes and its vascular complications.

Hot-Pressing Enhances Mechanical Strength of PEO Solid Polymer Electrolyte for All-Solid-State Sodium Metal Batteries.

Poly(ethylene oxide) (PEO)-based solid polymer electrolytes (SPEs) are widely utilized in all-solid-state sodium metal batteries (ASSSMBs) due to their excellent flexibility and safety. However, poor ionic conductivity and mechanical strength limit its development. In this work, an emerging solvent-free hot-pressing method is used to prepare mechanically robust PEO-based SPE, while sodium superionic conductors Na3 Zr2 Si2 PO12 (NZSP) and NaClO4 are introduced to improve ionic conductivity. The as-prepared electrolyte exhibits a high ionic conductivity of 4.42 × 10-4 S cm-1 and a suitable electrochemical stability window (4.5 V vs Na/Na+ ). Furthermore, the SPE enables intimate contact with the electrode. The Na||Na3 V2 (PO4 )3 @C ASSSMB delivers a high-capacity retention of 97.1% after 100 cycles at 0.5 C and 60 °C, and exhibits excellent Coulombic efficiency (CE) (close to 100%). The ASSSMB with the 20 µm thick electrolyte also demonstrates excellent cyclic stability. This study provides a promising strategy for designing stable polymer-ceramic composite electrolyte membranes through hot-pressing to realize high-energy-density sodium metal batteries.

Potassium sodium hydrogen citrate intervention on gut microbiota and clinical features in uric acid stone patients.

The high recurrence rate of renal uric acid stone (UAS) poses a significant challenge for urologists, and potassium sodium hydrogen citrate (PSHC) has been proven to be an effective oral dissolution drug. However, no studies have investigated the impact of PSHC on gut microbiota and its metabolites during stone dissolution therapy. We prospectively recruited 37 UAS patients and 40 healthy subjects, of which 12 patients completed a 3-month pharmacological intervention. Fasting vein blood was extracted and mid-stream urine was retained for biochemical testing. Fecal samples were collected for 16S ribosomal RNA (rRNA) gene sequencing and short chain fatty acids (SCFAs) content determination. UAS patients exhibited comorbidities such as obesity, hypertension, gout, and dyslipidemia. The richness and diversity of the gut microbiota were significantly decreased in UAS patients, Bacteroides and Fusobacterium were dominant genera while Subdoligranulum and Bifidobacterium were poorly enriched. After PSHC intervention, there was a significant reduction in stone size accompanied by decreased serum uric acid and increased urinary pH levels. The abundance of pathogenic bacterium Fusobacterium was significantly downregulated following the intervention, whereas there was an upregulation observed in SCFA-producing bacteria Lachnoclostridium and Parasutterella, leading to a significant elevation in butyric acid content. Functions related to fatty acid synthesis and amino acid metabolism within the microbiota showed upregulation following PSHC intervention. The correlation analysis revealed a positive association between stone pathogenic bacteria abundance and clinical factors for stone formation, while a negative correlation with SCFAs contents. Our preliminary study revealed that alterations in gut microbiota and metabolites were the crucial physiological adaptation to PSHC intervention. Targeted regulation of microbiota and SCFA holds promise for enhancing drug therapy efficacy and preventing stone recurrence. KEY POINTS: • Bacteroides and Fusobacterium were identified as dominant genera for UAS patients • After PSHC intervention, Fusobacterium decreased and butyric acid content increased • The microbiota increased capacity for fatty acid synthesis after PSHC intervention.

Achieving Dendrite-Free Solid-State Lithium-Metal Batteries via In Situ Construction of Li3P/LiCl Interfacial Layers.

Hybrid solid electrolyte (HSE) exhibits potential as a solid electrolyte due to its satisfactory Li+ conductivity, superior flexibility, and optimal interface compatibility. However, the inadequate wettability of the Li/HSE interface leads to significant contact impedance, thus fostering the formation of Li dendrites and limiting their practical applicability. Here, a straightforward strategy to enhance the interfacial wettability between Li and HSE and promote the uniform migration of Li+ by in situ construction of a multifunctional interface consisting of Li3P/LiCl (PCl@Li) was created. The Li3P component acts as a Li+ channel, banishing Li+ diffusion obstacles within the interface layer, while the electronically insulating LiCl component acts as an electron-blocking shield at the Li/HSE interface, promoting uniform Li+ deposition and preventing the formation of Li dendrites. The interface impedance of the symmetric PCl@Li|HSE|PCl@Li battery decreases markedly from 230.2 to 47.4 Ω cm-2. Additionally, the battery demonstrates superb cycling stability for over 1300 h at 0.1 mA cm-2 and maintains a minimal overpotential of 32 mV at 30 °C. The PCl@Li|HSE|LiFePO4 battery shows an initial discharge-specific capacity of 135.6 mA h g-1 at 1 C, with a notable capacity retention of 87.0% (118.0 mA h g-1) after 500 cycles. This work provides a new facile strategy for all-solid-state batteries to address interface issues between Li electrodes and HSE.

Dynamic Chest Radiograph Simulation Technique with Deep Convolutional Neural Networks: A Proof-of-Concept Study.

In this study, we present an innovative approach that harnesses deep neural networks to simulate respiratory lung motion and extract local functional information from single-phase chest X-rays, thus providing valuable auxiliary data for early diagnosis of lung cancer. A novel radiograph motion simulation (RMS) network was developed by combining a U-Net and a long short-term memory (LSTM) network for image generation and sequential prediction. By utilizing a spatial transformer network to deform input images, our proposed network ensures accurate image generation. We conducted both qualitative and quantitative assessments to evaluate the effectiveness and accuracy of our proposed network. The simulated respiratory motion closely aligns with pulmonary biomechanics and reveals enhanced details of pulmonary diseases. The proposed network demonstrates precise prediction of respiratory motion in the test cases, achieving remarkable average Dice scores exceeding 0.96 across all phases. The maximum variation in lung length prediction was observed during the end-exhale phase, with average deviation of 4.76 mm (±6.64) for the left lung and 4.77 mm (±7.00) for the right lung. This research validates the feasibility of generating patient-specific respiratory motion profiles from single-phase chest radiographs.

Identification and validation of prognostic and tumor microenvironment characteristics of necroptosis index and BIRC3 in clear cell renal cell carcinoma.

Background: Necroptosis is a form of programmed cell death; it has an important role in tumorigenesis and metastasis. However, details of the regulation and function of necroptosis in clear cell renal cell carcinoma (ccRCC) remain unclear. It is necessary to explore the significance of necroptosis in ccRCC. Methods: Necroptosis-related clusters were discerned through the application of Consensus Clustering. Based on the TCGA and GEO databases, we identified prognostic necroptosis-related genes (NRGs) with univariate COX regression analysis. The necroptosis-related model was constructed through the utilization of LASSO regression analysis, and the immune properties, tumor mutation burden, and immunotherapy characteristics of the model were assessed using multiple algorithms and datasets. Furthermore, we conducted comprehensive GO, KEGG, and GSVA analyses to probe into the functional aspects of biological pathways. To explore the expression and of hub gene (BIRC3) in different ccRCC cell types and cell lines, single-cell sequencing data was analysed and we performed Quantitative Real-time PCR to detect the expression of BIRC3 in ccRCC cell lines. Function of BIRC3 in ccRCC was assessed through Cell Counting Kit-8 (CCK8) assay (for proliferation), transwell and wound healing assays (for migration and invasion). Results: Distinct necroptosis-related clusters exhibiting varying prognostic implications, and enrichment pathways were identified in ccRCC. A robust necroptosis-related model formulated based on the expression of six prognostic NRGs, presented substantial predictive capabilities of overall survival and was shown to be related with patients' immune profiles, tumor mutation burden, and response to immunotherapy. Notably, the hub gene BIRC3 was markedly upregulated in both ccRCC tissues and cell lines, and showed significant correlations with immunosuppressive cells, immune checkpoints, and oncogenic pathways. Downregulation of BIRC3 demonstrated a negative regulatory effect on ccRCC cell proliferation migration and invasion. Conclusion: The necroptosis-related model assumed a pivotal role in determining the prognosis, tumor mutation burden, immunotherapy response, and immune cell infiltration characteristics among ccRCC patients. BIRC3 exhibited significant correlations with the immunosuppressive microenvironment, which highlighted its potential for informing the design of innovative immunotherapies for ccRCC patients.

A Crosstalk Between Castration-Resistant Prostate Cancer Cells, M2 Macrophages, and NK Cells: Role of the ATM-PI3K/AKT-PD-L1 Pathway.

Castration-resistant prostate cancer (CRPC) in males is associated with a poor prognosis and a higher risk of treatment-related adverse effects, with high mortality among cancers globally. It is thus imperative to explore novel potential molecules with dual therapeutic and biomarker functions. Based on the recent research findings, the expression levels of ataxia telangiectasia mutant kinase (ATM) in prostate cancer (PC) tissues collected from CRPC patients were higher than hormone-dependent PC patients. Using CRPC cell lines (C4-2 and CWR22Rv1), the transwell chamber experiments revealed ATM promoted macrophage recruitment in CRPC cells in vitro via C-X-C motif chemokine ligand 12 (CXCL12). Further in vitro investigations demonstrated that polarized macrophages prevented NK cell recruitment and reduced the immunocidal activity of NK cells against CRPC cell lines. Moreover, ATM boosted programmed death receptor ligand 1 (PD-L1) expression while inhibiting NK group 2D (NKG2D) ligand expression in selected cell lines via PI3K/AKT signaling pathway. The in vivo investigations revealed ATM induced proliferation of CRPC and macrophage recruitment, while the NK cell recruitment was found to suppress ATM expression and CRPC proliferation. In conclusion, it could be demonstrated that inhibiting ATM increased the susceptibility of CRPC to NK cell inhibitors by dampening the CXCL12 and PI3K/AKT-PD-L1 pathways, thereby offering a novel and individualized treatment protocol for treating CRPC.

The altered composition of gut microbiota and biochemical features as well as dietary patterns in a southern Chinese population with recurrent renal calcium oxalate stones.

The correlation among gut microbiota, biochemical features, and dietary patterns in recurrent stone formers has been inadequately investigated in the Chinese population. Forty-two patients with calcium oxalate stones (CaOxS group), including 34 recurrent stone formers (RS group), and 40 nonstone healthy subjects (NS group) from Changshu Hospital Affiliated with Soochow University, were prospectively recruited. Food frequency questionnaires were completed by participants, fasting vein blood was extracted, 24-h urine was collected for biochemical detection, and fecal samples were gathered for 16S ribosomal RNA (rRNA) gene sequencing. BMI; serum levels of triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), magnesium, and creatinine; and urine levels of magnesium in stone formers were significantly different from those of controls, and RS patients showed significantly low serum phosphate and high urine phosphate levels. Celery, bamboo shoots, and pickled food were the favored foods of local stone formers. Patients with recurrent stones had altered microbiota composition, with Escherichia, Fusobacterium, and Epulopiscium being the predominant pathogenic genera. The gut microbiota in RS patients had stronger functions in fatty acid and amino acid degradation but weaker functions in their biosynthesis. The pathogenic genera were positively correlated with BMI; serum levels of TGs and creatinine; urine levels of calcium, phosphate, and uric acid (UA); and celery, bamboo shoots, and pickled food intake. The abundance of Escherichia and Fusobacterium and the levels of serum magnesium and creatinine were the most relevant factors associated with stone recurrence and could be validated as biomarkers of recurrence. Our research provides a novel prevention strategy for the recurrence of renal calcium oxalate stones in the Han Chinese population of southern China.

Identification of mitophagy-related biomarkers and immune infiltration in major depressive disorder.

BACKGROUND: Major depressive disorder (MDD) is a life-threatening and debilitating mental health condition. Mitophagy, a form of selective autophagy that eliminates dysfunctional mitochondria, is associated with depression. However, studies on the relationship between mitophagy-related genes (MRGs) and MDD are scarce. This study aimed to identify potential mitophagy-related biomarkers for MDD and characterize the underlying molecular mechanisms. METHODS: The gene expression profiles of 144 MDD samples and 72 normal controls were retrieved from the Gene Expression Omnibus database, and the MRGs were extracted from the GeneCards database. Consensus clustering was used to determine MDD clusters. Immune cell infiltration was evaluated using CIBERSORT. Functional enrichment analyses were performed to determine the biological significance of mitophagy-related differentially expressed genes (MR-DEGs). Weighted gene co-expression network analysis, along with a network of protein-protein interactions (PPI), was used to identify key modules and hub genes. Based on the least absolute shrinkage and selection operator analysis and univariate Cox regression analysis, a diagnostic model was constructed and evaluated using receiver operating characteristic curves and validated with training data and external validation data. We reclassified MDD into two molecular subtypes according to biomarkers and evaluated their expression levels. RESULTS: In total, 315 MDD-related MR-DEGs were identified. Functional enrichment analyses revealed that MR-DEGs were mainly enriched in mitophagy-related biological processes and multiple neurodegenerative disease pathways. Two distinct clusters with diverse immune infiltration characteristics were identified in the 144 MDD samples. MATR3, ACTL6A, FUS, BIRC2, and RIPK1 have been identified as potential biomarkers of MDD. All biomarkers showed varying degrees of correlation with immune cells. In addition, two molecular subtypes with distinct mitophagy gene signatures were identified. CONCLUSIONS: We identified a novel five-MRG gene signature that has excellent diagnostic performance and identified an association between MRGs and the immune microenvironment in MDD.

Deep learning attention-guided radiomics for COVID-19 chest radiograph classification.

Background: Accurate assessment of coronavirus disease 2019 (COVID-19) lung involvement through chest radiograph plays an important role in effective management of the infection. This study aims to develop a two-step feature merging method to integrate image features from deep learning and radiomics to differentiate COVID-19, non-COVID-19 pneumonia and normal chest radiographs (CXR). Methods: In this study, a deformable convolutional neural network (deformable CNN) was developed and used as a feature extractor to obtain 1,024-dimensional deep learning latent representation (DLR) features. Then 1,069-dimensional radiomics features were extracted from the region of interest (ROI) guided by deformable CNN's attention. The two feature sets were concatenated to generate a merged feature set for classification. For comparative experiments, the same process has been applied to the DLR-only feature set for verifying the effectiveness of feature concatenation. Results: Using the merged feature set resulted in an overall average accuracy of 91.0% for three-class classification, representing a statistically significant improvement of 0.6% compared to the DLR-only classification. The recall and precision of classification into the COVID-19 class were 0.926 and 0.976, respectively. The feature merging method was shown to significantly improve the classification performance as compared to using only deep learning features, regardless of choice of classifier (P value <0.0001). Three classes' F1-score were 0.892, 0.890, and 0.950 correspondingly (i.e., normal, non-COVID-19 pneumonia, COVID-19). Conclusions: A two-step COVID-19 classification framework integrating information from both DLR and radiomics features (guided by deep learning attention mechanism) has been developed. The proposed feature merging method has been shown to improve the performance of chest radiograph classification as compared to the case of using only deep learning features.

Modified the 8th AJCC staging system for patients with advanced prostate cancer: a study based on SEER database.

BACKGROUND: The American Joint Committee on Cancer (AJCC) 8th staging system of prostate cancer may be insufficient in predicting the prognosis of some staged patients. This study aimed to modify the AJCC 8th staging system in patients with advanced prostate cancer. METHODS: Data of patients with advanced prostate cancer from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2016 were enrolled in this cohort study. All patients were divided into the training set and the testing set with a ratio of 6:4. Multivariate Cox survival model was utilized to obtain the nomogram score for each stage variable. The modified staging system was based on the total nomogram score. The C-index and Kaplan-Meier (K-M) curves were used to show the prognostic prediction effect of patients with different staging systems. RESULTS: A total of 28,006 patients were included for analysis. T stage, N stage, M stage, primary Gleason pattern score, secondary Gleason pattern score, and PSA level were included as stage variables. Patients with AJCC stage III C [hazard ratio (HR) = 4.17, 95% confidence interval (CI), 3.39-5.13] and AJCC stage IV B (HR = 3.19, 95%CI, 1.79-5.69) were associated with worse prognosis compared with those of AJCC stage III B, while no statistical significance was found in patients with stage IV A (P > 0.05). In terms of the modified staging system, patients with modified stage III C (HR = 2.06, 95%CI, 1.46-2.92), modified stage IV A (HR = 6.91, 95%CI, 4.81-9.94), and modified stage IV B (HR = 21.89, 95%CI, 14.76-32.46) were associated with a poorer prognosis compared with patients with modified stage III B. The prognostic ability (C-index) of the modified staging system (0.789; 95%CI, 0.777-0.801) was better than that of the AJCC 8th edition system (0.762; 95%CI, 0.748-0.776) (0.789 vs. 0.762, P = 0.004). The K-M curves indicated that the modified staging system may be distinguished prognostic differences in patients with different stages. CONCLUSION: Modified staging system may be better than AJCC 8th staging system for predicting prognosis in prostate cancer patients. The AJCC 8th staging system should be further optimized.

Radiomics-Based Detection of COVID-19 from Chest X-ray Using Interpretable Soft Label-Driven TSK Fuzzy Classifier.

The COVID-19 pandemic has posed a significant global public health threat with an escalating number of new cases and death toll daily. The early detection of COVID-related CXR abnormality potentially allows the early isolation of suspected cases. Chest X-Ray (CXR) is a fast and highly accessible imaging modality. Recently, a number of CXR-based AI models have been developed for the automated detection of COVID-19. However, most existing models are difficult to interpret due to the use of incomprehensible deep features in their models. Confronted with this, we developed an interpretable TSK fuzzy system in this study for COVID-19 detection using radiomics features extracted from CXR images. There are two main contributions. (1) When TSK fuzzy systems are applied to classification tasks, the commonly used binary label matrix of training samples is transformed into a soft one in order to learn a more discriminant transformation matrix and hence improve classification accuracy. (2) Based on the assumption that the samples in the same class should be kept as close as possible when they are transformed into the label space, the compactness class graph is introduced to avoid overfitting caused by label matrix relaxation. Our proposed model for a multi-categorical classification task (COVID-19 vs. No-Findings vs. Pneumonia) was evaluated using 600 CXR images from publicly available datasets and compared against five state-of-the-art AI models in aspects of classification accuracy. Experimental findings showed that our model achieved classification accuracy of over 83%, which is better than the state-of-the-art models, while maintaining high interpretability.

Identifying Prognostic Biomarkers Related to m6A Modification and Immune Infiltration in Renal Cell Carcinoma.

BACKGROUND: Renal cell carcinoma (RCC) is the largest category of kidney tumors and usually does not have a good prognosis. N6-methyladenosine(m6A) and immune infiltration have received increased attention because of their great influence on the clinical outcome and prognosis of cancer patients. METHODS: We identified hub genes through multi-dimensional screening, including DEGs, PPI analysis, LASSO regression, and random forest. Meanwhile, GO/KEGG enrichment, cMAP analysis, prognostic analysis, m6A prediction, and immune infiltration analysis were performed to understand the potential mechanism and screen therapeutic drugs. RESULTS: We screened 275 downregulated and 185 upregulated genes using three GEO datasets and the TCGA dataset. In total, 82 candidate hub genes were selected using STRING and Cytoscape. Enrichment analysis illustrated that the top 3 biological process terms and top 1 KEGG term were related to immunity. cMAP analysis showed some antagonistic molecules can be candidate drugs for the treatment of RCC. Then, six hub genes (ERBB2, CASR, P2RY8, CAT, PLAUR, and TIMP1) with strong predictive values for prognosis and clinicopathological features were selected. Meanwhile, P2RY8, ERBB2, CAT, and TIMP1 may obtain m6A modification by binding METTL3 or METTL14. On the other hand, differential expression of CAT, ERBB2, P2RY8, PLAUR, and TIMP1 affects the infiltration of the majority of immune cells. CONCLUSIONS: We identified six hub genes through multi-dimensional screening. They all possess strong predictive value for prognosis and clinicopathological features. Meanwhile, hub genes may regulate the progression of RCC via an m6A- and immunity-dependent mechanism.

Development and validation of bone-suppressed deep learning classification of COVID-19 presentation in chest radiographs.

Background: Coronavirus disease 2019 (COVID-19) is a pandemic disease. Fast and accurate diagnosis of COVID-19 from chest radiography may enable more efficient allocation of scarce medical resources and hence improved patient outcomes. Deep learning classification of chest radiographs may be a plausible step towards this. We hypothesize that bone suppression of chest radiographs may improve the performance of deep learning classification of COVID-19 phenomena in chest radiographs. Methods: Two bone suppression methods (Gusarev et al. and Rajaraman et al.) were implemented. The Gusarev and Rajaraman methods were trained on 217 pairs of normal and bone-suppressed chest radiographs from the X-ray Bone Shadow Suppression dataset (https://www.kaggle.com/hmchuong/xray-bone-shadow-supression). Two classifier methods with different network architectures were implemented. Binary classifier models were trained on the public RICORD-1c and RSNA Pneumonia Challenge datasets. An external test dataset was created retrospectively from a set of 320 COVID-19 positive patients from Queen Elizabeth Hospital (Hong Kong, China) and a set of 518 non-COVID-19 patients from Pamela Youde Nethersole Eastern Hospital (Hong Kong, China), and used to evaluate the effect of bone suppression on classifier performance. Classification performance, quantified by sensitivity, specificity, negative predictive value (NPV), accuracy and area under the receiver operating curve (AUC), for non-suppressed radiographs was compared to that for bone suppressed radiographs. Some of the pre-trained models used in this study are published at (https://github.com/danielnflam). Results: Bone suppression of external test data was found to significantly (P<0.05) improve AUC for one classifier architecture [from 0.698 (non-suppressed) to 0.732 (Rajaraman-suppressed)]. For the other classifier architecture, suppression did not significantly (P>0.05) improve or worsen classifier performance. Conclusions: Rajaraman suppression significantly improved classification performance in one classification architecture, and did not significantly worsen classifier performance in the other classifier architecture. This research could be extended to explore the impact of bone suppression on classification of different lung pathologies, and the effect of other image enhancement techniques on classifier performance.

Volumetric multiphase ventilation imaging based on four-dimensional computed tomography for functional lung avoidance radiotherapy.

PURPOSE: Current computed tomography (CT)-based lung ventilation imaging (CTVI) techniques derive a static ventilation image without temporal information. This research aims to develop a four-dimensional CT (4DCT)-based multiphase dynamic ventilation imaging framework capable of recovering the entire ventilation process throughout the breathing cycle for functional lung avoidance radiotherapy (FLART). METHODS: A total of 15 free-breathing thoracic 4DCT scans of lung or esophageal cancer patients were collected from the public datasets. The lung region of each phase image was first delineated, and then the mask-free isotropic total variation image registration algorithm was used to derive the deformation vector fields between the end-expiration (EE) phase and other phases. As a surrogate of ventilation, the voxel-wise local expansion ratio of each phase relative to the EE phase was estimated using the parameterized Integrated Jacobian Formulation method in the EE phase coordinate. Lastly, the dynamic ventilation images were generated by warping these phase-specific local expansion distributions with a same geometry into their respective breathing phases. Quantitative analysis, including interphase Spearman correlation coefficients, voxel-wise, and regional-wise expansion/contraction tracking, were performed to indirectly validate the proposed method. RESULTS: The proposed method maintains the physiological meaning of ventilation on each phase and enables to recover the dynamic lung ventilation process. The mean interphase Spearman correlations ranged between 0.23 ± 0.20 and 0.93 ± 0.04 and decreased near the EE phase. Only 26.2% (2.59E + 6 out of 9.89E + 6) of lung voxels exhibited the same expansion/contraction pattern as the global lung. Qualitative and quantitative evaluations of the interphase ventilation distribution difference show that ventilation spatiotemporal heterogeneities generally exist during respiration. CONCLUSIONS: In contrast to conventional CTVI metrics, our method enables to extract additional phase-resolved respiration-correlated information and reflects the generally existed ventilation spatiotemporal heterogeneities. Subsequent studies with quantitative phase-by-phase cross-modality evaluations will further explore its potential to deepen our understanding of lung function and respiration mechanics and also to facilitate more accurate implementation of FLART.

Association of Gut Microbiota and Biochemical Features in a Chinese Population With Renal Uric Acid Stone.

Previous studies suggest that patients with nephrolithiasis exhibit dysbiosis in their gut microbiota, but those studies were conducted in calcium oxalate stone patients. We aimed to explore the association of gut microbiota and biochemical features of renal uric acid stone (UAS) patients in a Chinese population and identify the related bacteria that may affect the pathopoiesis of UAS. A case-control study of 117 patients with UAS, 123 patients with gout, and 135 healthy controls were included from January 2014 to October 2020. For each subject, data on demographics, biochemical parameters of blood and urine were analyzed. Fifteen patients with gout, 16 patients with UAS, 17 UAS patients with gout, and 17 healthy subjects were enrolled and provided fecal samples. The characteristics of gut microbiota were explored by using 16S ribosomal RNA (rRNA) gene sequencing and analyzed by using a combination of software mother and R. Hyperuricemia was the main risk factor for the development of gout and UAS. Obesity, dyslipidemia, and aciduria were unique risk factors for UAS patients. The richness, diversity, and relative abundance of dominant bacteria at the phylum and genus levels of gut microbiota in UAS patients were significantly distinct from other subjects. Abundance of Bacteroides and Fusobacterium was significantly positively correlated with the serum uric acid (UA) level of UAS patients. Fusobacteria was involved in the metabolism and degradation of certain short-chain fatty acids, amino acids, and sugars in pathopoiesis of UAS, and inhibited their synthesis pathways. Fusobacteria may be related to the pathogenesis of UAS, and this finding contributes to the personalized treatment of UAS from the perspective of maintaining micro-ecological equilibrium in gut.

Oncolytic Adenovirus with SPAG9 shRNA Driven by DD3 Promoter Improved the Efficacy of Docetaxil for Prostate Cancer.

Prostate cancer (PCa) is a common malignant tumor of the male urinary system and ranks the second in the causes of tumor-related deaths. Differential display code 3 (DD3) is a noncoding gene that is specifically expressed in PCa. High expression of sperm-associated antigen 9 (SPAG9) is closely related to tumorigenesis of PCa, and SPAG9 is a therapeutic target for PCa. In this study, a new oncolytic adenovirus DD3-ZD55-SPAG9 was constructed by using DD3 promoter to enhance the efficacy and safety of adenovirus. The combined use of DD3-ZD55-SPAG9 and docetaxel showed that DD3-ZD55-SPAG9 significantly improved the anti-tumor efficacy of docetaxel in PCa both in vitro and in vivo. The mechanism was related to the induction of tumor cell apoptosis and the inhibition of tumor cell invasion. In conclusion, DD3-ZD55-SPAG9 combined with docetaxel is an effective strategy for PCa therapy.

A case report of acute testicular pain secondary to segmental testicular infarction.

BACKGROUND: Segmental testicular infarction is a rare condition that often occurs in the upper pole of the left testicle and usually presents with acute onset of scrotal pain. Contrast-enhanced ultrasound and MR are essential for diagnosing and differentiating segmental testicular infarction in clinical practice, and conservative treatment can only be adopted after a definitive diagnosis. In the present case, after conservative treatment, the infarct volume was reduced, the blood flow around the infarct was increased, and blood flow signals appeared in the infarct. We performed a correlation analysis to investigate the causes of these changes. CASE PRESENTATION: A 33-year-old male, without any specific disease history, was admitted to the hospital with a 5-day history of left testicular pain, and the imaging showed focal necrosis of the left testicle with hemorrhage. He was diagnosed with segmental testicular infarction after differentiating and excluding it from malignant tumors. Conservative medical treatment was given, and the symptoms of testicular pain were relieved after treatment. After discharge, regular reexamination at follow-ups showed that the infarct's size was reduced, the blood flow around the infarct was increased, and blood flow signals appeared in the infarct. CONCLUSION: Conservative treatment has become the standard treatment currently adopted after confirming the diagnosis of segmental testicular infarction through contrast-enhanced ultrasound and MR. The blood flow changes in and around the focus of testicular infarction can be related to various factors. At present, relevant conclusions of the underlying mechanisms were mainly deduced from infarction studies of other related organs such as the heart and brain; thus, the specific pathological mechanism needs further experimental verification.

Deep Learning-Based Automatic Assessment of Radiation Dermatitis in Patients With Nasopharyngeal Carcinoma.

PURPOSE: Radiation dermatitis (RD) is a common, unpleasant side effect of patients receiving radiation therapy. In clinical practice, the severity of RD is graded manually through visual inspection, which is labor intensive and often leads to large interrater variations. To overcome these shortcomings, this study aimed to develop an automatic RD assessment based on deep learning (DL) techniques that could efficiently assist the RD severity classification in clinical application. METHODS AND MATERIALS: A total of 1205 photographs of the head and neck region were collected from patients with nasopharyngeal carcinoma (NPC) undergoing radiation therapy. The severity of RD in these photographs was graded by 5 qualified assessors based on the Radiation Therapy Oncology Group guidance. An end-to-end RD grading framework was developed by combining a DL-based segmentation network and a DL-based RD severity classifier, which are used for segmenting the neck region from the camera-captured photographs and grading, respectively. U-Net was used for segmentation and another convolutional neural network classifier (DenseNet-121) was applied to RD severity classification. Dice similarity coefficient was used to evaluate the performance of segmentation. Severity classification was evaluated by several metrics, including overall accuracy, precision, recall, and F1 score. RESULTS: Results of segmentation showed that the averaged dice similarity coefficients were 91.2% and 90.8% for front and side view, respectively. For RD severity classification, the overall accuracy of test photographs was 83.0%. Our method accurately classified 90.5% of grade 0, 67.2% of grade 1, 93.8% of grade 2, and 100% of above grade 2 cases. The overall prediction performance was comparable with human assessors. There was no significant difference in accuracy when using manually or automatically segmented regions (P = .683). CONCLUSIONS: We have successfully demonstrated a DL-based method for automatic assessment of RD severity in patients with NPC. This method holds great potential for efficient and effective assessing and monitoring of RD in patients with NPC.

Bioinformatics analysis of the clinical relevance of CDCA gene family in prostate cancer.

BACKGROUND: Prostate cancer (PCa) is the second most frequent cancer in men worldwide, and its mortality rate is increasing every year. The cell division cycle-associated (CDCA) gene family plays vital roles in the cell cycle process, but an analysis of these proteins in PCa is still lacking. METHODS: UALCAN and GEPIA were used to examine the transcriptional data and survival of the CDCA gene family in PCa patients. CDCA genetic alterations, prognostic value of genetic alterations, and correlations of CDCAs with each other in PCa were downloaded from cBioPortal. The functional enrichment data of CDCA-related genes were analyzed using DAVID. RESULTS: Six CDCA genes were upregulated in PCa tissues relative to those in normal tissues (P < .001), including NUF2, CDCA2, CDCA3, CDCA5, CBX2, and CDCA8. The expression levels of the 6 CDCAs were related to the tumor Gleason score (P < .05). In addition, survival analysis using GEPIA suggested that PCa patients with increased NUF2, CBX2, and CDCA2/3/5/8 expression levels had poor relapse-free survival (P < .05). Distinct patterns of genetic alterations of the 6 CDCAs were observed in PCa, and pairwise comparison of the mRNA expression of the 6 CDCAs displayed a close relationship. The biological functions of CDCA-related genes are principally associated with the activation of the following pathways: cell cycle, Fanconi anemia pathway, microRNAs in cancer, oocyte meiosis, and homologous recombination. CONCLUSIONS: Upregulated CDCA (NUF2, CBX2, and CDCA2/3/5/8) expression in PCa tissues may play a crucial role in the occurrence of PCa. These CDCAs can predict relapse-free survival prognosis and the Gleason score of patients with PCa. Moreover, CDCAs probably exert their functions in tumorigenesis through the cell cycle and miRNAs in the cancer pathway.