CYP24A1 affected macrophage polarization through degradation of vitamin D as a candidate biomarker for ovarian cancer prognosis.

Ovarian cancer (OC) is a fatal gynecological malignancy with a poor prognosis in which mitochondria-related genes are involved deeply. In this study, we aim to screen mitochondria-related genes that play a role in OC prognosis and investigate its effects. Through single-cell sequencing technology and bioinformatics analysis, including TCGA ovarian cancer data analysis, gene expression signature analysis (GES), immune infiltration analysis, Gene Ontology (GO) enrichment analysis, Gene Set Enrichment Analysis (GSEA), and Principal Component Analysis (PCA), our findings revealed that CYP24A1 regulated macrophage polarization through vitamin D (VD) degradation and served as a target gene for the second malignant subtype of OC through bioinformatics analyses. For further validation, the expression and function of CYP24A1 in OC cells was investigated. And the expression of CYP24A1 was much higher in carcinoma than in paracancerous tissue, whereas the VD content decreased in the OC cell lines with CYP24A1 overexpression. Moreover, macrophages were polarized towards M1 after the intervention of VD-treated OC cell lines and inhibited the malignant phenotypes of OC. However, the effect could be reversed by overexpressing CYP24A1, resulting in the polarization of M2 macrophages, thereby promoting tumor progression, as verified by constructing xenograft models in vitro. In conclusion, our findings suggested that CYP24A1 induced M2 macrophage polarization through interaction with VD, thus promoting the malignant progression of OC.

Circulating tumor DNA methylation: a promising clinical tool for cancer diagnosis and management.

Cancer continues to pose significant challenges to the medical community. Early detection, accurate molecular profiling, and adequate assessment of treatment response are critical factors in improving the quality of life and survival of cancer patients. Accumulating evidence shows that circulating tumor DNA (ctDNA) shed by tumors into the peripheral blood preserves the genetic and epigenetic information of primary tumors. Notably, DNA methylation, an essential and stable epigenetic modification, exhibits both cancer- and tissue-specific patterns. As a result, ctDNA methylation has emerged as a promising molecular marker for noninvasive testing in cancer clinics. In this review, we summarize the existing techniques for ctDNA methylation detection, describe the current research status of ctDNA methylation, and present the potential applications of ctDNA-based assays in the clinic. The insights presented in this article could serve as a roadmap for future research and clinical applications of ctDNA methylation.

Meconium peritonitis in multiple intestinal atresia with combined immune deficiency caused by a TTC7A mutation: A case report.

Multiple intestinal atresia with combined immune deficiency is a severe autosomal recessive disorder caused by the tetratricopeptide repeat domain 7A (TTC7A) gene deficiency, which is characterized by extensive intestinal defects with immune deficiency. This report describes a fetus with TTC7A deficiency who developed meconium peritonitis in utero. Evidence suggests that patients with TTC7A deficiency present with intestinal defects as early as in utero. In this case, intestinal abnormalities were considered during the prenatal examination at week 28, and chromosome and genetic tests were performed. The results indicated that the fetus had a TTC7A complex heterozygous mutation. The male infant underwent surgical treatment after birth and developed severe infection and sepsis, which confirmed the presence of multiple intestinal atresia with combined immune deficiency. Our case suggests an association between meconium peritonitis and the TTC7A gene deficiency, indicating the possibility of severe intestinal defects and immune deficiencies after birth and guiding subsequent fetal treatment choices.

Natural polymer-based scaffolds for soft tissue repair.

Soft tissues such as skin, muscle, and tendon are easily damaged due to injury from physical activity and pathological lesions. For soft tissue repair and regeneration, biomaterials are often used to build scaffolds with appropriate structures and tailored functionalities that can support cell growth and new tissue formation. Among all types of scaffolds, natural polymer-based scaffolds attract much attention due to their excellent biocompatibility and tunable mechanical properties. In this comprehensive mini-review, we summarize recent progress on natural polymer-based scaffolds for soft tissue repair, focusing on clinical translations and materials design. Furthermore, the limitations and challenges, such as unsatisfied mechanical properties and unfavorable biological responses, are discussed to advance the development of novel scaffolds for soft tissue repair and regeneration toward clinical translation.

Anti-Müllerian Hormone Accelerates Pathological Process of Insulin Resistance in Polycystic Ovary Syndrome Patients.

Insulin resistance (IR) is one of the most common features of polycystic ovary syndrome (PCOS), which is related to obesity. Whether increased anti-Müllerian hormone (AMH) levels in PCOS are involved in the pathogenesis of insulin resistance remains unclear. We investigated serum levels of leptin and AMH along with basic clinical and metabolic parameters in 114 PCOS patients and 181 non-PCOS women. PCOS patients presented higher fasting blood glucose, insulin concentrations and Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) in addition to body mass index (BMI), lipids profiles and hormone levels. HOMA-IR showed a positive correlation with BMI, AMH, leptin, and low-density lipoprotein-cholesterol (LDL-c) levels. Interestingly, AMH is strongly positively correlated with HOMA-IR and insulin concentrations for 1st and 2nd hours of glucose treatment after fasting. Among PCOS women with BMI≥25 kg/m2, high AMH level group showed an increased HOMA-IR when compared to normal AMH level. However, among PCOS women with normal BMI, women with high AMH presented an elevated fasting insulin levels but not HOMA-IR when compared to normal AMH group. In vitro treatment of isolated islet cells with high concentration of leptin (200 ng/ml) or high leptin plus high concentration of AMH (1 ng/ml) significantly enhanced insulin secretion. Importantly, co-treatment of AMH plus leptin upregulates the expression of pro-apoptotic proteins, such as Bax, caspase-3, and caspase-8 after incubating with a high level of glucose. These results suggest that AMH may involve in the pathological process of pancreatic ß-cells in obese PCOS women.

Epidemic situation of the complex seasonality of imported influenza A and B virus transmission in Guangxi ports of China

Background/aim: Analysis of the characteristics of influenza virus in imported cases in Guangxi province of China. Materials and methods: Throat swabs of imported cases with influenza-like symptoms were detected by real-time PCR from July 2016 to December 2019. Results: 1292 laboratory detections of influenza were reported in 3974 influenza-like cases, of which 71.67% (926) were influenza A. The ratio of test positive was 32.82%. The proportion of detections of influenza B was 28.33% (366). A total of 70.51% of the cases mostly came from Vietnam (911). A total of 86.76% (1121) of the cases were imported from Dongxing Port, Nanning Airport, and Pingxiang Port. There was no statistical difference in all age groups. At the same time, 3 of the untyped A-type specimens were sequenced by next- generation sequencing. Among them, the sequences of 2 specimens from Vietnam had high homology with the influenza strain H3N2 in Hong Kong in 2017. The specimen sequence from Thailand is highly homologous to the influenza pandemic strain H1N1 in Brisbane, Australia in 2018. Conclusion: Imported influenza cases in Guangxi have occurred throughout the year, with higher numbers in winter and spring. The cases mostly came from Vietnam with influenza A. Relevant measures should be taken to control the further spread of the virus.

Cesarean scar pregnancy combined with arteriovenous malformation successfully treated with transvaginal fertility-sparing surgery: A case report and literature review.

INTRODUCTION: A cesarean scar pregnancy (CSP), when combined with an arteriovenous malformation (AVM), is a rare, but potentially life-threatening condition that may be associated with uncontrolled hemorrhage. Hysterectomy is indicated when conservative treatment fails. Preservation of fertility is challenging. PATIENT CONCERNS: We reported a 33-year-old woman with a CSP combined with an AVM who failed methotrexate administration as conservative treatment. DIAGNOSES: A CSP combined with an AVM was diagnosed via three-dimensional color Doppler angiogram and magnetic resonance imaging. INTERVENTIONS: Transvaginal removal of the ectopic gestation and repair of the uterine defect was performed without incident. OUTCOMES: The fertility of the patient was preserved and hysterectomy was avoided. CONCLUSION: Transvaginal fertility-sparing surgery may be successfully performed to prevent hysterectomy when conservative treatment fails in patients with a CSP combined with an AVM.

Perinatal depressive and anxiety symptoms of pregnant women during the coronavirus disease 2019 outbreak in China.

BACKGROUND: On January 20, 2020, a new coronavirus epidemic with human-to-human transmission was officially declared by the Chinese government, which caused significant public panic in China. In light of the coronavirus disease 2019 outbreak, pregnant women may be particularly vulnerable and in special need for preventive mental health strategies. Thus far, no reports exist to investigate the mental health response of pregnant women to the coronavirus disease 2019 outbreak. OBJECTIVE: This study aimed to examine the impact of coronavirus disease 2019 outbreak on the prevalence of depressive and anxiety symptoms and the corresponding risk factors among pregnant women across China. STUDY DESIGN: A multicenter, cross-sectional study was initiated in early December 2019 to identify mental health concerns in pregnancy using the Edinburgh Postnatal Depression Scale. This study provided a unique opportunity to compare the mental status of pregnant women before and after the declaration of the coronavirus disease 2019 epidemic. A total of 4124 pregnant women during their third trimester from 25 hospitals in 10 provinces across China were examined in this cross-sectional study from January 1, 2020, to February 9, 2020. Of these women, 1285 were assessed after January 20, 2020, when the coronavirus epidemic was publicly declared and 2839 were assessed before this pivotal time point. The internationally recommended Edinburgh Postnatal Depression Scale was used to assess maternal depression and anxiety symptoms. Prevalence rates and risk factors were compared between the pre- and poststudy groups. RESULTS: Pregnant women assessed after the declaration of coronavirus disease 2019 epidemic had significantly higher rates of depressive symptoms (26.0% vs 29.6%, P=.02) than women assessed before the epidemic declaration. These women were also more likely to have thoughts of self-harm (P=.005). The depressive rates were positively associated with the number of newly confirmed cases of coronavirus disease 2019 (P=.003), suspected infections (P=.004), and deaths per day (P=.001). Pregnant women who were underweight before pregnancy, primiparous, younger than 35 years, employed full time, in middle income category, and had appropriate living space were at increased risk for developing depressive and anxiety symptoms during the outbreak. CONCLUSION: Major life-threatening public health events such as the coronavirus disease 2019 outbreak may increase the risk for mental illness among pregnant women, including thoughts of self-harm. Strategies targeting maternal stress and isolation such as effective risk communication and the provision of psychological first aid may be particularly useful to prevent negative outcomes for women and their fetuses.

Proteomics of Uterosacral Ligament Connective Tissue from Women with and without Pelvic Organ Prolapse.

PURPOSE: Damage to the uterosacral ligaments is an important contributor to uterine and vaginal prolapse. The aim of this study is to identify differentially expressed proteins (DEPs) in the uterosacral ligaments of women with and without pelvic organ prolapse (POP) and analyze their relationships to cellular mechanisms involved in the pathogenesis of POP. EXPERIMENTAL DESIGN: Uterosacral ligament connective tissue from four patients with POP and four control women undergo iTRAQ analysis followed by ingenuity pathway analysis (IPA) of DEPs. DEPs are validated using Western blot analysis. RESULTS: A total of 1789 unique protein sequences are identified in the uterosacral ligament connective tissues. The expression levels of 88 proteins are significantly different between prolapse and control groups (≥1.2-fold, p < 0.05). IPA demonstrates the association of 14 DEPs with "Connective Tissue Function." Among them, fibromodulin, collagen alpha-1 (XIV) chain, calponin-1, tenascin, and galectin-1 appear most likely to play a role in the etiology of POP. CONCLUSIONS AND CLINICAL RELEVANCE: At least six proteins not previously associated with the pathogenesis of POP with biologic functions that suggest a plausible relationship to the disorder are identified. These results may be helpful for furthering the understanding of the pathophysiological mechanisms of POP.

[A New Spectral-Spatial Algorithm Method for Hyperspectral Image Target Detection].

With high-resolution spatial information and continuous spectrum information, hyperspectral remote sensing image -has a unique advantage in the field of target detection. Traditional hyperspectral remote sensing image target detection methods emphasis on using spectral information to determine deterministic algorithm and statistical algorithms. Deterministic algorithms find the target by calculating the distance between the target spectrum and detected spectrum however, they are unable to detect sub-pixel target and are easily affected by noise. Statistical methods which calculate background statistical characteristics to detect abnormal point as target. It can detect subpixel target targets and small targets better thanbig size target,. With the spatial resolution increasing, subpixel target detection target has gradually grown to a single pixel and multi-pixel target. At this point, hyperspectral image usually has large homogeneous regions where the neighboring pixels wihin the regions consist of the same type of materials and have a similar spectral characteristics, therefore, the spatial information should be needed to incorporate into the algorithm for targe detection. This paper proposes an algorithm for hyperspectral target detection combined spectrum characteristics and spatial characteristics. The algorithm is based on traditional target detection operator and combined neighborhood clustering statistics. Firstly, the algorithm uses target detection operator to divided hyperspectral image into a potential target region and background region. Then, it calculates the centroid of the potential target area. Finally, as the centroid for neighborhood clustering center to clust data in order to exclud background from potential target area, through iterative calculation to obtain the final results of the target detection. The traditional statistics algorithms defines the total image as background area in order to extract background statistics features, and the algorithm propsed devided the total image into background part and potential target part, which cut off the target interference for background statistics feature extraction. Compared with CEM operators and ACE operators, the algorithm proposed outperforms than traditional operators in big target detection .

[A Novel Spatial-Spectral Sparse Representation for Hyperspectral Image Classification Based on Neighborhood Segmentation].

Traditional hyperspectral image classification algorithms focus on spectral information application, however, with the increase of spatial resolution of hyperspectral remote sensing images, hyperspectral imaging presents clustering properties on spatial domain for the same category. It is critical for hyperspectral image classification algorithms to use spatial information in order to improve the classification accuracy. However, the marginal differences of different categories display more obviously. If it is introduced directly into the spatial-spectral sparse representation for image classification without the selection of neighborhood pixels, the classification error and the computation time will increase. This paper presents a spatial-spectral joint sparse representation classification algorithm based on neighborhood segmentation. The algorithm calculates the similarity with spectral angel in order to choose proper neighborhood pixel into spatial-spectral joint sparse representation model. With simultaneous subspace pursuit and simultaneous orthogonal matching pursuit to solve the model, the classification is determined by computing the minimum reconstruction error between testing samples and training pixels. Two typical hyperspectral images from AVIRIS and ROSIS are chosen for simulation experiment and results display that the classification accuracy of two images both improves as neighborhood segmentation threshold increasing. It concludes that neighborhood segmentation is necessary for joint sparse representation classification.

Human leukocyte antigen-G (HLA-G) expression in cervical cancer lesions is associated with disease progression.

The immunotolerant human leukocyte antigen (HLA)-G has direct inhibitory effects on natural killer cells, dendritic cells, T cells and can indirectly induce tolerant regulatory cells. The significance of the aberrant HLA-G expression in malignant contexts has been intensively investigated. In the current study, HLA-G expression in 22 normal cervical tissues, 14 cervical intraepithelial neoplasia (CIN) patients and 129 patients with squamous cell cervical cancer were examined using immunohistochemistry. The association of HLA-G expression with disease progression was calculated with the Pearson Chi-square test. It was found that HLA-G expression was absent in normal cervical tissues, and that HLA-G expression was increased from patients with CIN III (35.7%, 4/14) to patients with cervical cancer (62.8%, 81/129). Among the cervical cancer patients, HLA-G expression in FIGO stage I, II, and stage III+IV was 53.6% (45/84), 76.3% (29/38), and 100.0% (7/7), respectively. Taken together, our findings indicated that HLA-G expression was associated with the disease progression in patients with cervical cancer.