Ovarian juvenile granulosa cell tumors with Ollier’s disease in children with IDH1 gene somatic mutation

ObjectiveThe aim of this study was to explore the symptoms, treatment, and pathogenesis of ovarian juvenile granulosa cell tumors with Ollier’s disease in children.MethodsFrom October 2019 to October 2020, clinical data were retrospectively analyzed for one case of ovarian juvenile granulosa cell tumors with Ollier’s disease. Whole-exome sequencing and Sanger sequencing were used to detect gene mutation in ovarian tumor and chondroma tissue. NADP-dependent isocitrate dehydrogenase-1 (IDH1) and S6 ribosomal protein expression levels in cells transfected with wild-type or mutant plasmid were analyzed by Western blot.ResultsThe 4-year-old female showed multiple skeletal deformities, bilateral breast development with chromatosis, and vulvar discharge. Sex hormone assay suggested that estradiol and prolactin were elevated, and the x-ray of limbs suggested enchondroma. Pelvic ultrasound and abdominal CT revealed a right ovarian solid mass. Pathologic examination of the right ovarian solid mass showed a juvenile granulosa cell type. A c.394C>T (p. Arg132Cys) mutation of the IDH1 gene was detected in both the ovarian juvenile granulosa cell tumors and enchondroma. Transfection of HeLa cells with either WT or Mut plasmid caused 4.46- or 3.77-fold overexpression of IDH1 gene compared to non-transfected control cells, respectively. R132C mutation inhibited the phosphorylation of S6 ribosomal protein, which is central to the mTOR pathway. Postoperatively, estradiol and prolactin levels fell to values normal for her age and bilateral breast gradual retraction.ConclusionThe incidence of ovarian juvenile granulosa cell tumors with Ollier’s disease in children may be caused by generalized mesodermal dysplasia; IDH1 gene mutation may play a facilitated role in this process. Surgical operation is the main treatment. We suggest that patients with ovarian juvenile granulosa cell tumors and Ollier’s disease should undergo regular investigation

Patent ductus arteriosus shunting direction and diameter predict inpatient outcomes in newborns with congenital diaphragmatic hernia

ObjectiveTo evaluate whether the patent ductus arteriosus (PDA) can serve as a predictive factor for inpatient outcomes in congenital diaphragmatic hernia (CDH) patients.MethodsA retrospective cohort study was conducted on 59 CDH patients at the Capital Institute of Pediatrics from January 2020 to August 2022. Echocardiography was performed at least three times: within 2–3 h after birth, pre-operatively, and post-operatively of CDH surgery. Based on the direction of the PDA shunt in the first echocardiogram, patients were classified into three groups: left-to-right shunting or closed PDA (L-R), bi-directional shunting, and right-to-left shunting (R-L).ResultsThe mortality rate was 15.3% (9/59), with all non-survivors having R-L shunting and group mortality of 39.1% (9/23). The direction of the PDA shunt was significantly associated with the duration of ventilation and length of hospital stay (p < 0.05). Decreased PDA diameter or pre-operative shunting direction change towards L-R or bi-directional shunting were associated with higher survival rates, while increased PDA diameter or continuous R-L shunting were associated with higher mortality rates. Pre-operative PDA shunt direction, PDA size after birth and before surgery, gestational age of diagnosis, and shortening fraction before surgery were significantly correlated with patient outcomes. The direction of the preoperative PDA shunt was the most relevant factor among these relationships (p = 0.009, OR 20.6, CI 2.2∼196.1).ConclusionOur findings highlight the importance of monitoring changes in PDA shunt directionality and diameter in the early stage after birth, as these parameters may serve as valuable predictors of patient outcomes

Resistance to autosomal dominant Alzheimer's disease in an APOE3 Christchurch homozygote: a case report.

We identified a PSEN1 (presenilin 1) mutation carrier from the world's largest autosomal dominant Alzheimer's disease kindred, who did not develop mild cognitive impairment until her seventies, three decades after the expected age of clinical onset. The individual had two copies of the APOE3 Christchurch (R136S) mutation, unusually high brain amyloid levels and limited tau and neurodegenerative measurements. Our findings have implications for the role of APOE in the pathogenesis, treatment and prevention of Alzheimer's disease

Multidifferential study of identified charged hadron distributions in ZZ-tagged jets in proton-proton collisions at s=\sqrt{s}=13 TeV

Jet fragmentation functions are measured for the first time in proton-proton collisions for charged pions, kaons, and protons within jets recoiling against a $Z$ boson. The charged-hadron distributions are studied longitudinally and transversely to the jet direction for jets with transverse momentum 20 $< p_{\textrm{T}} < 100$ GeV and in the pseudorapidity range $2.5 < \eta < 4$. The data sample was collected with the LHCb experiment at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 1.64 fb$^{-1}$. Triple differential distributions as a function of the hadron longitudinal momentum fraction, hadron transverse momentum, and jet transverse momentum are also measured for the first time. This helps constrain transverse-momentum-dependent fragmentation functions. Differences in the shapes and magnitudes of the measured distributions for the different hadron species provide insights into the hadronization process for jets predominantly initiated by light quarks.Comment: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2022-013.html (LHCb public pages

Study of the B−→Λc+Λˉc−K−B^{-} \to \Lambda_{c}^{+} \bar{\Lambda}_{c}^{-} K^{-} decay

The decay $B^{-} \to \Lambda_{c}^{+} \bar{\Lambda}_{c}^{-} K^{-}$ is studied in proton-proton collisions at a center-of-mass energy of $\sqrt{s}=13$ TeV using data corresponding to an integrated luminosity of 5 $\mathrm{fb}^{-1}$ collected by the LHCb experiment. In the $\Lambda_{c}^+ K^{-}$ system, the $\Xi_{c}(2930)^{0}$ state observed at the BaBar and Belle experiments is resolved into two narrower states, $\Xi_{c}(2923)^{0}$ and $\Xi_{c}(2939)^{0}$, whose masses and widths are measured to be $$m(\Xi_{c}(2923)^{0}) = 2924.5 \pm 0.4 \pm 1.1 \,\mathrm{MeV}, \\ m(\Xi_{c}(2939)^{0}) = 2938.5 \pm 0.9 \pm 2.3 \,\mathrm{MeV}, \\ \Gamma(\Xi_{c}(2923)^{0}) = \phantom{000}4.8 \pm 0.9 \pm 1.5 \,\mathrm{MeV},\\ \Gamma(\Xi_{c}(2939)^{0}) = \phantom{00}11.0 \pm 1.9 \pm 7.5 \,\mathrm{MeV},$$ where the first uncertainties are statistical and the second systematic. The results are consistent with a previous LHCb measurement using a prompt $\Lambda_{c}^{+} K^{-}$ sample. Evidence of a new $\Xi_{c}(2880)^{0}$ state is found with a local significance of $3.8\,\sigma$, whose mass and width are measured to be $2881.8 \pm 3.1 \pm 8.5\,\mathrm{MeV}$ and $12.4 \pm 5.3 \pm 5.8 \,\mathrm{MeV}$, respectively. In addition, evidence of a new decay mode $\Xi_{c}(2790)^{0} \to \Lambda_{c}^{+} K^{-}$ is found with a significance of $3.7\,\sigma$. The relative branching fraction of $B^{-} \to \Lambda_{c}^{+} \bar{\Lambda}_{c}^{-} K^{-}$ with respect to the $B^{-} \to D^{+} D^{-} K^{-}$ decay is measured to be $2.36 \pm 0.11 \pm 0.22 \pm 0.25$, where the first uncertainty is statistical, the second systematic and the third originates from the branching fractions of charm hadron decays.Comment: All figures and tables, along with any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2022-028.html (LHCb public pages

Status of Clinical Application of Yufeng Ningxin Preparations and the Meta-Analysis of Its Efficacy and Safety in the Treatment of Cardiovascular and Cerebrovascular Diseases

Objective The objective of this study was to analyze the status of the clinical application of Yufeng Ningxin (YFNX) preparations and systematically evaluate their efficacy and safety in the treatment of cardiovascular and cerebrovascular diseases

Pilose antler extract restores type I and III collagen to accelerate wound healing

Granulation tissue has supporting and filling functions in wound healing. The collagen produced by fibroblast acts as a cell scaffold in the granulation tissue to facilitate the formation of new blood vessels and epithelial coverage. Previously, we extracted protein components from the pilose antler that was involved in the biological process of collagen fibril organization. They were also found to contain abundant extracellular matrix(ECM) components. Therefore, in this experiment, we used a rat model of full-thickness skin excision and fibroblasts to perform an experiment for determination of the effects of pilose antler protein extract (PAE) on collagen content and fiber synthesis during wound healing. Additionally, we further analyzed its pharmacological effects on wound healing and the possible regulatory mechanisms. We found that PAE accelerated synthesis of type I and III collagen, promoted the formation of type III collagen fibers, and reduced collagen degradation by recruiting fibroblasts. Furthermore, the extract upregulated the expression of TGF β R1 and Smad2, and initiated the entry of Smad2/Smad3 into the nucleus. After adding SB431542 to inhibit TGF-β type I receptor activity, PAE's ability to promote Smad2/Smad3 nuclear localization was weakened. These data indicate that local PAE therapy can promote the proliferation of fibroblasts, dynamically regulate the expression of TGF-β, and increase the amount of collagen and the synthesis of type III collagen fibers by promoting smad2 activity in the proliferation period, thus accelerating the regenerative healing of wounds

CLRD: Collaborative Learning for Retinopathy Detection Using Fundus Images

Retinopathy, a prevalent disease causing visual impairment and sometimes blindness, affects many individuals in the population. Early detection and treatment of the disease can be facilitated by monitoring the retina using fundus imaging. Nonetheless, the limited availability of fundus images and the imbalanced datasets warrant the development of more precise and efficient algorithms to enhance diagnostic performance. This study presents a novel online knowledge distillation framework, called CLRD, which employs a collaborative learning approach for detecting retinopathy. By combining student models with varying scales and architectures, the CLRD framework extracts crucial pathological information from fundus images. The transfer of knowledge is accomplished by developing distortion information particular to fundus images, thereby enhancing model invariance. Our selection of student models includes the Transformer-based BEiT and the CNN-based ConvNeXt, which achieve accuracies of 98.77% and 96.88%, respectively. Furthermore, the proposed method has 5.69–23.13%, 5.37–23.73%, 5.74–23.17%, 11.24–45.21%, and 5.87–24.96% higher accuracy, precision, recall, specificity, and F1 score, respectively, compared to the advanced visual model. The results of our study indicate that the CLRD framework can effectively minimize generalization errors without compromising independent predictions made by student models, offering novel directions for further investigations into detecting retinopathy

MIL-CT: Multiple Instance Learning via a Cross-Scale Transformer for Enhanced Arterial Light Reflex Detection

One of the early manifestations of systemic atherosclerosis, which leads to blood circulation issues, is the enhanced arterial light reflex (EALR). Fundus images are commonly used for regular screening purposes to intervene and assess the severity of systemic atherosclerosis in a timely manner. However, there is a lack of automated methods that can meet the demands of large-scale population screening. Therefore, this study introduces a novel cross-scale transformer-based multi-instance learning method, named MIL-CT, for the detection of early arterial lesions (e.g., EALR) in fundus images. MIL-CT utilizes the cross-scale vision transformer to extract retinal features in a multi-granularity perceptual domain. It incorporates a multi-head cross-scale attention fusion module to enhance global perceptual capability and feature representation. By integrating information from different scales and minimizing information loss, the method significantly improves the performance of the EALR detection task. Furthermore, a multi-instance learning module is implemented to enable the model to better comprehend local details and features in fundus images, facilitating the classification of patch tokens related to retinal lesions. To effectively learn the features associated with retinal lesions, we utilize weights pre-trained on a large fundus image Kaggle dataset. Our validation and comparison experiments conducted on our collected EALR dataset demonstrate the effectiveness of the MIL-CT method in reducing generalization errors while maintaining efficient attention to retinal vascular details. Moreover, the method surpasses existing models in EALR detection, achieving an accuracy, precision, sensitivity, specificity, and F1 score of 97.62%, 97.63%, 97.05%, 96.48%, and 97.62%, respectively. These results exhibit the significant enhancement in diagnostic accuracy of fundus images brought about by the MIL-CT method. Thus, it holds potential for various applications, particularly in the early screening of cardiovascular diseases such as hypertension and atherosclerosis

A Research on the Combination Strategies of Multiple Features for Hyperspectral Remote Sensing Image Classification

It has been common to employ multiple features in the identification of the images acquired by hyperspectral remote sensing sensors, since more features give more information and have complementary properties. Few studies have discussed the combination strategies of multiple feature groups. This study made a systematic research on this problem. We extracted different groups of features from the initial hyperspectral images and tried different combination scenarios. We integrated spectral features with different textural features and employed different dimensionality reduction algorithms. Experimental results on three widely used hyperspectral remote sensing images suggested that “dimensionality reduction before combination” performed better especially when textural features performed well. The study further compared different combination frameworks of multiple feature groups, including direct combination, manifold learning, and multiple kernel method. The experimental results demonstrated the effectiveness of direct combination with an autoweight calculation