[Clinical characteristics and treatment in adults and children with histiocytic necroti-zing lymphadenitis].

OBJECTIVE: To analyze the clinical data of histiocytic necrotizing lymphadenitis(HNL), comparing the similarities and differences between children and adults, to deepen the understanding of the disease by clinical physicians, and to improve diagnostic rate and reduce misdiagnosis and mistreatment. METHODS: The clinical data of hospitalized patients with histiocytic necrotizing lymphadenitis diagnosed by biopsy from January 2010 to August 2023 in Peking University First Hospital were collec-ted, and the clinical features, laboratory examinations, pathological features, treatments with antibiotics and glucocorticoids, and prognosis of histiocytic necrotic lymphadenitis were analyzed. Grouped based on age, the differences of clinical characteristics, laboratory tests, treatment, and prognosis between the children group (< 16 years old) and the adult group (≥16 years old) were compared. RESULTS: Among the 81 enrolled patients, there were 42 males and 39 females. The median age was 21(14, 29) years, the median duration of disease was 20.0(13.0, 30.0) days, and the median length of hospital stay was 13.0 (10.0, 15.0) days. The first symptoms were fever, lymphadenopathy, and both. All the patients had enlarged lymph nodes with different parts and sizes, 96.3% (78 of 81) of the patients had cervical lymphadenopathy, 50.6% (41 of 81) had bilateral cervical lymphadenopathy, 55.6% (45 of 81) had supraclavicular, axillary or inguinal lymphadenopathy, and the median lymph node diameter was 20.0(20.0, 30.0) mm. Only one patient had no fever, the other 80 patients had fever, the median peak body temperature was 39.0(38.0, 39.8) ℃. Accompanying symptoms: rash (8.6%, 7/81), fatigue (34.6%, 28/81), night sweating (8.6%, 7/81), chills (25.3%, 25/81), muscle soreness (13.6%, 11/81), and joint pain (6.2%, 5/81). There were 17 cases (21.0%, 17/81) of hepatosplenomegaly, of which 12 cases (70.6%, 12/17) were splenomegaly. 68.8%(55/80) of patients had a decrease in white blood cell (WBC) count, with 47.5%(38/80)increased in lymphocyte(LY)proportion, 53.4%(39/73) increased in high-sensitivity C-reactive protein(CRP), 79.2%(57/72) increased in erythrocyte sedimentation rate(ESR), 22.2%(18/81) increased in alanine transaminase(ALT), 27.2%(22/81) elevated in aspartate transaminase(AST), and 81.6%(62/76) elevated in lactate dehydrogenase(LDH). All the 81 patients underwent lymph node biopsy, and 77.8%(63/81) of the patients showed that most of the structures in the lymph nodes were destroyed or disappeared, and 16.0%(13/81) of the lymph nodes were still in existence, hyperplasia and normal lymph node were 1.2%(1/81) respectively, and 3.7%(3/81) had normal lymph node structures. Immunohistochemical staining was performed in 67 cases. The percentages of CD3+ and CD68(KP1)+ were respectively 97.0%(65/67), and MPO+ were 94.0%(63/67). In the study, 51 patients (63.0%, 51/81) were treated with glucocorticoid therapy after diagnosis. The median time for temperature to return to normal was 1.0(1.0, 4.0) days after glucocorticoid therapy. when the glucocorticoid treatment worked best, the body temperature could drop to normal on the same day. There were significant differences in length of stay, predisposing factors, chills, the rate of increase in high-sensitivity CRP, antibiotic and glucocorticoid treatment between the adults and children groups (P < 0.05). CONCLUSION: In clinical practice, if there are cases with unexplained fever, superficial lymph node enlargement, and reduced white blood cells as clinical characteristics, and general antibiotics treatment is ineffective, histiocytic necrotic lymphadenitis should be considered. Lymph node biopsy should be performed as early as possible to clarify the diagnosis, reduce misdiagnosis and mistreatment, and symptomatic treatment should be the main treatment. Glucocorticoids therapy has a definite therapeutic effect.

Momentum contrast transformer for COVID-19 diagnosis with knowledge distillation.

Intelligent diagnosis has been widely studied in diagnosing novel corona virus disease (COVID-19). Existing deep models typically do not make full use of the global features such as large areas of ground glass opacities, and the local features such as local bronchiolectasis from the COVID-19 chest CT images, leading to unsatisfying recognition accuracy. To address this challenge, this paper proposes a novel method to diagnose COVID-19 using momentum contrast and knowledge distillation, termed MCT-KD. Our method takes advantage of Vision Transformer to design a momentum contrastive learning task to effectively extract global features from COVID-19 chest CT images. Moreover, in transfer and fine-tuning process, we integrate the locality of convolution into Vision Transformer via special knowledge distillation. These strategies enable the final Vision Transformer simultaneously focuses on global and local features from COVID-19 chest CT images. In addition, momentum contrastive learning is self-supervised learning, solving the problem that Vision Transformer is challenging to train on small datasets. Extensive experiments confirm the effectiveness of the proposed MCT-KD. In particular, our MCT-KD is able to achieve 87.43% and 96.94% accuracy on two publicly available datasets, respectively.

Cross-species scRNA-seq reveals the cellular landscape of retina and early alterations in type 2 diabetes mice.

Single-cell RNA sequencing (scRNA-seq) analysis have provided an unprecedented resolution for the studies on diabetic retinopathy (DR). However, the early changes in the retina in diabetes remain unclear. A total of 8 human and mouse scRNA-seq datasets, containing 276,402 cells were analyzed individually to comprehensively delineate the retinal cell atlas. The neural retinas were isolated from the type 2 diabetes (T2D) and control mice, and scRNA-seq analysis was conducted to evaluate the early effects of diabetes on the retina. Bipolar cell (BC) heterogeneity were identified. We found some stable BCs across multiple datasets, and explored their biological functions. A new RBC subtype (Car8_RBC) in the mouse retina was validated using the multi-color immunohistochemistry. AC149090.1 was significantly upregulated in the rod cells, ON cone BCs (CBCs), OFF CBCs, and RBCs in T2D mice. Additionally, the interneurons, especially BCs, were the most vulnerable cells to diabetes by integrating scRNA-seq and genome-wide association studies (GWAS) analyses. In conclusion, this study delineated a cross-species retinal cell atlas and uncovered the early pathological alterations in the retina of T2D mice.

Immune profiling and prognostic model of pancreatic cancer using quantitative pathology and single-cell RNA sequencing.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) has a complex tumor immune microenvironment (TIME), the clinical value of which remains elusive. This study aimed to delineate the immune landscape of PDAC and determine the clinical value of immune features in TIME. METHODS: Univariable and multivariable Cox regression analyses were performed to evaluate the clinical value of immune features and establish a new prognostic model. We also conducted single-cell RNA sequencing (scRNA-seq) to further characterize the immune profiles of PDAC and explore cell-to-cell interactions. RESULTS: There was a significant difference in the immune profiles between PDAC and adjacent noncancerous tissues. Several novel immune features were captured by quantitative pathological analysis on multiplex immunohistochemistry (mIHC), some of which were significantly correlated with the prognosis of patients with PDAC. A risk score-based prognostic model was established based on these immune features. We also constructed a user-friendly nomogram plot to predict the overall survival (OS) of patients by combining the risk score and clinicopathological features. Both mIHC and scRNA-seq analysis revealed PD-L1 expression was low in PDAC. We found that PD1 + cells were distributed in different T cell subpopulations, and were not enriched in a specific subpopulation. In addition, there were other conserved receptor-ligand pairs (CCL5-SDC1/4) besides the PD1-PD-L1 interaction between PD1 + T cells and PD-L1 + tumor cells. CONCLUSION: Our findings reveal the immune landscape of PDAC and highlight the significant value of the combined application of mIHC and scRNA-seq for uncovering TIME, which might provide new clues for developing immunotherapy combination strategies.

Efficacy of anti-osteoporosis treatment for men with osteoporosis: a meta-analysis.

INTRODUCTION: Osteoporosis and fracture are important healthcare problems for men worldwide, which are relevant to severe disability and mortality. This meta-analysis aimed to assess the effectiveness of pharmacological therapy in men with osteoporosis, and to provide evidence-based hints for clinical practice. MATERIALS AND METHODS: PubMed, Embase, Web of Science were searched from inception to July 31, 2022. Pooled standardized mean difference (SMD) and relative risk (RR) were calculated. Heterogeneity between included studies and publication bias were detected. RESULTS: Twenty clinical studies were enrolled in this meta-analysis. The pooled SMD for mean percentage differences of change from baseline in lumbar spine BMD between the treatment group and the control group was 4.95 (95% CI 2.48, 7.42, I2 = 99%, p < 0.0001). For mean percentage differences of change in femoral neck BMD, the overall SMD was 3.08 (95% CI 0.95, 5.20, I2 = 99%, p = 0.0045). For a change in total hip BMD, the overall SMD was 1.06 (95% CI 0.50, 1.63, I2 = 82%, p = 0.0002). The overall RR for incident vertebral fractures was 0.50 (95% CI 0.37, 0.68, I2 = 5%, p = 0.3971). The pooled RR for nonvertebral fractures and clinical fracture were 0.74 (95% CI 0.41, 1.33, I2 = 28%, p = 0.3139) and 0.81 (95% CI 0.54, 1.21, I2 = 0%, p = 0.2992). CONCLUSION: Findings in this meta-analysis indicate that pharmacological treatment increases lumbar spine, femoral neck, total hip BMD, and decreases incident vertebral fractures in men with osteoporosis.

Single-cell RNA-seq transcriptomic landscape of human and mouse islets and pathological alterations of diabetes.

Single-cell RNA sequencing has paved the way for delineating the pancreatic islet cell atlas and identifying hallmarks of diabetes. However, pathological alterations of type 2 diabetes (T2D) remain unclear. We isolated pancreatic islets from control and T2D mice for single-cell RNA sequencing (scRNA-seq) and retrieved multiple datasets from the open databases. The complete islet cell landscape and robust marker genes and transcription factors of each endocrine cell type were identified. GLRA1 was restricted to beta cells, and beta cells exhibited obvious heterogeneity. The beta subcluster in the T2D mice remarkably decreased the expression of Slc2a2, G6pc2, Mafa, Nkx6-1, Pdx1, and Ucn3 and had higher unfolded protein response (UPR) scores than in the control mice. Moreover, we developed a Web-based interactive tool, creating new opportunities for the data mining of pancreatic islet scRNA-seq datasets. In conclusion, our work provides a valuable resource for a deeper understanding of the pathological mechanism underlying diabetes.

Latent feature representation learning for Alzheimer's disease classification.

Early detection and treatment of Alzheimer's Disease (AD) are significant. Recently, multi-modality imaging data have promoted the development of the automatic diagnosis of AD. This paper proposes a method based on latent feature fusion to make full use of multi-modality image data information. Specifically, we learn a specific projection matrix for each modality by introducing a binary label matrix and local geometry constraints and then project the original features of each modality into a low-dimensional target space. In this space, we fuse latent feature representations of different modalities for AD classification. The experimental results on Alzheimer's Disease Neuroimaging Initiative database demonstrate the proposed methods effectiveness in classifying AD.

Effects of calorie intake and sampling time on thyroid stimulating hormone concentration.

BACKGROUND: This study aimed to investigate the effects of blood sampling after calorie intake on thyroid stimulating hormone (TSH) level, compared with blood sampling in fasting state. METHODS: This was a prospective, randomized, controlled study. Subjects from the outpatients in the department of endocrinology without evidence of thyroid diseases were included and then randomized into the fasting group, diet intake group, and glucose intake group, respectively. Fasting blood was collected from all subjects at 7:00 am for the measurement of TSH and free thyroxine (FT4) concentrations. Afterwards, the subjects were maintained at fasting state (fasting group), had an intake of the mixed diet with 400 kcal calories (diet intake group), and had an intake of 75 g glucose (glucose intake group), respectively, and blood was collected again 2 h later (9:00 am on the same day) for TSH and FT4 level measurement and comparison. RESULTS: A total of 150 subjects were enrolled, of whom 146 met the inclusion criteria, with 48, 48, and 50 in the diet intake group, glucose intake group, and fasting group, respectively. The TSH in the diet intake group was significantly lower at 9:00 am (TSH9am) than the level at 7:00 am (TSH7am) (P < 0.001), with a median variation of -0.71 mU/L, and a median variation rate of -32.4%. In the glucose intake group, TSH9am was also significantly lower than TSH7am (P < 0.001), with a median variation of -0.73 mU/L, and a median variation rate of -31.5%. For the fasting group, TSH9am decreased slightly but was significantly lower than TSH7am (P < 0.001), with a median variation of -0.1 mU/L, and a median variation rate of -5.2%. According to TSH7am measurements, 9 subjects (3 subjects in each group) met the diagnostic criteria of subclinical hypothyroidism. However, according toTSH9am measurements, only 2 patients in the fasting group met the diagnostic criteria of subclinical hypothyroidism. CONCLUSION: Compared with the fasting state, the TSH level at 2 h after the calorie intake was decreased by about 30%, which might influence the diagnosis of subclinical hypothyroidism. TRIAL REGISTRATION: ChiCTR2100047454 (18/06/2021).

Consistency Between Thyrotropin Receptor Antibody (TRAb) and Thyroid-Stimulating Antibody (TSAb) Levels in Patients with Graves Disease.

OBJECTIVE: To investigate the consistency between thyrotropin receptor antibody (TRAb) and thyroid-stimulating antibody (TSAb) levels in patients with Graves disease (GD). METHODS: We performed a cross-sectional observational study to recruit eligible patients with GD who visited the outpatient endocrinology clinic for the purpose of evaluating the consistency between their TRAb and TSAb levels. Our cohort included 28 men and 99 women. RESULTS: The median levels of TRAb and TSAb were 5.65 IU/L and 3.76 IU/L, respectively, in the enrolled patients with GD. The levels of TRAb (5.03 vs 8.42 IU/L; P = .008) and TSAb (2.69 vs 5.37 IU/L; P = .008) in patients with adequate thyroid regulation were all lower than those in patients with inadequate thyroid regulation. CONCLUSIONS: Although TRAb is closely related to TSAb, we observed high heterogeneity of TRAb due to relatively low consistency between the levels of the 2 antibodies.

Transferred Subspace Learning Based on Non-negative Matrix Factorization for EEG Signal Classification.

EEG signal classification has been a research hotspot recently. The combination of EEG signal classification with machine learning technology is very popular. Traditional machine leaning methods for EEG signal classification assume that the EEG signals are drawn from the same distribution. However, the assumption is not always satisfied with the practical applications. In practical applications, the training dataset and the testing dataset are from different but related domains. How to make best use of the training dataset knowledge to improve the testing dataset is critical for these circumstances. In this paper, a novel method combining the non-negative matrix factorization technology and the transfer learning (NMF-TL) is proposed for EEG signal classification. Specifically, the shared subspace is extracted from the testing dataset and training dataset using non-negative matrix factorization firstly and then the shared subspace and the original feature space are combined to obtain the final EEG signal classification results. On the one hand, the non-negative matrix factorization can assure to obtain essential information between the testing and the training dataset; on the other hand, the combination of shared subspace and the original feature space can fully use all the signals including the testing and the training dataset. Extensive experiments on Bonn EEG confirmed the effectiveness of the proposed method.

High-Order Laplacian Regularized Low-Rank Representation for Multimodal Dementia Diagnosis.

Multimodal heterogeneous data, such as structural magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF), are effective in improving the performance of automated dementia diagnosis by providing complementary information on degenerated brain disorders, such as Alzheimer's prodromal stage, i.e., mild cognitive impairment. Effectively integrating multimodal data has remained a challenging problem, especially when these heterogeneous data are incomplete due to poor data quality and patient dropout. Besides, multimodal data usually contain noise information caused by different scanners or imaging protocols. The existing methods usually fail to well handle these heterogeneous and noisy multimodal data for automated brain dementia diagnosis. To this end, we propose a high-order Laplacian regularized low-rank representation method for dementia diagnosis using block-wise missing multimodal data. The proposed method was evaluated on 805 subjects (with incomplete MRI, PET, and CSF data) from the real Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Experimental results suggest the effectiveness of our method in three tasks of brain disease classification, compared with the state-of-the-art methods.

A novel GNAS mutation in pseudohypoparathyroidism type 1a in a Chinese man presented with recurrent seizure: a case report.

BACKGROUND: Pseudohypoparathyroidism is a rare genetic disease characterized by hypocalcaemia and hyperphosphataemia due to the defect to the guanine nucleotide-binding protein alpha subunit (GNAS) gene. Patients with pseudoparathyroidism type 1a and 1c could manifest Albright's hereditary osteodystrophy and multiple hormone resistance including gonadotropin and thyroid stimulating hormone. CASE PRESENTATION: Here we report a Chinese man who presented with fatigue, recurrent seizure and Albright's hereditary osteodystrophy. His genetic study revealed a heterozygote mutation in the GNAS gene [NM_000516.4(GNAS): c2787_2788del (p.Val930AspfsTer12)]. After calcium and calcitriol supplement, his seizures achieved partially remission. CONCLUSIONS: We report a case of PHP1a or 1c with a novel frameshift mutation in GNAS gene in a patient presenting with AHO, as well as TSH and partial gonadotropin resistance. This mutation in this case has not been reported in literature and adds to the spectrum of genetic mutations related to PHP.

Research of Low-Rank Representation and Discriminant Correlation Analysis for Alzheimer's Disease Diagnosis.

As population aging is becoming more common worldwide, applying artificial intelligence into the diagnosis of Alzheimer's disease (AD) is critical to improve the diagnostic level in recent years. In early diagnosis of AD, the fusion of complementary information contained in multimodality data (e.g., magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF)) has obtained enormous achievement. Detecting Alzheimer's disease using multimodality data has two difficulties: (1) there exists noise information in multimodal data; (2) how to establish an effective mathematical model of the relationship between multimodal data? To this end, we proposed a method named LDF which is based on the combination of low-rank representation and discriminant correlation analysis (DCA) to fuse multimodal datasets. Specifically, the low-rank representation method is used to extract the latent features of the submodal data, so the noise information in the submodal data is removed. Then, discriminant correlation analysis is used to fuse the submodal data, so the complementary information can be fully utilized. The experimental results indicate the effectiveness of this method.

Effectiveness and safety of pulsatile intravenous insulin therapy for the improvement of respiratory quotient in Chinese patients with diabetes mellitus.

Pulsatile intravenous insulin therapy (PIVIT) is a means of imitating naturally occurring insulin pulses artificially. It is thought to improve carbohydrate metabolism, which can be assessed using the respiratory quotient (RQ). The aim of this present study was to assess the efficacy and safety of PIVIT for the improvement of RQ in Chinese patients with diabetes mellitus (DM). This 12-week, multi-center, prospective, randomized, open-label, parallel-group study involved 110 DM patients (both type 1 and type 2) whose RQ was <0.8. Of these, 53 patients formed the control group, in which standard anti-diabetic therapy was maintained, and 54 patients formed the treatment group, which underwent weekly PIVIT in addition to the administration of standard anti-diabetic therapy. RQ was evaluated monthly in control subjects, and before and after every PIVIT treatment in the treatment group. After weekly PIVIT for 12 weeks, the mean RQ increased from 0.70 to 0.90 in the treatment group, but did not change in the control group. The percentage of subjects reporting adverse events (AEs) was 31.5% (17/54) in the treatment group and 9.43% (5/53) in the control group (P=0.0053). The most frequently reported AE (by 12 subjects) was a gastroenteric reaction when these individuals were receiving 50% glucose during the PIVIT treatment. The majority of AEs were mild and did not interfere with the ongoing treatment. Thus, PIVIT can be viewed as tolerated and effective for the improvement of RQ in Chinese DM patients. This study was retrospectively registered with the Chinese Clinical Trial Registry (http://www.chictr.org.cn) on November 13th 2019 (registration no. ChiCTR1900027510).

Pioglitazone Improved Insulin Sensitivity and First Phase Insulin Secretion Among Obese and Lean People with Diabetes: A Multicenter Clamp Study.

INTRODUCTION: To investigate the effects of pioglitazone (PIO) on insulin resistance and first phase insulin secretion among obese and lean Chinese people with type 2 diabetes mellitus (T2DM). METHODS: Sixty-eight drug-naïve patients with T2DM were treated with PIO for 16 weeks. Before and after the treatment, insulin sensitivity was evaluated by the euglycemic hyperinsulinemic clamp test. Plasma insulin levels at 0, 3, 5, 7, and 10 min during intravenous glucose tolerance test were determined to calculate the first phase insulin secretion and pancreatic ß-cell function. Circulating adiponectin levels were quantified. RESULTS: In both the lean and the obese patients with T2DM, the reduction of HbA1c following the PIO treatment was more than 1% (P < 0.001) and glucose infusion rate, acute insulin response, glucose disposal index, and ß-cell glucose sensitivity increased significantly (P < 0.001). A multiple linear regression analysis showed that the improvements of first phase insulin secretion and insulin sensitivity were independently associated with the changes of HbA1c, but the change of first phase insulin secretion exhibited a higher correlation coefficient (R2 = 0.20, P = 0.001) than the change of insulin sensitivity did (R2 = 0.07, P = 0.040). The PIO treatment led to a significant increase in adiponectin levels only in the obese group (P < 0.05). CONCLUSION: A 16-week treatment of PIO significantly increased insulin sensitivity and ß-cell function in the lean group as well as in the obese group among Chinese T2DM patients, demonstrating that both lean and obese diabetic adults would profit from PIO. TRIAL REGISTRATION: The ChiCTR registry number is ChiCTR-OPC-17011571. FUNDING: Takeda Pharmaceutical Co. Ltd. and Pfizer Pharmaceutical Co. Ltd.

Semi-Supervised SVM With Extended Hidden Features.

Many traditional semi-supervised learning algorithms not only train on the labeled samples but also incorporate the unlabeled samples in the training sets through an automated labeling process such as manifold preserving. If some labeled samples are falsely labeled, the automated labeling process will generally propagate negative impact on the classifier in quite a serious manner. In order to avoid such an error propagating effect, the unlabeled samples should not be directly incorporated into the training sets during the automated labeling strategy. In this paper, a new semi-supervised support vector machine with extended hidden features (SSVM-EHF) is presented to address this issue. According to the maximum margin principle and the minimum integrated squared error between the probability distributions of the labeled and unlabeled samples, the dimensionality of the labeled and unlabeled samples is extended through an orthonormal transformation to generate the corresponding hidden features shared by the labeled and unlabeled samples. After doing so, the last step in the process of training of SSVM-EHF is done only on the labeled samples with their original and hidden features, and the unlabeled samples are no longer explicitly used. Experimental results confirm the effectiveness of the proposed method.

Obesity has an interactive effect with genetic variation in the activating transcription factor 6 gene on the risk of pre-diabetes in individuals of Chinese Han descent.

Endoplasmic reticulum (ER) stress is one of the contributing factors to the development of ß-cell failure in type 2 diabetes. ER stress response through ATF6 has been shown to play an important role in insulin resistance and pancreatic ß-cell function. We investigated whether genetic polymorphisms in ATF6 were associated with the risk of pre-diabetes in a Chinese Han population, and whether they had a synergistic effect with obesity. Our samples included 828 individuals who were diagnosed as pre-diabetic, and 620 controls. The minor allele A at rs2340721 was associated with increased risk for pre-diabetes(p = 0.013), and this association was still significant after adjusting for gender, age, body mass index (BMI), and waist-hip ratio(p' = 0.011). BMI, treated as a continuous variable, and rs2340721 had an interactive effect on pre-diabetic risk(p for interaction = 0.003, ß = 0.106). Carriers of GG at rs7522210 were also at a higher risk compared to non-carriers (OR = 1.390, 95%CI:1.206-1.818, p = 0.013, adjusted OR' = 1.516, 95%CI:1.101-2.006, p' = 0.006). GG homozygotes had increased fasting blood glucose (FBG) levels(GG vs CX: 5.6 ± 0.52 vs 5.5 ± 0.57 mmol/L, p = 0.016), lower insulin levels (0,30,120 minutes after glucose load) (p < 0.05), and reduced areas under the insulin curve than non-carriers(GG vs CX:67.3(44.2-102.3) vs 73.1(49.4-111.4), p = 0.014). rs10918270 was associated with FBG, and rs4657103 with 2 hour glucose levels after a 75 g glucose load. We also identified a haplotype of TTAG composed of rs4657103, rs2134697, rs2340721, and rs12079579, which was associated with pre-diabetes. The genetic variation in ATF6 is associated with pre-diabetes and has interactive effects with BMI on pre-diabetes in the Chinese Han population.

Common variants in PERK, JNK, BIP and XBP1 genes are associated with the risk of prediabetes or diabetes-related phenotypes in a Chinese population.

BACKGROUND: Prediabetes is an early stage of ß-cell dysfunction presenting as insulin resistance. Evidences suggest that endoplasmic reticulum (ER) stress is involved in the pathogenesis of type 2 diabetes mellitus and prediabetes. In a Chinese population with prediabetes, we investigated single nucleotide polymorphisms (SNPs) in the genes of PERK, JNK, XBP1, BIP and CHOP which encode molecular proteins involved in ER stress pathways. METHODS: Nine SNPs at the PERK, JNK, XBP1, BIP and CHOP loci were genotyped by mass spectrometry in 1 448 unrelated individuals. By using a 75 g oral glucose tolerance test (OGTT), 828 subjects were diagnosed as prediabetes and 620 subjects aged 55 years and over as normal controls based on WHO diagnostic criteria (1999) for diabetes mellitus. RESULTS: The allele C of SNP rs867529 at PERK locus was a risk factor for prediabetes, with the carriers of C allele genotype at a higher risk of prediabetes compared to non-carriers (OR = 1.279, 95% CI: 1.013-1.614, P = 0.039, after adjustment for age, sex and body mass index (BMI). The SNPs rs6750998 at PERK locus was associated with homeostasis model assessments of insulin resistance (HOMA-IR) (P = 0.019), and rs17037621 with BMI (P = 0.044). The allele G of SNP rs10986663 in BIP gene was associated with a decreased risk of prediabetes (OR = 0.699, 95% CI: 0.539-0.907, P = 0.007). The SNP rs2076431 in JNK gene was associated with fasting plasma glucose levels (P = 0.006) and waist-hip ratios (P = 0.019). The SNP rs2239815 in XBP1 gene was associated with 2-hour plasma glucose levels after 75 g oral glucose load (P = 0.048) in the observed population. CONCLUSION: Common variants at PERK and BIP loci contributed to the risk of prediabetes, and the genetic variations in JNK and XBP1 genes are associated with diabetes-related clinical parameters in this Chinese population.

A peptide mimicking VGLL4 function acts as a YAP antagonist therapy against gastric cancer.

The Hippo pathway has been implicated in suppressing tissue overgrowth and tumor formation by restricting the oncogenic activity of YAP. However, transcriptional regulators that inhibit YAP activity have not been well studied. Here, we uncover clinical importance for VGLL4 in gastric cancer suppression and find that VGLL4 directly competes with YAP for binding TEADs. Importantly, VGLL4's tandem Tondu domains are not only essential but also sufficient for its inhibitory activity toward YAP. A peptide mimicking this function of VGLL4 potently suppressed tumor growth in vitro and in vivo. These findings suggest that disruption of YAP-TEADs interaction by a VGLL4-mimicking peptide may be a promising therapeutic strategy against YAP-driven human cancers.

[Spontaneous pregnancy in a patient with lymphocytic hypophysitis].

Lymphocytic hypophysitis(LH) is a rare but increasingly recognized autoimmune endocrine condition that causes partial or total hypopituitarism and is often associated with peripartum young women. We here report a 28-year-old patient who had a spontaneous and uneventful pregnancy following LH that had been treated with transspenoidal surgery and followed by anti-inflammatory agent. The woman failed to lactate and developed frontal headaches 3 months after normal delivery of her first child 3 years ago. Lab test showed the reduced concentrations of thyroxine, estradial and cortisol, suggesting hypopituitarism. Magnetic resonance imaging of the brain with contrast was performed and showed a uniformly enhancing pituitary mass with elevated optic chiasm. She underwent transsphenoidal surgery and histological examination of the resected specimen was consistent with lymphocytic hypophysitis. Anti-inflamation was started with prednisolone 40 mg per day because of a recurrence of headache that had completely recovered after surgery and regularly withdrawn to a long term maintenance dose of 10 mg per day. Physiological thyroxine replacement therapy was maintained. Her menstruation was restored without sex hormone replacement after 3 months. Three years after surgery, she got pregnant spontaneously and had normal breastfeeding after delivery. LH did not recur during this peripartum.