Degradation-driven vegetation-soil-microbe interactions alter microbial carbon use efficiency in Moso bamboo forests.

Microbial carbon utilization efficiency (CUE) is a crucial indicator for evaluating the efficiency of soil carbon sequestration and transformation, which is applied to quantify the proportion of soil carbon extracted by microbes for anabolism (growth) and catabolism (respiration). Previous studies have shown that the degradation of Moso bamboo forests (Phyllostachys edulis) destroyed the aboveground bamboo structure, reduced vegetation carbon storage, and weakened ecosystem carbon sequestration capacity. Interestingly, soil organic carbon stocks are gradually increasing. However, the mechanism by which degradation-induced changes in soil and vegetation characteristics affect microbial CUE and drive soil carbon sequestration remains unclear. Here we selected four stands with the same origin but different degradation years (intensive management, CK; 2 years' degradation, DM1; 6 years' degradation, DM2; and 10 years' degradation, DM3) based on the local management profiles. The principle of space-for-time substitution was used to investigate the changes in microbial CUE along a degradation time and to further identify the controlling biotic and abiotic factors. Our finding showed that microbial CUE increased by 12.27 %, 31.01 %, and 55.95 %, respectively, compared with CK; whereas microbial biomass turnover time decreased from 23.99 ± 1.11 to 17.16 ± 1.20 days. Promoting microbial growth was the main pathway to enhance microbial CUE. Massive inputs of vegetative carbon replenished soil carbon substrate content, and altered microbial communities and life history strategy, which in turn promoted microbial growth and increased microbial CUE. These findings provide theoretical support for the interactions between carbon dynamics and microbial physiology in degraded bamboo forests, and reinforce the importance of vegetation and microbial properties and soil carbon substrates in predicting microbial CUE.

Triglyceride-glycaemic index: Insights into predicting fetal macrosomia and its interaction with gestational diabetes mellitus: A cohort study of Chinese pregnant women.

OBJECTIVE: This study investigates the association between a new insulin resistance indicator, the triglyceride-glucose (TyG) index, and the risk of macrosomia. DESIGN: This is a prospective cohort study. METHODS: This study included 1332 women who delivered at Peking University International Hospital between October 2017 and August 2019. Participants were divided equally into three groups based on the TyG index. Logistic regression and restricted cubic spline (RCS) analyses were used to evaluate the relationship between the TyG index and macrosomia and conducted subgroup analyses. The TyG index's ability to predict macrosomia was assessed using the receiver operating characteristic (ROC) curve. RESULTS: Multivariable logistic regression analysis revealed that the TyG index is an independent risk factor for macrosomia (Odds ratio [OR] 1.84, 95% confidence interval [CI] 1.02-3.30, p < .05). RCS analysis indicates that the risk of macrosomia increases with the rise of the TyG index (p for nonlinearity <.001) when the TyG index is >6.53. Subgroup analysis showed a synergistic additive interaction between the TyG index and gestational diabetes mellitus (GDM) of macrosomia. The area under the ROC curve for the predictive model was 0.733 (95% CI 0.684, 0.781), with a sensitivity of 76.4% and specificity of 66.9%. Incorporating the TyG index alongside traditional risk factors notably enhances macrosomia prediction (p < .05). CONCLUSIONS: The TyG index independently predicts macrosomia, and exhibits an additive interaction with GDM in its occurrence. Integrating the TyG index with traditional risk factors improves the prediction of macrosomia. TRIAL REGISTRY: Clinical trials. gov [NCT02966405].

Med1 controls thymic T-cell migration into lymph node through enhancer-based Foxo1-Klf2 transcription program.

The migration is the key step for thymic T cells to enter circulation and then lymph nodes (LNs), essential for future immune surveillance. Although promoter-based transcriptional regulation through Foxo1, Klf2, Ccr7, and Sell regulates T-cell migration, it remains largely unexplored whether and how enhancers are involved in this process. Here we found that the conditional deletion of Med1, a component of the mediator complex and a mediator between enhancers and RNA polymerase II, caused a reduction of both CD4+ and CD8+ T cells in LNs, as well as a decrease of CD8+ T cells in the spleen. Importantly, Med1 deletion hindered the migration of thymic αßT cells into the circulation and then into LNs, accompanied by the downregulation of KLF2, CCR7, and CD62L. Mechanistically, Med1 promotes Klf2 transcription by facilitating Foxo1 binding to the Klf2 enhancer. Furthermore, forced expression of Klf2 rescued Ccr7 and Sell expression, as well as αßT-cell migration into LNs. Collectively, our study unveils a crucial role for Med1 in regulating the enhancer-based Foxo1-Klf2 transcriptional program and the migration of αßT cells into LNs, providing valuable insights into the molecular mechanisms underlying T-cell migration.

KF-Containing Interphase Formation Enables Better Potassium Ion Storage Capability.

Rechargeable potassium ion batteries have long been regarded as one alternative to conventional lithium ion batteries because of their resource sustainability and cost advantages. However, the compatibility between anodes and electrolytes remains to be resolved, impeding their commercial adoption. In this work, the K-ion storage properties of Bi nanoparticles encapsulated in N-doped carbon nanocomposites have been examined in two typical electrolyte solutions, which show a significant effect on potassium insertion/removal processes. In a KFSI-based electrolyte, the N-C@Bi nanocomposites exhibit a high specific capacity of 255.2 mAh g-1 at 0.5 A g-1, which remains at 245.6 mAh g-1 after 50 cycles, corresponding to a high capacity retention rate of 96.24%. In a KPF6-based electrolyte, the N-C@Bi nanocomposites show a specific capacity of 209.0 mAh g-1, which remains at 71.5 mAh g-1 after 50 cycles, corresponding to an inferior capacity retention rate of only 34.21%. Post-investigations reveal the formation of a KF interphase derived from salt decomposition and an intact rod-like morphology after cycling in K2 electrolytes, which are responsible for better K-ion storage properties.

Targeting a disintegrin and metalloprotease (ADAM) 17-CD122 axis enhances CD8+ T cell effector differentiation and anti-tumor immunity.

CD8+ T cell immune responses are regulated by multi-layer networks, while the post-translational regulation remains largely unknown. Transmembrane ectodomain shedding is an important post-translational process orchestrating receptor expression and signal transduction through proteolytic cleavage of membrane proteins. Here, by targeting the sheddase A Disintegrin and Metalloprotease (ADAM)17, we defined a post-translational regulatory mechanism mediated by the ectodomain shedding in CD8+ T cells. Transcriptomic and proteomic analysis revealed the involvement of post-translational regulation in CD8+ T cells. T cell-specific deletion of ADAM17 led to a dramatic increase in effector CD8+ T cell differentiation and enhanced cytolytic effects to eliminate pathogens and tumors. Mechanistically, ADAM17 regulated CD8+ T cells through cleavage of membrane CD122. ADAM17 inhibition led to elevated CD122 expression and enhanced response to IL-2 and IL-15 stimulation in both mouse and human CD8+ T cells. Intriguingly, inhibition of ADAM17 in CD8+ T cells improved the efficacy of chimeric antigen receptor (CAR) T cells in solid tumors. Our findings reveal a critical post-translational regulation in CD8+ T cells, providing a potential therapeutic strategy of targeting ADAM17 for effective anti-tumor immunity.

HCA-DAN: hierarchical class-aware domain adaptive network for gastric tumor segmentation in 3D CT images.

BACKGROUND: Accurate segmentation of gastric tumors from CT scans provides useful image information for guiding the diagnosis and treatment of gastric cancer. However, automated gastric tumor segmentation from 3D CT images faces several challenges. The large variation of anisotropic spatial resolution limits the ability of 3D convolutional neural networks (CNNs) to learn features from different views. The background texture of gastric tumor is complex, and its size, shape and intensity distribution are highly variable, which makes it more difficult for deep learning methods to capture the boundary. In particular, while multi-center datasets increase sample size and representation ability, they suffer from inter-center heterogeneity. METHODS: In this study, we propose a new cross-center 3D tumor segmentation method named Hierarchical Class-Aware Domain Adaptive Network (HCA-DAN), which includes a new 3D neural network that efficiently bridges an Anisotropic neural network and a Transformer (AsTr) for extracting multi-scale context features from the CT images with anisotropic resolution, and a hierarchical class-aware domain alignment (HCADA) module for adaptively aligning multi-scale context features across two domains by integrating a class attention map with class-specific information. We evaluate the proposed method on an in-house CT image dataset collected from four medical centers and validate its segmentation performance in both in-center and cross-center test scenarios. RESULTS: Our baseline segmentation network (i.e., AsTr) achieves best results compared to other 3D segmentation models, with a mean dice similarity coefficient (DSC) of 59.26%, 55.97%, 48.83% and 67.28% in four in-center test tasks, and with a DSC of 56.42%, 55.94%, 46.54% and 60.62% in four cross-center test tasks. In addition, the proposed cross-center segmentation network (i.e., HCA-DAN) obtains excellent results compared to other unsupervised domain adaptation methods, with a DSC of 58.36%, 56.72%, 49.25%, and 62.20% in four cross-center test tasks. CONCLUSIONS: Comprehensive experimental results demonstrate that the proposed method outperforms compared methods on this multi-center database and is promising for routine clinical workflows.

When covalent organic frameworks meet zeolites: Enhancing rhodamine B removal through the synergy in the emerging organic-inorganic nanoadsorbents.

The development of new porous materials has attracted intense attention as adsorbents for removing pollutants from wastewater. However, pure inorganic and organic porous materials confront various problems in purifying the wastewater. In this work, we integrated a covalent organic framework (TpPa-1) with an inorganic zeolite (TS-1) for the first time via a solvothermal method to fabricate new-type nanoadsorbents. The covalent organic framework/zeolite (TpPa-1/TS-1) nanoadsorbents combined the merits of the zeolite and COF components and possessed efficient adsorptive removal of organic contaminants from solution. Structural morphology and chemical composition characterization by powder X-ray diffraction, scanning electron microscopy, Fourier-transform infrared spectroscopy, X-ray photoelectron spectroscopy, transmission electron microscopy and thermogravimetric analysis demonstrated the successful preparation of TpPa-1/TS-1 composite nanoadsorbents. The resultant composite adsorbent TpPa-1/TS-1 removed rhodamine B at 1.7 and 2.6 times the efficiency of TpPa-1 and TS-1, respectively. Additional investigation revealed that the Freundlich adsorption isotherm and the pseudo-second-order kinetic model could be employed to represent the adsorption process more appropriately. Thermodynamic calculation analysis showed that the adsorption process proceeded spontaneously and exothermically. Besides, the effects of pH, absorbent mass and ionic strength on the adsorption performance were systematically investigated. The prepared composite adsorbent showed a slight decrease in removal efficiency after eight cycles of repeated use, and real water environment experiments also showed the high stability of the adsorbent. The enhanced performance can be attributed to electrostatic interaction, acid-base interaction, hydrogen bonding and π-π interactions.

Multiple serum anti-glutamate receptor antibody levels in clozapine-treated/naïve patients with treatment-resistant schizophrenia.

BACKGROUND: Glutamatergic function abnormalities have been implicated in the etiology of treatment-resistant schizophrenia (TRS), and the efficacy of clozapine may be attributed to its impact on the glutamate system. Recently, evidence has emerged suggesting the involvement of immune processes and increased prevalence of antineuronal antibodies in TRS. This current study aimed to investigate the levels of multiple anti-glutamate receptor antibodies in TRS and explore the effects of clozapine on these antibody levels. METHODS: Enzyme linked immunosorbent assay (ELISA) was used to measure and compare the levels of anti-glutamate receptor antibodies (NMDAR, AMPAR, mGlur3, mGluR5) in clozapine-treated TRS patients (TRS-C, n = 37), clozapine-naïve TRS patients (TRS-NC, n = 39), and non-TRS patients (nTRS, n = 35). Clinical symptom severity was assessed using the Positive and Negative Symptom Scale (PANSS), while cognitive function was evaluated using the MATRICS Consensus Cognitive Battery (MCCB). RESULT: The levels of all four glutamate receptor antibodies in TRS-NC were significantly higher than those in nTRS (p < 0.001) and in TRS-C (p < 0.001), and the antibody levels in TRS-C were comparable to those in nTRS. However, no significant associations were observed between antibody levels and symptom severity or cognitive function across all three groups after FDR correction. CONCLUSION: Our findings suggest that TRS may related to increased anti-glutamate receptor antibody levels and provide further evidence that glutamatergic dysfunction and immune processes may contribute to the pathogenesis of TRS. The impact of clozapine on anti-glutamate receptor antibody levels may be a pharmacological mechanism underlying its therapeutic effects.

Establishment of pregnancy-specific lipid reference intervals in pregnant women in a single-centre and assessment of the predictive value of early lipids for gestational diabetes mellitus: a prospective cohort study.

INTRODUCTION: This study was aimed at establishing a pregnancy-specific lipid reference interval (RI) in pregnant women in a single-centre in the Beijing area of China, simultaneously exploring the predictive value of lipid levels in early pregnancy for gestational diabetes mellitus (GDM). MATERIAL AND METHODS: From October 2017 to August 2019, Peking University International Hospital established records for 1588 pregnant women, whose lipid profiles were determined during the first and third trimesters. The Hoffmann technique was used to calculate gestation-specific lipid RI. The 95% reference range for gestational lipids was also estimated for 509 healthy pregnant women screened according to the Clinical and Laboratory Standards Institute guideline. Multivariate logistic regression analysis was used to calculate odds ratios (OR) and their 95% confidence interval (CI), and the receiver operating characteristic (ROC) curve was applied to assess the predictive value of lipids in the first trimester for the diagnosis of GDM. RESULTS: Total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) levels were significantly higher in the third trimester (p < 0.05). Hoffmann technique RI of the lipid profiles and the 95% reference range of the lipid profiles in healthy pregnant women did not differ statistically (p > 0.05). TC, TG, and LDL-C levels were higher in the GDM group in the first trimester (p < 0.05), and the risk of GDM was 2.1 times higher in women with higher TG (95% CI: 1.13-3.77, p < 0.05). The optimal ROC cut-off for TG to predict GDM was 2.375 mmol / L, and the area under the ROC curve was 0.622 (95% CI: 0.592-0.751), with a sensitivity of 73.7% and a specificity of 59.3%. CONCLUSIONS: This study established pregnancy-specific lipid RI for pregnant women in a single centre in the Beijing area of China. Pregnant women with TG ≥ 2.375 mmol/L in the first trimester were at significantly increased risk for GDM.

Prevalence and clinical correlates of suicide attempts in patients with first-episode drug-naïve major depressive disorder and comorbid autoimmune thyroiditis.

BACKGROUND: Autoimmune thyroiditis is closely associated with major depressive disorder (MDD) and suicide attempts. However, few studies have examined this relationship. AIMS: The study aimed to assess the prevalence and correlates of suicide attempts in patients with first-episode drug-naïve (FEDN) MDD and autoimmune thyroiditis. METHOD: We recruited 1718 out-patients with FEDN MDD and assessed depressive, anxiety and psychotic symptoms with the Hamilton Rating Scale for Depression, Hamilton Rating Scale for Anxiety (HRSA) and Positive and Negative Syndrome Subscale positive subscale, respectively. The serum levels of free triiodothyronine, free thyroxine, thyroid stimulating hormone (TSH), antithyroglobulin, thyroid peroxidase antibody (TPOAb) and several other metabolic parameters were assessed. Patients were divided into non-autoimmune thyroiditis, autoimmune thyroiditis only and autoimmune thyroiditis with abnormal TSH groups, based on autoimmune thyroiditis severity. Multiple logistic regression model was applied to identify the correlates of suicide attempts in patients with MDD and autoimmune thyroiditis with abnormal TSH. RESULTS: Compared with the non-autoimmune thyroiditis group, the autoimmune thyroiditis with abnormal TSH group had a nearly fourfold higher likelihood of reporting a suicide attempt, whereas no difference was found between the non-autoimmune thyroiditis and autoimmune thyroiditis only groups. HRSA score, lnTPOAb and lnTSH were independently associated with suicide attempts in patients with autoimmune thyroiditis with abnormal TSH. CONCLUSIONS: Patients with MDD and autoimmune thyroiditis with abnormal TSH are at higher risk for suicide attempt. TPOAb, TSH and anxiety are all independently associated with suicide attempts in this population, and regular thyroid checks are warranted.

Canopeo app as image-based phenotyping tool in controlled environment utilizing Arabidopsis mutants.

Canopeo app was developed as a simple, accurate, rapid, and free tool to analyze ground cover fraction (GCF) from red-green-blue (RGB) images and videos captured in the field. With increasing interest in tools for plant phenotyping in controlled environments, the usefulness of Canopeo to identify differences in growth among Arabidopsis thaliana mutants in a controlled environment were explored. A simple imaging system was used to compare Arabidopsis mutants based on the FLAVIN-BINDING, KELCH REPEAT, F-BOX-1 (FKF1) mutation, which has been identified with increased biomass accumulation. Two FKF1 lines such as null expression (fkf1-t) and overexpression (FKF1-OE) lines were used along with wild type (Col-0). Canopeo was used to phenotype plants, based on biomass estimations. Under long-day photoperiod, fkf1-t had increased cellulose biosynthesis, and therefore biomass. Resource partitioning favored seedling vigor and delayed onset of senescence. In contrast, FKF1-OE illustrated a determinative growth habit where plant resources are primarily allocated for seed production. This study demonstrates the use of Canopeo for model plants and highlights its potential for phenotyping broadleaved crops in controlled environments. The value of adapting Canopeo for lab use is those with limited experience and resources have access to phenotyping methodology that is simple, accessible, accurate, and cost-efficient in a controlled environment setting.

Relationship between bisphenol A and autoimmune thyroid disease in women of childbearing age.

Background: Autoimmune thyroid disease (AITD) is the main cause of hypothyroidism in women of childbearing age. Bisphenol A (BPA) is an environmental factor affecting AITD. This study aims to investigate relationship between BPA and AITD in women of childbearing age, thereby contributing novel evidence for the prevention of hypothyroidism in this specific demographic. Methods: A total of 155 women of childbearing age were enrolled in this study, including the euthyroid group comprised 60 women with euthyroidism and thyroid autoantibodies negativity and the AITD group consisted of 95 women with euthyroidism and at least one thyroid autoantibody positivity. The general information, thyroid function, thyroid autoantibodies, and thyroid ultrasound results of the two groups of women of childbearing age were recorded. Urinary BPA and urinary BPA/creatinine were detected. The difference of BPA levels between the two groups was compared. logistic regression was used to analyze the correlation between BPA and AITD. Results: The proportion of multiparous and serum thyroid stimulating hormone levels were significantly higher in the AITD group compared to the euthyroid group. Logistic regression analysis revealed that BPA levels did not exhibit a statistically significant association with AITD. Spearman correlation analysis revealed a statistically significant correlation between BPA and urinary iodine levels (r=0.30, P < 0.05), as well as a correlation between urinary BPA and free tetraiodothyronine (FT4) levels (r=0.29, P < 0.05). Conclusion: This study revealed a correlation between urinary BPA levels and FT4 levels. However, it did not establish a relationship between BPA and AITD in women of childbearing age.

Altered spontaneous neurological activity in methamphetamine use disorders and its association with cognitive function.

BACKGROUND: Methamphetamine (MA) is a widely used and detrimental drug, yet the precise mechanisms by which MA affects cognitive function remain unclear. This study aims to investigate the relationship between cognitive function and brain functional imaging in individuals with MA use disorder (MUD). METHODS: This study involved 45 patients diagnosed with MUD and 43 healthy controls (HC). Cognitive function assessment utilized the MATRICS Consensus Cognitive Battery, and functional data were acquired using a 3.0 Tesla magnetic resonance imaging scanner. RESULTS: The MUD group exhibited lower regional homogeneity (ReHo) values in the bilateral postcentral, the left superior temporal, and the left lingual regions compared to the HC group. Additionally, the MUD group displayed higher amplitude of low-frequency fluctuation (ALFF) values in the bilateral fusiform and the left putamen compared to the HC group, along with lower ALFF values in the bilateral postcentral cortices and the left middle cingulate cortex compared to the HC group (all p < 0.05, with false discovery rate corrected). Linear regression analysis revealed a positive correlation between the ReHo value in the right postcentral cortex and the neuropsychology assessment battery-mazes test (p = 0.014). Furthermore, the ALFF value in the left putamen showed negative correlations with the scores of the digit-symbol coding test (p = 0.027), continuous performance test (p = 0.037), and battery-mazes test (p = 0.024). CONCLUSION: Patients with MUD exhibit altered brain spontaneous neurological activities, and the intensity of spontaneous neurological activity in the left putamen is strongly associated with cognitive function.

Evaluation of the applicability of gasification coarse slag as a fine aggregate in controlled low-strength material: preparation, performance, and environmental effect.

Gasification slag (GS) is rich in SiO2, Al2O3, and Fe2O3, and has excellent particle size gradation, which has the potential to be employed as an aggregate in the field of controlled low-strength material (CLSM). Nevertheless, the large-scale application of GS as the fine aggregate for the preparation of CLSM has been scarcely investigated. In the present work, the applicability of replacing part of coal gangue (CG) with gasification coarse slag (GCS) as fine aggregate for the preparation of CLSM was investigated. The results revealed that using GCS as a fine aggregate improved the flowability of CLSM, and increasing the GCS content from 0 to 50 wt% improved the flowability from 250.0 to 280.0 mm. The 28-day compressive strength of all CLSM conformed to the requirements of ACI Committee 229. Compared to the Blank group, the 7- and 28-day compressive strength of the CLSM increased by 23.07% and 26.80%, respectively, at a GCS content of 50 wt%. The increase in compressive strength was mainly due to the pore-filling and hydration-promoting effect of the GCS, which made the structure denser. The dense structure reduced the expansion rate, absorption, and porosity rate of CLSM and increased the wet density. The optimal process parameter was the addition of 10 wt% of GCS. The results of heavy metal ion leaching showed that the optimal sample GS10 leached all heavy metal ions in much less than the limit values of GB 8978-1996 and GB 5085.3-2007. The results will provide new ideas and technical approaches for the large-scale application of GCS as the fine aggregate in CLSM.

Preparation of a novel CSM@ZIF-67 composite microsphere to facilitate Congo red adsorption from dyeing wastewater.

ABSTRACTChitosan (CS) is commonly used as an adsorbent for wastewater treatment because of its low cost, strong adsorption properties, and high availability of raw materials required for its production. However, CS exhibits limited adaptability to pH, poor mechanical properties, and high swelling in aqueous media; these limitations restrict its widespread use. To address these issues, herein, zeolitic imidazolate framework-67 (ZIF-67) is loaded onto crosslinked CS microspheres (CSM) to prepare CSM@ZIF-67, a composite adsorbent. Next, the CSM@ZIF-67 is applied to the treatment of Congo red (CR) dye, which is typically present in printing and dyeing wastewater. The results demonstrate that the in situ synthesis of metal-organic frameworks (MOFs) on CSM improve the dispersion of MOFs and preserve the morphology of the MOFs. The adsorption equilibrium of CSM@ZIF-67 is reached within 150 min, and its adsorption capacity is as high as 538.4 mg/g at a pH of 9 and temperature of 25 °C. The CR adsorption process is consistent with the pseudo-second-order kinetic and Langmuir isotherm models, thus revealing that chemisorption is the primary rate-limiting step, and the pollutants are adsorbed on the adsorbent surface in a monolayer. Experiments on material cycling and regeneration performance reveal that the removal efficiency of CSM@ZIF-67 remains above 90%, even after five rounds of adsorption. CSM@ZIF-67 has abundant functional groups and adsorption sites and can efficiently remove CR through mutual interactions between the metal coordination effect, π-π conjugation, hydrogen bonding, and electrostatic interactions.

Exploring 3-O-glycosylations of 20(R)-dammarane ginsenosides and the catalytic mechanism underlying the stereoselectivity with the combined assistance of AlphaFold 2 and molecular docking.

Glycosylation at C3-OH is the favorable modification for pharmaceutical activities and diversity expansion of 20(R)-dammarane ginsenosides. The 3-O-glycosylation, exclusively occurring in 20(R)-PPD ginsenosides, has never been achieved in 20(R)-PPT ginsenosides. Herein, 3-O-glycosylation of 20(R)-PPT enabled by a glycosyltransferase (GT) OsSGT2 was achieved with the combined assistance of AlphaFold 2 and molecular docking. Firstly, we combined AlphaFold2 algorithm and molecular docking to predict interactions between 20(R)-PPT and candidate GTs. A catalytically favorable binding geometry was thus identified in the OsSGT2-20(R)-PPT complex, suggesting OsSGT2 might act on 20(R)-PPT. The enzymatic assays demonstrated that OsSGT2 reacted with varied sugar donors to form 20(R)-PPT 3-O-glycosides, exhibiting donor promiscuity. Additionally, OsSGT2 displayed acceptor promiscuity, catalyzing 3-O-glucosylation of 20(R/S)-PPT, 20(R/S)-PPD and 20(R/S)-Rh1, respectively. Protein engineering on OsSGT2 was thus performed to probe its catalytic mechanism underlying its stereoselectivity. The W207A mutant preferred 20(S)-dammarane aglycons, while F395Q/A396G(QG) displayed a conversion enhancement towards both 20(R/S)-dammarane aglycons. The QG mutant was then used to synthesize 20(R)-PPT 3-O-glucoside, which displayed a moderate angiotensin-converting enzyme inhibitory effect with an IC50 of 27.5 ± 4.7 µM, superior to that of its 20(S)-epimer, with the combined assistance of target fishing and reverse docking. The water solubility of 20(R)-PPT 3-O-glucoside increased as well.

How to determine the optimal proximal fusion level for Scheuermann kyphosis.

OBJECTIVE: To determine optimal proximal fusion levels for instrumented spinal fusion for Scheuermann kyphosis. METHODS: We reviewed 86 patients (33 women) who underwent corrective instrumented spinal fusion for Scheuermann kyphosis. All patients had long-cassette upright lateral radiographs taken preoperatively, postoperatively, and at 2 years and the last follow-up. Demographic, radiographic, and surgical parameters were compared between patients with and without PJK. RESULTS: PJK occurred in 28 patients (32%). The mean maximum Cobb angle was 85.8° ± 11.7° preoperatively, 54.8° ± 14.2° postoperatively, and 59.7° ± 16.8° at the last follow-up. Age and sex did not differ between the PJK and non-PJK groups (P > 0.05). The preoperative curve characteristics, fusion levels, and corrective ratio were similar in both groups (P > 0.05). The maximal Cobb angle at 2 years and the last follow-up significantly differed between the 2 groups (P < 0.05). The proportion of patients with the uppermost instrumented vertebra (UIV) at or above the proximal end vertebra (PEV) was similar in both groups (P > 0.05). The proportion of patients with UIV at or above T2 was significantly greater in the non-PJK group (P < 0.05). PJK was significantly associated with a C7 plumb line (C7PL)-sacrum distance ≥ 50 mm (P < 0.05). CONCLUSION: PJK is the main cause of postoperative correction loss. Proper fusion-level selection can reduce PJK occurrence. We recommend having the UIV at T2 or above, especially when the C7PL-sacrum distance ≥ 50 mm.

Free triiodothyronine (FT3)-to-free thyroxine (FT4) ratio identified as a risk factor for gestational diabetes in euthyroid pregnant women: insights from a Chinese population cohort study.

Background: To explore the association between thyroid hormones and gestational diabetes mellitus in euthyroid pregnant women, with the aim of preventing the occurrence of gestational diabetes mellitus. Methods: In this prospective study, a total of 1222 euthyroid pregnant women in their first trimester were recruited at Peking University International Hospital between December 2017 and March 2019. These participants underwent an oral glucose tolerance test during the 24-28 weeks of gestation. Results: During early pregnancy, the gestational diabetes mellitus group displayed lower levels of free thyroxine when compared to the non-gestational diabetes mellitus group. Additionally, the ratio of free triiodothyronine to free thyroxine in the gestational diabetes mellitus group during early pregnancy was significantly higher (p<0.05). The ratio of free triiodothyronine to free thyroxine during early pregnancy showed a positive correlation with blood glucose levels at 0, 60, and 120 min both before and after glucose loading (all p<0.05). During early pregnancy, there was a negative relationship between free thyroxine levels and fasting blood glucose. The free triiodothyronine levels were positively correlated to blood glucose levels at 120 min following glucose loading (all p<0.05). Conclusion: The ratio of free triiodothyronine-to-free thyroxine is an independent risk factor for gestational diabetes mellitus and has the potential to be a predictor for gestational diabetes mellitus in euthyroid pregnant women.

USBDAN: Unsupervised Scale-aware and Boundary-aware Domain Adaptive Network for Gastric Tumor Segmentation.

Accurate segmentation of gastric tumors from computed tomography (CT) images provides useful image information for guiding the diagnosis and treatment of gastric cancer. Researchers typically collect datasets from multiple medical centers to increase sample size and representation, but this raises the issue of data heterogeneity. To this end, we propose a new cross-center 3D tumor segmentation method named unsupervised scale-aware and boundary-aware domain adaptive network (USBDAN), which includes a new 3D neural network that efficiently bridges an Anisotropic neural network and a Transformer (AsTr) for extracting multi-scale features from the CT images with anisotropic resolution, and a scale-aware and boundary-aware domain alignment (SaBaDA) module for adaptively aligning multi-scale features between two domains and enhancing tumor boundary drawing based on location-related information drawn from each sample across all domains. We evaluate the proposed method on an in-house CT image dataset collected from four medical centers. Our results demonstrate that the proposed method outperforms several state-of-the-art methods.