Efficacy and Safety of Sacral Neuromodulation by Converting Constant Frequency Stimulation Into Variable Frequency Stimulation in Patients With Detrusor Overactivity and Impaired Contractility: A Single-Center Prospective Study

OBJECTIVES

In patients with detrusor overactivity with impaired contractility (DOIC), it is difficult to relieve abnormal lower urinary tract symptoms during both storage and voiding using sacral neuromodulation (SNM) with constant frequency stimulation (CFS). We sought to evaluate the efficacy and safety of SNM using variable frequency stimulation (VFS) in patients with DOIC by comparing it with outcomes of SNM with CFS.

MATERIALS AND METHODS

Between September 2020 and May 2021, we prospectively enrolled 20 patients with DOIC, confirmed on urodynamic examination, and administered SNM with VFS. The patients were followed up and required to maintain voiding diaries and record scale scores of overactive bladder symptoms, psychology and quality of life, uroflowmetry, ultrasonic postvoid residual urine, and adverse events at baseline and during the CFS and VFS phases.

RESULTS

The average testing phase was 26.3 ± 4.1 days. Compared with baseline values, overactive bladder symptom, psychologic health questionnaire, and quality of life scores, in addition to voiding frequency, urgency incontinence frequency, daily catheterization volume of voiding diary, and ultrasonic postvoid residual (PVR) decreased significantly during both the CFS and VFS phases (p < 0.05). The average voided volume, functional bladder capacity, and maximum urine flow rate significantly increased during the CFS and VFS phases (p < 0.05). In the VFS phase, voiding frequency, urgency incontinence frequency, daily catheterization volume of voiding diary, and ultrasonic PVR further decreased (p < 0.05), whereas functional bladder capacity, maximum urine flow rate, quality of life score, overactive bladder symptom score, and psychologic health questionnaire score further improved compared with results obtained in the CFS phase (p < 0.05). In the VFS phase, the success rate of further improvement of symptoms was 85.0%, and no new complications were noted.

CONCLUSIONS

SNM that converts CFS into VFS may be an effective treatment option for patients with DOIC, exhibiting no increase in adverse events.

The extreme wet and large precipitation size increase carbon uptake in Eurasian meadow steppes: Evidence from natural and manipulated precipitation experiments

The distribution of seasonal precipitation would profoundly affect the dynamics of carbon fluxes in terrestrial ecosystems. However, little is known about the impacts of extreme precipitation and size events on ecosystem carbon cycle when compared to the effects of average precipitation amount. The study involved an analysis of carbon fluxes and water exchange using the eddy covariance and chamber based techniques during the growing seasons of 2015-2017 in Bayan, Mongolia and 2019-2021 in Hulunbuir, Inner Mongolia, respectively. The components of carbon fluxes and water exchange at each site were normalized to evaluate of relative response among carbon fluxes and water exchange. The investigation delved into the relationship between carbon fluxes and extreme precipitation over five gradients (control, dry spring, dry summer, wet spring and wet summer) in Hulunbuir meadow steppe and distinct four precipitation sizes (0.1-2, 2-5, 5-10, and 10-25 mm d-1) in Bayan meadow steppe. The wet spring and summer showed the greatest ecosystem respiration (ER) relative response values, 76.2% and 73.5%, respectively, while the dry spring (-16.7%) and dry summer (14.2%) showed the lowest values. Gross primary production (GPP) relative response improved with wet precipitation gradients, and declined with dry precipitation gradients in Hulunbuir meadow steppe. The least value in net ecosystem CO2 exchange (NEE) was found at 10-25 mm d-1 precipitation size in Bayan meadow steppe. Similarly, the ER and GPP increased with size of precipitation events. The structural equation models (SEM) satisfactorily fitted the data (χ2 = 43.03, d.f. = 11, p = 0.215), with interactive linkages among soil microclimate, water exchange and carbon fluxes components regulating NEE. Overall, this study highlighted the importance of extreme precipitation and event size in influencing ecosystem carbon exchange, which is decisive to further understand the carbon cycle in meadow steppes.

Undernutrition-induced substance metabolism and energy production disorders affected the structure and function of the pituitary gland in a pregnant sheep model

Introduction

Undernutrition spontaneously occurs in ewes during late gestation and the pituitary is an important hinge in the neurohumoral regulatory system. However, little is known about the effect of undernutrition on pituitary metabolism.

Methods

Here, 10 multiparous ewes were restricted to a 30% feeding level during late gestation to establish an undernutrition model while another 10 ewes were fed normally as controls. All the ewes were sacrificed, and pituitary samples were collected to perform transcriptome, metabolome, and quantitative real-time PCR analysis and investigate the metabolic changes.

Results

PCA and PLS-DA of total genes showed that undernutrition changed the total transcriptome profile of the pituitary gland, and 581 differentially expressed genes (DEGs) were identified between the two groups. Clusters of orthologous groups for eukaryotic complete genomes demonstrated that substance transport and metabolism, including lipids, carbohydrates, and amino acids, energy production and conversion, ribosomal structure and biogenesis, and the cytoskeleton were enriched by DEGs. Kyoto encyclopedia of genes and genomes pathway enrichment analysis displayed that the phagosome, intestinal immune network, and oxidative phosphorylation were enriched by DEGs. Further analysis found that undernutrition enhanced the lipid degradation and amino acid transport, repressing lipid synthesis and transport and amino acid degradation of the pituitary gland. Moreover, the general metabolic profiles and metabolic pathways were affected by undernutrition, repressing the 60S, 40S, 28S, and 39S subunits of the ribosomal structure for translation and myosin and actin synthesis for cytoskeleton. Undernutrition was found also to be implicated in the suppression of oxidative phosphorylation for energy production and conversion into a downregulation of genes related to T cell function and the immune response and an upregulation of genes involved in inflammatory reactions enriching phagosomes.

Discussion

This study comprehensively analyses the effect of undernutrition on the pituitary gland in a pregnant sheep model, which provides a foundation for further research into the mechanisms of undernutrition-caused hormone secretion and metabolic disorders.

Accurate prediction and key protein sequence feature identification of cyclins

Cyclin proteins are a group of proteins that activate the cell cycle by forming complexes with cyclin-dependent kinases. Identifying cyclins correctly can provide key clues to understanding the function of cyclins. However, due to the low similarity between cyclin protein sequences, the advancement of a machine learning-based approach to identify cycles is urgently needed. In this study, cyclin protein sequence features were extracted using the profile-based auto-cross covariance method. Then the features were ranked and selected with maximum relevance-maximum distance (MRMD) 1.0 and MRMD2.0. Finally, the prediction model was assessed through 10-fold cross-validation. The computational experiments showed that the best protein sequence features generated by MRMD1.0 could correctly predict 98.2% of cyclins using the random forest (RF) classifier, whereas seven-dimensional key protein sequence features identified with MRMD2.0 could correctly predict 96.1% of cyclins, which was superior to previous studies on the same dataset both in terms of dimensionality and performance comparisons. Therefore, our work provided a valuable tool for identifying cyclins. The model data can be downloaded from https://github.com/YUshunL/cyclin.

The molecular mechanism of propionate-regulating gluconeogenesis in bovine hepatocytes

OBJECTIVE

Cows that are nursing get around 80% of their glucose from liver gluconeogenesis. Propionate, a significant precursor of liver gluconeogenesis, can regulate the key genes involved in hepatic gluconeogenesis expression, but its precise effects on the activity of enzymes have not yet been fully elucidated. Therefore, the aim of this study was to investigate the effects of propionate on the activity, gene expression, and protein abundance of the key enzymes involved in the gluconeogenesis of dairy cow hepatocytes.

METHODS

The hepatocytes were cultured and treated with various concentrations of sodium propionate (0, 1.25, 2.50, 3.75, and 5.00 mM) for 12 h. Glucose content in the culture media was determined by an enzymatic coloring method. The activities of gluconeogenesis related enzymes were determined by enzyme linked immunosorbent assay kits, and the levels of gene expression and protein abundance of the enzymes were detected by real-time quantitative polymerase chain reaction and Western blot, respectively.

RESULTS

Propionate supplementation considerably increased the amount of glucose in the culture medium compared to the control (p<0.05); while there was no discernible difference among the various treatment concentrations (p>0.05). The activities of cytoplasmic phosphoenolpyruvate carboxylase (PEPCK1), mitochondrial phosphoenolpyruvate carboxylase (PEPCK2), pyruvate carboxylase (PC), and glucose-6-phosphatase (G6PC) were increased with the addition of 2.50 and 3.75 mM propionate; the gene expressions and protein abundances of PEPCK1, PEPCK2, PC, and G6PC were increased by 3.75 mM propionate addition.

CONCLUSION

Propionate encouraged glucose synthesis in bovine hepatocytes, and 3.75 mM propionate directly increased the activities, gene expressions and protein abundances of PC, PEPCK1, PEPCK2, and G6PC in bovine hepatocytes, providing a theoretical basis of propionate-regulating gluconeogenesis in bovine hepatocytes.

Multi-dimensional radiomics analysis to predict visceral pleural invasion in lung adenocarcinoma of ≤3 cm maximum diameter

AIM

To explore the value of radiomics analysis in preoperatively predicting visceral pleural invasion (VPI) of lung adenocarcinoma (LAC) with ≤3 cm maximum diameter and to compare the performance of two-dimensional (2D) and three-dimensional (3D) computed tomography (CT) radiomics models.

MATERIALS AND METHODS

A total of 391 LAC patients were enrolled retrospectively, of whom 142 were VPI (+) and 249 were VPI (-). Radiomics features were extracted from 2D and 3D regions of interest (ROIs) of tumours in CT images. 2D and 3D radiomics models were developed combining the optimal radiomics features by using the logistic regression machine-learning method and radiomics scores (rad-scores) were calculated. Nomograms were constructed by integrating independent risk factors and rad-scores. The performance of each model was evaluated by using the receiver operator characteristic (ROC) curve, decision curve analysis (DCA), clinical impact curve (CIC), and calculating the area under the curve (AUC).

RESULTS

There was no difference in the VPI prediction between 2D and 3D radiomics models (training group: 2D AUC=0.835, 3D AUC=0.836, p=0.896; validation group: 2D AUC=0.803, 3D AUC=0.794, p=0.567). The 2D and 3D nomograms performed similarly regarding discrimination (training group: 2D AUC=0.867, 3D AUC=0.862, p=0.409, validation group: 2D AUC=0.835, 3D AUC=0.827, p=0.558), and outperformed their corresponding radiomics models and the clinical model. DCA and CIC revealed that the 2D nomogram had slightly better clinical utility.

CONCLUSION

The 2D radiomics model has a similar discrimination capability compared with the 3D radiomics model. The 2D nomogram performs slightly better for individual VPI prediction in LAC.

Association between serum multi-protein biomarker profile and real-world disability in multiple sclerosis

Few studies examined blood biomarkers informative of patient-reported outcome (PRO) of disability in people with multiple sclerosis (MS). We examined the associations between serum multi-protein biomarker profiles and patient-reported MS disability. In this cross-sectional study (2017-2020), adults with diagnosis of MS (or precursors) from two independent clinic-based cohorts were divided into a training and test set. For predictors, we examined seven clinical factors (age at sample collection, sex, race/ethnicity, disease subtype, disease duration, disease-modifying therapy [DMT], and time interval between sample collection and closest PRO assessment) and 19 serum protein biomarkers potentially associated with MS disease activity endpoints identified from prior studies. We trained machine learning (ML) models (Least Absolute Shrinkage and Selection Operator regression [LASSO], Random Forest, Extreme Gradient Boosting, Support Vector Machines, stacking ensemble learning, and stacking classification) for predicting Patient Determined Disease Steps (PDDS) score as the primary endpoint and reported model performance using the held-out test set. The study included 431 participants (mean age 49 years, 81% women, 94% non-Hispanic White). For binary PDDS score, combined feature input of routine clinical factors and the 19 proteins consistently outperformed base models (comprising clinical features alone or clinical features plus one single protein at a time) in predicting severe (PDDS ≥ 4) versus mild/moderate (PDDS < 4) disability across multiple machine learning approaches, with LASSO achieving the best area under the curve (AUCPDDS = 0.91) and other metrics. For ordinal PDDS score, LASSO model comprising combined clinical factors and 19 proteins as feature input (R2PDDS = 0.31) again outperformed base models. The two best-performing LASSO models (i.e., binary and ordinal PDDS score) shared six clinical features (age, sex, race/ethnicity, disease subtype, disease duration, DMT efficacy) and nine proteins (cluster of differentiation 6, CUB-domain-containing protein 1, contactin-2, interleukin-12 subunit-beta, neurofilament light chain [NfL], protogenin, serpin family A member 9, tumor necrosis factor superfamily member 13B, versican). By comparison, LASSO models with clinical features plus one single protein at a time as feature input did not select either NfL or glial fibrillary acidic protein (GFAP) as a final feature. Forcing either NfL or GFAP as a single protein feature into models did not improve performance beyond clinical features alone. Stacking classification model using five functional pathways to represent multiple proteins as meta-features implicated those involved in neuroaxonal integrity as significant contributors to predictive performance. Thus, serum multi-protein biomarker profiles improve the prediction of real-world MS disability status beyond clinical profile alone or clinical profile plus single protein biomarker, reaching clinically actionable performance.

Does Epigallocatechin Gallate as a Radiation Protective Agent Reduce the Anti-Tumor Effect of Radiotherapy in Postoperative Breast Cancer Radiotherapy?

PURPOSE/OBJECTIVE(S)

Based on the previous encouraging results, we further explored whether EGCG would have a protective effect on potential tumor lesions, that is, reduce the efficacy of radiotherapy. We selected patients with stage III breast cancer with or without EGCG. The local control rate, distant metastasis rate, DFS and OS were compared between the two groups.

MATERIALS/METHODS

Patients with stage III breast cancer who were treated with EGCG and radiotherapy was selected from a phase II clinical study (ClinicalTrials.gov, No. NCT02580279). Each patient was matched with one control patient without EGCG From the medical database of our hospital matching for age and stage. The control group of stage-and age-matched patients was selected at random from the medical database of our hospital RESULTS: We identified 43 EGCG patients and 43 matched controls. The median age was 45 years (range: 26-67). Between the two groups, there were no obvious differences in the baseline demographic or clinical features. When compared to the placebo group, the mean radiation-induced dermatitis index (RIDI) in the EGCG group was substantially lower (2.56±1.14 vs 3.36±1.16 T = -3.232, P = 0.002). Repeated measures ANOVA indicated the significant differences in the RTOG score during the course of radiotherapy between the two groups (F = 9.611 P = 0.003). The patients mostly experienced RID two or three weeks after starting radiotherapy, although in the EGCG group, it appeared later (3.19±0.91 weeks) than it did in the placebo group (2.67±0.84 weeks), P = 0.008. The median follow-up for patients in the EGCG group at the time of data collection was 50.6 months with 95% confidence interval (CI) from 43.9 to 57.3. While it was 48.6 months (95% CI: 43.4-53.9) for patients in the control group. There was no significant difference in overall survival (OS), disease free-survival (DFS) and freedom from locoregional (LRF) and distant failure (DMF) (P > 0.05). At the data cut-off (December 2021), the 4-year DFS with EGCG was 71.4% compared to 65.4% with conventional therapy, and the 4-year OS was 77.0% compared to 80.3%.

CONCLUSION

The prophylactic use of EGCG solution reduced the RID score of stage III breast cancer patients without negatively impacting the therapeutic effect of radiotherapy on the tumor. EGCG is safe and feasible choice for RID for breast cancer during radiotherapy.

Hexavalent Ions Insertion in Garnet Li7 La3 Zr2 O12 Toward a Low Temperature Densification Reaction

Nowadays, solid electrolytes are considered the main alternative to conventional liquid electrolytes in lithium batteries. The fabrication of these materials is however limited by the strict synthesis conditions, requiring high temperatures which can negatively impact the final performances. Here, it is shown that a modification of garnet-based Li7 La3 Zr2 O12 (LLZO) and the incorporation of tellurium can accelerate the synthesis process by lowering the formation temperature of cubic LLZO at temperatures below 700 °C. Optimized synthesis at 750 °C showed a decrease in particle size and cell parameter for samples with higher amounts of Te and the evaluation of electrochemical performances reported for LLZO Te0.25 a value of ionic conductivity of 5,15×10-5  S cm-1 after hot-pressing at 700 °C, two orders of magnitude higher than commercial Al-LLZO undergoing the same working conditions, and the highest value at this densification temperature. Partial segregation of Te-rich phases occurs for high-temperature densification. Our study shows the advantages of Te insertion on the sintering process of LLZO garnet and demonstrates the achievement of highly conductive LLZO with a low-temperature treatment.

The cardioprotective effect of Shengmai Zhenwu decoction on patients with chronic heart failure based on the levels of soluble interleukin 1 receptor-like 1

Objective

The purpose of this study was to evaluate the protective effect of Shengmai Zhenwu decoction on patients with chronic heart failure (CHF) based on the levels of soluble interleukin 1 receptor-like 1 (ST2).

Methods

We included a total of 80 outpatients and inpatients with CHF who were undergoing treatment at the Shanghai Municipal Hospital of Traditional Chinese Medicine between March 2020 and March 2022. We randomly divided them into the observation group (n = 40) and the control group (n = 40). Patients in the control group received treatments as per conventional Western medicine, while those in the observation group were treated with the Shengmai Zhenwu decoction in conjunction with Western medicine for eight consecutive weeks. We then compared the pre- and post-treatment levels of ST2 and N-terminal pro-brain natriuretic peptide (NT-proBNP) of the patients in the two groups.

Results

There were no significant differences in the pre-treatment levels of ST2 and NT-proBNP indexes between the two groups (P > 0.05), while the post-treatment comparison between the two groups in terms of ST2 and NT-proBNP levels suggested that the effect in the observation group was better, with statistical significance (P < 0.05).

Conclusion

Shengmai Zhenwu decoction was beneficial in patients with CHF, suggesting that it could be a promising and effective method for the treatment of CHF.

Effects of lactic acid bacteria-fermented formula milk supplementation on colonic microbiota and mucosal transcriptome profile of weaned piglets

Supplemental probiotic fermented milk as a gut modulator can improve growth performance for weaned piglets by promoting the development of the small intestine in digestion and immune function. The effect on colon health might also play a considerable part in the favourable role of probiotic fermented milk in the growth performance improvement of weaned piglets; however, it has yet to be reported. This study aimed to investigate the effects of supplementation with lactic acid bacteria-fermented formula milk (LFM) on colonic morphology, microbiota composition, and mucosal transcriptome profile in weaned piglets. A total of 24 male weaned piglets were randomly divided into two groups: a control (CON) treatment or the LFM-supplemented treatment. Each group consisted of six replicates (cages) with two piglets per cage, and each piglet in the LFM group was supplemented with 80 mL LFM three times a day for 21 d, while the CON group was treated with the same amount of drinking water. Results showed that supplementation of LFM reduced the colonic histological damage scores and significantly increased the number of goblet cells per crypt. Furthermore, LFM consumption decreased the levels of pro-inflammation cytokines in the colonic mucosa. LFM downregulated the expression of inflammatory genes (CXCL9 and CXCL10) involving Toll-like receptor signalling pathway, immune response, and response to bacterium, and up-regulated two active genes (S100A8 and S100A9) involving the IL-17 signalling pathway and Toll-like receptor 4 binding. In addition, LFM could increase the potential probiotic genera containing Lachnospira and Anaerorhabdus furcosa group, which were positively related to short-chain fatty acid (SCFA) production. Correspondingly, LFM-fed piglets had higher total bacterial load and total SCFA concentration in the colonic digesta compared with the CON group. These novel findings support the benefits of LFM in enhancing intestinal homoeostasis and ameliorating weaning stress for weaned piglets, which is associated with the modulation of gut microbiota composition and immune-related genes.

Growth assessment of pure ground-glass nodules on CT: comparison of density and size measurement methods

PURPOSE

To investigate the differences of size and density measurements in assessing pure ground-glass nodules (pGGNs) growth, and compare the growth rates and growth proportions of the two methods during follow-up period.

METHODS

Ninety patients with at least 3 consecutive thin-section chest CTs and confirmed 103 pGGNs on baseline CT were enrolled retrospectively. Using the two definitions of size and density to evaluate pGGNs growth with semi-automated segmentation. Then, the two methods were compared to assess differences in pGGNs growth.

RESULTS

For the size and density methods to assess nodule growth, 50.5% and 26.2% showed interval growth at the last CT (p < 0.001). Among the 19 nodules that grew in both size and density, the volume doubling time (VDT) of solid component (mean, 317.1; standard deviation, 224.8 days) was shorter than total VDT (median, 942.8; range, 400.1-2315.9 days) (p < 0.001). Of the 27 growth pGGNs assessed by the density method, the growth rates at years 1 and 2 were 25.9% and 63.0%, while the growth rates of 52 growing nodules assessed by size method were 11.5% and 48.1%, respectively. Twenty of 103 (19.4%) nodules were classified into category 4A lesions, and 7 (6.8%) were 4B lesions.

CONCLUSION

Compared to size measurements, observed density increases have a higher proportion of early growth and faster growth rates in growing nodules. Clinicians need to pay close attention to the nodules of new solid components and make timely decision management.

Treatment of berberine alleviates diabetic nephropathy by reducing iron overload and inhibiting oxidative stress

Diabetic nephropathy (DN) has become one of the major fatal factors in diabetic patients. The aim of this study was to elucidate the function and mechanism by which berberine exerts renoprotective effects in DN. In this work, we first demonstrated that urinary iron concentration, serum ferritin and hepcidin levels were increased and total antioxidant capacity was significantly decreased in DN rats, while these changes could be partially reversed by berberine treatment. Berberine treatment also alleviated DN-induced changes in the expression of proteins involved in iron transport or iron uptake. In addition, berberine treatment also partially blocked the expression of renal fibrosis markers induced by DN, including MMP2, MMP9, TIMP3, β-arrestin-1, and TGF-β1. In conclusion, the results of this study suggest that berberine may exert renoprotective effects by ameliorating iron overload and oxidative stress and reducing DN.

Farrerol Alleviates Hypoxic-Ischemic Encephalopathy by Inhibiting Ferroptosis in Neonatal Rats via the Nrf2 Pathway

Farrerol (FA) is a traditional Chinese herbal medicine known for its anti-inflammatory and anti-oxidative properties in various diseases. Ferroptosis is an iron-dependent oxidative stress-induced cell death. It is characterized by lipid peroxidation and glutathione depletion and is involved in neuronal injury. However, the role of FA in inhibiting ferroptosis in hypoxic-ischemic encephalopathy (HIE) and its underlying mechanisms are not yet completely elucidated. This study aimed to investigate whether FA could mediate ferroptosis and explore its function and molecular mechanism in HIE. A neonatal rat model of HIE was used, and rats were treated with FA, ML385 (a specific inhibitor of nuclear factor erythroid 2-related factor 2 [Nrf2]), or a combination of both. Neurological deficits, infarction volume, brain water content, pathological changes, and iron ion accumulation in the brain tissues were measured using the Zea-Longa scoring system and triphenyl tetrazolium chloride (TTC), hematoxylin-eosin (HE), and Perls' staining. The expression levels of GSH-Px, MDA, SOD, and ROS in brain tissues were also evaluated. Western blot analysis was performed to analyze the expression of the Nrf2 pathway and ferroptosis-related proteins. The results showed that FA administration significantly reduced neuronal damage, infarct volume, cerebral edema, and iron ion accumulation and inhibited MDA and ROS levels while promoting GSH-Px and SOD levels. FA also increased the expression levels of glutathione peroxidase 4 (GPX4), solute carrier family 7 member 11 (SLC7A11), Nrf2, and HO-1. Moreover, the combination of ML385 and FA in HIE abolished the FA protective effects. Therefore, the study concludes that FA exerts a neuroprotective effect after HIE by inhibiting oxidative stress and ferroptosis via the Nrf2 signaling pathway.

Selenium-binding protein 1 inhibits malignant progression and induces apoptosis via distinct mechanisms in non-small cell lung cancer

BACKGROUND

Selenium is an essential trace element in the human body. In epidemiological and clinical studies, Se supplementation significantly reduced the incidence of lung cancer in individuals with low baseline Se levels. The significant action of selenium is based on the selenium-containing protein as a mediator. Of note, the previous studies reported that the expression of selenium-binding protein 1 (SELENBP1) was obviously decreased in many human cancer tissues including non-small cell lung cancer (NSCLC). However, its roles in the origin and development of NSCLC are still unclear.

METHODS

The expression of SELENBP1 was measured by qRT-PCR, Western blotting and IHC in our collected clinical NSCLC tissues and cell lines. Next, the CCK-8, colony formation, wound-haeling, Millicell, Transwell, FCM assay, and in vivo xenograft model were performed to explore the function of SELENBP1 in NSCLC. The molecular mechanisms of SELENBP1 were investigated by Western blotting or IF assay.

RESULTS

We further identified that the expression of SELENBP1 was significantly decreased in NSCLC tissues in TCGA database and 45 out of 59 collected clinical NSCLC tissues compared with adjacent nontumor tissues, as well as in four NSCLC cell lines compared with normal lung cells. Particularly, we unexpectedly discovered that SELENBP1 was obviously expressed in alveolar type 2 (AT-II) cells for the first time. Then, a series of in vitro experiments uncovered that overexpression of SELENBP1 inhibited the proliferation, migration, and invasion of NSCLC cells, and induced cell apoptosis. Moreover, overexpression of SELENBP1 also inhibited growth and induced apoptosis of NSCLC cells in vivo. Mechanistically, we demonstrated that overexpression of SELENBP1 inhibited the malignant characteristics of NSCLC cells in part via inactivating the PI3K/AKT/mTOR signal pathway. Meanwhile, we found that overexpression of SELENBP1 inducing the apoptosis of NSCLC cells was associated with the activation of caspase-3 signaling pathway under nonhigh level of oxidative stress, but overexpression of SELENBP1 facilitating the cell apoptosis might be related to its combining with GPX1 and colocalizing in the nucleus under high level of oxidative stress.

CONCLUSIONS

Our findings highlighted that SELENBP1 was an important tumor suppressor during the origin and development of NSCLC. It may help to discover novel biomarkers or drug therapy targets for NSCLC.

Plasma tissue-type plasminogen activator is associated with lipoprotein(a) and clinical outcomes in hospitalized patients with COVID-19

Background

Patients with COVID-19 have a higher risk of thrombosis and thromboembolism, but the underlying mechanism(s) remain to be fully elucidated. In patients with COVID-19, high lipoprotein(a) (Lp(a)) is positively associated with the risk of ischemic heart disease. Lp(a), composed of an apoB-containing particle and apolipoprotein(a) (apo(a)), inhibits the key fibrinolytic enzyme, tissue-type plasminogen activator (tPA). However, whether the higher Lp(a) associates with lower tPA activity, the longitudinal changes of these parameters in hospitalized patients with COVID-19, and their correlation with clinical outcomes are unknown.

Objectives

To assess if Lp(a) associates with lower tPA activity in COVID-19 patients, and how in COVID-19 populations Lp(a) and tPA change post infection.

Methods

Endogenous tPA enzymatic activity, tPA or Lp(a) concentration were measured in plasma from hospitalized patients with and without COVID-19. The association between plasma tPA and adverse clinical outcomes was assessed.

Results

In hospitalized patients with COVID-19, we found lower tPA enzymatic activity and higher plasma Lp(a) than that in non-COVID-19 controls. During hospitalization, Lp(a) increased and tPA activity decreased, which associates with mortality. Among those who survived, Lp(a) decreased and tPA enzymatic activity increased during recovery. In patients with COVID-19, tPA activity is inversely correlated with tPA concentrations, thus, in another larger COVID-19 cohort, we utilized plasma tPA concentration as a surrogate to inversely reflect tPA activity. The tPA concentration was positively associated with death, disease severity, plasma inflammatory, and prothrombotic markers, and with length of hospitalization among those who were discharged.

Conclusion

High Lp(a) concentration provides a possible explanation for low endogenous tPA enzymatic activity, and poor clinical outcomes in patients with COVID-19.

Evaluation of ICUs and weight of quality control indicators: an exploratory study based on Chinese ICU quality data from 2015 to 2020

This study aimed to explore key quality control factors that affected the prognosis of intensive care unit (ICU) patients in Chinese mainland over six years (2015-2020). The data for this study were from 31 provincial and municipal hospitals (3425 hospital ICUs) and included 2 110 685 ICU patients, for a total of 27 607 376 ICU hospitalization days. We found that 15 initially established quality control indicators were good predictors of patient prognosis, including percentage of ICU patients out of all inpatients (%), percentage of ICU bed occupancy of total inpatient bed occupancy (%), percentage of all ICU inpatients with an APACHE II score ⩾15 (%), three-hour (surviving sepsis campaign) SSC bundle compliance (%), six-hour SSC bundle compliance (%), rate of microbe detection before antibiotics (%), percentage of drug deep venous thrombosis (DVT) prophylaxis (%), percentage of unplanned endotracheal extubations (%), percentage of patients reintubated within 48 hours (%), unplanned transfers to the ICU (%), 48-h ICU readmission rate (%), ventilator associated pneumonia (VAP) (per 1000 ventilator days), catheter related blood stream infection (CRBSI) (per 1000 catheter days), catheter-associated urinary tract infections (CAUTI) (per 1000 catheter days), in-hospital mortality (%). When exploratory factor analysis was applied, the 15 indicators were divided into 6 core elements that varied in weight regarding quality evaluation: nosocomial infection management (21.35%), compliance with the Surviving Sepsis Campaign guidelines (17.97%), ICU resources (17.46%), airway management (15.53%), prevention of deep-vein thrombosis (14.07%), and severity of patient condition (13.61%). Based on the different weights of the core elements associated with the 15 indicators, we developed an integrated quality scoring system defined as F score=21.35%xnosocomial infection management + 17.97%xcompliance with SSC guidelines + 17.46%×ICU resources + 15.53%×airway management + 14.07%×DVT prevention + 13.61%×severity of patient condition. This evidence-based quality scoring system will help in assessing the key elements of quality management and establish a foundation for further optimization of the quality control indicator system.

A nomogram for predicting the risk of Bronchopulmonary dysplasia in premature infants

Background

Bronchopulmonary dysplasia (BPD) is a prevalent and critical complication among premature infants, with potentially long-lasting adverse effetcs. The present study aimed to establish a nomogram model to predict the risk of BPD in premature infants born at <32 weeks gestational age.

Methods

A retrospective single-center study was conducted on premature infants admitted to the neonatal intensive care unit (NICU) of the Children's Hospital of Nanjing Medical University from January 2018 to December 2020. Data were collected from clinical medical records, including the perinatal data and the critical information after birth. Clinical parameters and features were analyzed using univariate and multivariate logistic regression. A nomogram based on clinical data was established and validated using bootstrapping samples. The specificity and sensitivity of the nomogram were estimated using the receiver operating characteristic (ROC) based area under the curve (AUC).

Results

A total of 542 premature babies were included, and 152 infants (28.04%) were diagnosed with BPD. Birth weight, cesarean delivery, invasive/non-invasive ventilation at day 7 and 14 were identified as significant factors (p < 0.05) using univariate and the multivariate logistic regression analysis, and were entered into a nomogram. The calibration curve for BPD probability demonstrated a favorable concurrence between actual probability and predicted ability of the BPD nomogram. The nomogram showed potential differentiation, with an AUC of 0.925, 89.90% sensitivity, 76.71% specificity, and 86.35% accuracy.

Conclusion

The nomogram developed in this study provides a straightforward tool to predict the probability of BPD and assist clinicians in optimizing treatment regimens for premature infants born at <32 weeks gestational age. This study highlights the importance of identifying and monitoring significant clinical factors associated with BPD in premature infants to improve clinical outcomes.

A First Computational Frame for Recognizing Heparin-Binding Protein

Heparin-binding protein (HBP) is a cationic antibacterial protein derived from multinuclear neutrophils and an important biomarker of infectious diseases. The correct identification of HBP is of great significance to the study of infectious diseases. This work provides the first HBP recognition framework based on machine learning to accurately identify HBP. By using four sequence descriptors, HBP and non-HBP samples were represented by discrete numbers. By inputting these features into a support vector machine (SVM) and random forest (RF) algorithm and comparing the prediction performances of these methods on training data and independent test data, it is found that the SVM-based classifier has the greatest potential to identify HBP. The model could produce an auROC of 0.981 ± 0.028 on training data using 10-fold cross-validation and an overall accuracy of 95.0% on independent test data. As the first model for HBP recognition, it will provide some help for infectious diseases and stimulate further research in related fields.

Operator Product Expansion Coefficients of the 3D Ising Criticality via Quantum Fuzzy Spheres

Conformal field theory (CFT) plays a crucial role in the study of various critical phenomena. While much attention has been paid to the critical exponents of different universalities, which correspond to the conformal dimensions of CFT primary fields, other important and intricate data such as operator product expansion (OPE) coefficients governing the fusion of two primary fields, have remained largely unexplored, especially in dimensions higher than 2D (or equivalently, 1+1D). Motivated by the recently proposed fuzzy sphere regularization, we investigate the operator content of 3D Ising criticality from a microscopic perspective. We first outline the procedure for extracting OPE coefficients on the fuzzy sphere and then compute 13 OPE coefficients of low-lying CFT primary fields. Our results are highly accurate and in agreement with the numerical conformal bootstrap data of 3D Ising CFT. Moreover, we were able to obtain 4 OPE coefficients, including f_{T_{μν}T_{ρη}ε}, which were previously unknown, thus demonstrating the superior capabilities of our scheme. Expanding the horizon of the fuzzy sphere regularization from the state perspective to the operator perspective opens up new avenues for exploring a wealth of new physics.

Differentially Expressed Circulating Long-Noncoding RNAS in Premature Infants with Respiratory Distress Syndrome

Purpose

Recent studies have addressed the association between lung development and long-noncoding RNAs (lncRNAs). But few studies have investigated the role of lncRNAs in neonatal respiratory distress syndrome (RDS). Thus, this study aimed to compare the expression profile of circulating lncRNAs between RDS infants and controls.

Methods

10 RDS infants and 5 controls were enrolled. RDS patients were further divided into mild and severe RDS subgroups. Blood samples were collected for the lncRNA expression profile. Subsequently, differentially expressed lncRNAs were screened out. Bioinformatics analysis was applied to establish a co-expression network of differential lncRNAs and mRNAs, and predict the underlying biological functions.

Results

A total of 135 differentially expressed lncRNAs were identified, including 108 upregulated and 27 downregulated lncRNAs (fold-change>2 and P<0.05) among the three groups (non-RDS, mild RDS and severe RDS groups). Of these lncRNAs, four were selected as showing higher fold changes and validated by qRT-PCR. ENST00000470527.1, ENST00000504497.1, ENST00000417781.5, and ENST00000440408.5 were increased not only in the plasma of total RDS patients but also in the severe RDS subgroup. Gene Ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) analyses showed that differentially expressed lncRNAs may play important roles in RDS through regulating PI3KAkt, RAS, MAPK, and TGF-β signaling pathways.

Conclusion

The present results found that ENST00000470527.1, ENST00000504497.1, ENST00000417781.5, and ENST00000440408.5 may be invol ved in RDS. This could provide new insight into research of the potential pathophysiological mechanisms of preterm RDS.

Vasoactive intestinal peptide exerts therapeutic action by regulating PTEN in a model of Sjögren's disease

INTRODUCTION

Sjögren's disease (SjD) is a chronic autoimmune disease characterized by the loss of the secretory function of the exocrine glands. At present, drugs that can both correct the immune imbalance and improve exocrine gland function are needed. Meanwhile, vasoactive intestinal peptide (VIP) has been reported as a candidate with anti-inflammatory and immunoregulatory properties for treating autoimmune diseases.

METHODS

Nonobese diabetic (NOD) mice and the primary splenic lymphocyte cells (SPLCs) were used to construct the SS model. The therapeutic effects of VIP for SjD by evaluating water consumption, histopathology, T cell subsets, and related cytokines. RT-qPCR and Western blot analysis were used to identify the expression of the PTEN/PI3K/AKT pathway.

RESULTS

We found that VIP therapy in NOD mice could increase the expression of PTEN and VIP/VPAC1 receptor, as well as decrease the PI3K/AKT pathway. In vitro, the results showed that the PTEN knockdown decreased the Treg/Th17 ratio and enhanced the phosphorylated PI3K/AKT pathway, which were reversed with VIP treatment.

CONCLUSIONS

VIP exerts potential therapeutic action in SjD by upregulating PTEN through the PI3K/AKT pathway and Treg/Th17 cell balance.

Material Evaluation and Dynamic Powder Deposition Modeling of PEEK/CF Composite for Laser Powder Bed Fusion Process

Polymeric composites such as Poly-ether-ether-ketone (PEEK)/carbon fiber (CF) have been widely utilized due to outstanding performances such as high specific strength and specific modulus. The PEEK/CF components via powder bed fusion additive manufacturing usually show brittle fracture behaviors induced by their poor interfacial affinity and inner voids. These defects are strongly associated with powder packing quality upon deposition. The particle dynamic model has been widely employed to study the interactions of particle motions. Powder property, bulk material property, and interfacial features of composite powders are key factors in the particle dynamic model. In this work, an efficient and systematic material evaluation is developed for composite powders to investigate their deposition mechanism. The discrete element method is utilized to simulate the dynamic behaviors of PEEK/CF composite powders. The powder properties, bulk material properties, and interfacial features of powders are calibrated and justified by experimental measurement, numerical simulation, and design of experiments. The particle dynamic model can explain the powder flow behaviors and interactions. The experimental and simulation AOR results show a maximal deviation of 4.89%. It reveals that the addition of short CF particles can assist the flow of PEEK powders and improve the packing quality of the composite powders. The results show an experimental improvement of 31.3% and 55.2% for PEEK/CF_30wt% and PEEK/CF_50wt%, with a simulated improvement of 27.4% and 50.2% for corresponding composite powders.

Similarity-Driven Fine-Tuning Methods for Regularization Parameter Optimization in PET Image Reconstruction

We present an adaptive method for fine-tuning hyperparameters in edge-preserving regularization for PET image reconstruction. For edge-preserving regularization, in addition to the smoothing parameter that balances data fidelity and regularization, one or more control parameters are typically incorporated to adjust the sensitivity of edge preservation by modifying the shape of the penalty function. Although there have been efforts to develop automated methods for tuning the hyperparameters in regularized PET reconstruction, the majority of these methods primarily focus on the smoothing parameter. However, it is challenging to obtain high-quality images without appropriately selecting the control parameters that adjust the edge preservation sensitivity. In this work, we propose a method to precisely tune the hyperparameters, which are initially set with a fixed value for the entire image, either manually or using an automated approach. Our core strategy involves adaptively adjusting the control parameter at each pixel, taking into account the degree of patch similarities calculated from the previous iteration within the pixel's neighborhood that is being updated. This approach allows our new method to integrate with a wide range of existing parameter-tuning techniques for edge-preserving regularization. Experimental results demonstrate that our proposed method effectively enhances the overall reconstruction accuracy across multiple image quality metrics, including peak signal-to-noise ratio, structural similarity, visual information fidelity, mean absolute error, root-mean-square error, and mean percentage error.