High-Precision and Real-Time Measurement of Water Isotope Ratios Based on a Mid-Infrared Optical Sensor.

A compact spectrometer based on a mid-infrared optical sensor has been developed for high-precision and real-time measurement of water isotope ratios. The instrument uses laser absorption spectroscopy and applies the weighted Kalman filtering method to determine water isotope ratios with high precision and fast time response. The precision of the measurements is 0.41‰ for δ18O and 0.29‰ for δ17O with a 1 s time. This is much faster than the standard running average technique, which takes over 90 s to achieve the same level of precision. The successful development of this compact mid-infrared optical sensor opens up new possibilities for its future applications in atmospheric and breath gas research.

CD3, CD8, IFN-γ, tumor and stroma inflammatory cells as prognostic indicators for surgically resected SCLC: evidences from a 10-year retrospective study and immunohistochemical analysis.

Current clinical guidelines limit surgical intervention to patients with cT1-2N0M0 small cell lung cancer (SCLC). Our objective was to reassess the role of surgery in SCLC management, and explore novel prognostic indicators for surgically resected SCLC. We reviewed all patients diagnosed with SCLC from January 2011 to April 2021 in our institution. Survival analysis was conducted using the Kaplan-Meier method, and independent prognostic factors were assessed through the Cox proportional hazard model. In addition, immunohistochemistry (IHC) staining was performed to evaluate the predictive value of selected indicators in the prognosis of surgically resected SCLC patients. In the study, 177 SCLC patients undergoing surgical resection were ultimately included. Both univariate and multivariate Cox analysis revealed that incomplete postoperative adjuvant therapy emerged as an independent risk factor for adverse prognosis (p < 0.001, HR 2.96). Survival analysis revealed significantly superior survival among pN0-1 patients compared to pN2 patients (p < 0.0001). No significant difference in postoperative survival was observed between pN1 and pN0 patients (p = 0.062). Patients with postoperative stable disease (SD) exhibited lower levels of tumor inflammatory cells (TIC) (p = 0.0047) and IFN-γ expression in both area and intensity (p < 0.0001 and 0.0091, respectively) compared to those with postoperative progressive disease (PD). Conversely, patients with postoperative SD showed elevated levels of stromal inflammatory cells (SIC) (p = 0.0453) and increased counts of CD3+ and CD8+ cells (p = 0.0262 and 0.0330, respectively). Survival analysis indicated that high levels of SIC, along with low levels of IFN-γ+ cell area within tumor tissue, may correlate positively with improved prognosis in surgically resected SCLC (p = 0.017 and 0.012, respectively). In conclusion, the present study revealed that the patients with pT1-2N1M0 staging were a potential subgroup of SCLC patients who may benefit from surgery. Complete postoperative adjuvant therapy remains an independent factor promoting a better prognosis for SCLC patients undergoing surgical resection. Moreover, CD3, CD8, IFN-γ, TIC, and SIC may serve as potential indicators for predicting the prognosis of surgically resected SCLC.

Interpreting the Mechanism of Active Ingredients in Polygonati Rhizoma in Treating Depression by Combining Systemic Pharmacology and In Vitro Experiments.

Polygonati Rhizoma (PR) has certain neuroprotective effects as a homology of medicine and food. In this study, systematic pharmacology, molecular docking, and in vitro experiments were integrated to verify the antidepressant active ingredients in PR and their mechanisms. A total of seven compounds in PR were found to be associated with 45 targets of depression. Preliminarily, DFV docking with cyclooxygenase 2 (COX2) showed good affinity. In vitro, DFV inhibited lipopolysaccharide (LPS)-induced inflammation of BV-2 cells, reversed amoeba-like morphological changes, and increased mitochondrial membrane potential. DFV reversed the malondialdehyde (MDA) overexpression and superoxide dismutase (SOD) expression inhibition in LPS-induced BV-2 cells and decreased interleukin-1ß (IL-1ß), tumor necrosis factor-α (TNF-α), and IL-6 mRNA expression levels in a dose-dependent manner. DFV inhibited both mRNA and protein expression levels of COX2 induced by LPS, and the activation of NACHT, LRR, and PYD domains-containing protein 3 (NLRP3) and caspase1 was suppressed, thus exerting an antidepressant effect. This study proves that DFV may be an important component basis for PR to play an antidepressant role.

Research progress on the pharmacological mechanism, in vivo metabolism and structural modification of Erianin.

Erianin is an important bibenzyl compound in dendrobium and has a wide spectrum of pharmacological properties. Since Erianin was discovered, abundant results have been achieved in the in vitro synthesis, structural modification, and pharmacological mechanism research. Researchers have developed a series of simple and efficient in vitro synthesis methods to improve the shortcomings of poor water solubility by replacing the chemical structure or coating it in nanomaterials. Erianin has a broad anti-tumor spectrum and significant anti-tumor effects. In addition, Erianin also has pharmacological actions like immune regulation, anti-inflammatory, and anti-angiogenesis. A comprehensive understanding of the synthesis, metabolism, structural modification, and pharmacological action pathways of Erianin is of great value for the utilization of Erianin. Therefore, this review conducts a relatively systematic look back at Erianin from the above four aspects, to give a reference for the evolvement and further appliance of Erianin.

The identification of a two-gene prognostic model based on cisplatin resistance-related ceRNA network in small cell lung cancer.

BACKGROUND: Small cell lung cancer (SCLC) is a very malignant tumor with rapid growth and early metastasis. Platinum-based chemo-resistance is the major issue for SCLC treatment failure. Identifying a new prognostic model will help to make an accurate treatment decision for SCLC patients. METHODS: Using the genomics of drug sensitivity in cancer (GDSC) database, we identified cisplatin resistance-related lncRNAs in SCLC cells. Based on the competing endogenous RNA (ceRNA) network, we identified the mRNAs correlated with the lncRNAs. Using Cox and LASSO regression analysis, a prognostic model was established. The survival prediction accuracy was evaluated by receiver operating characteristic (ROC) curve and Kaplan-Meier analysis. GSEA, GO, KEGG and CIBERSORT tools were used for functional enrichment and immune cells infiltration analysis. RESULTS: We first screened out 10 differentially expressed lncRNAs between cisplatin resistant and sensitive SCLC cells from GDSC database. Based on ceRNA network, 31 mRNAs were identified with a correlation with the 10 lncRNAs. Furthermore, two genes (LIMK2 and PI4K2B) were identified by Cox and LASSO regression analysis to construct a prognostic model. Kaplan-Meier analysis indicated that the high-risk group had a poor overall survival compared with the low-risk group. The predicted area under the ROC curve (AUC) was 0.853 in the training set, and the AUC was 0.671 in the validation set. In the meanwhile, the low expression of LIMK2 or the high expression of PI4K2B in SCLC tumors was also significantly associated with poor overall survival in both training and validation sets. Functional enrichment analysis showed that the low-risk group was enriched in the apoptosis pathway and high immune infiltration of T cells. Finally, an apoptosis-related gene Cathepsin D (CTSD) was identified to be up-regulated in the low-risk group, and its higher expression correlated with better overall survival in SCLC. CONCLUSION: We established a prognostic model and potential biomarkers (LIMK2, PI4K2B and CTSD), which could help to improve the risk stratification of SCLC patients.

Inhibitors against Two PDZ Domains of MDA-9 Suppressed Migration of Breast Cancer Cells.

Melanoma differentiation-associated gene 9 (MDA-9) is a small adaptor protein with tandem PDZ domains that promotes tumor progression and metastasis in various human cancers. However, it is difficult to develop drug-like small molecules with high affinity due to the narrow groove of the PDZ domains of MDA-9. Herein, we identified four novel hits targeting the PDZ1 and PDZ2 domains of MDA-9, namely PI1A, PI1B, PI2A, and PI2B, using a protein-observed nuclear magnetic resonance (NMR) fragment screening method. We also solved the crystal structure of the MDA-9 PDZ1 domain in complex with PI1B and characterized the binding poses of PDZ1-PI1A and PDZ2-PI2A, guided by transferred paramagnetic relaxation enhancement. The protein-ligand interaction modes were then cross-validated by the mutagenesis of the MDA-9 PDZ domains. Competitive fluorescence polarization experiments demonstrated that PI1A and PI2A blocked the binding of natural substrates to the PDZ1 and PDZ2 domains, respectively. Furthermore, these inhibitors exhibited low cellular toxicity, but suppressed the migration of MDA-MB-231 breast carcinoma cells, which recapitulated the phenotype of MDA-9 knockdown. Our work has paved the way for the development of potent inhibitors using structure-guided fragment ligation in the future.

Sequence-Based Prediction of Transmembrane Protein Crystallization Propensity.

Transmembrane proteins play a vital role in cell life activities. There are several techniques to determine transmembrane protein structures and X-ray crystallography is the primary methodology. However, due to the special properties of transmembrane proteins, it is still hard to determine their structures by X-ray crystallography technique. To reduce experimental consumption and improve experimental efficiency, it is of great significance to develop computational methods for predicting the crystallization propensity of transmembrane proteins. In this work, we proposed a sequence-based machine learning method, namely Prediction of TransMembrane protein Crystallization propensity (PTMC), to predict the propensity of transmembrane protein crystallization. First, we obtained several general sequence features and the specific encoded features of relative solvent accessibility and hydrophobicity. Second, feature selection was employed to filter out redundant and irrelevant features, and the optimal feature subset is composed of hydrophobicity, amino acid composition and relative solvent accessibility. Finally, we chose extreme gradient boosting by comparing with other several machine learning methods. Comparative results on the independent test set indicate that PTMC outperforms state-of-the-art sequence-based methods in terms of sensitivity, specificity, accuracy, Matthew's Correlation Coefficient (MCC) and Area Under the receiver operating characteristic Curve (AUC). In comparison with two competitors, Bcrystal and TMCrys, PTMC achieves an improvement by 0.132 and 0.179 for sensitivity, 0.014 and 0.127 for specificity, 0.037 and 0.192 for accuracy, 0.128 and 0.362 for MCC, and 0.027 and 0.125 for AUC, respectively.

BBPpred: Sequence-Based Prediction of Blood-Brain Barrier Peptides with Feature Representation Learning and Logistic Regression.

Blood-brain barrier peptides (BBPs) have a large range of biomedical applications since they can cross the blood-brain barrier based on different mechanisms. As experimental methods for the identification of BBPs are laborious and expensive, computational approaches are necessary to be developed for predicting BBPs. In this work, we describe a computational method, BBPpred (blood-brain barrier peptides prediction), that can efficiently identify BBPs using logistic regression. We investigate a wide variety of features from amino acid sequence information, and then a feature learning method is adopted to represent the informative features. To improve the prediction performance, seven informative features are selected for classification by eliminating redundant and irrelevant features. In addition, we specifically create two benchmark data sets (training and independent test), which contain a total of 119 BBPs from public databases and the literature. On the training data set, BBPpred shows promising performances with an AUC score of 0.8764 and an AUPR score of 0.8757 using the 10-fold cross-validation. We also test our new method on the independent test data set and obtain a favorable performance. We envision that BBPpred will be a useful tool for identifying, annotating, and characterizing BBPs. BBPpred is freely available at http://BBPpred.xialab.info.

Prediction of Neuropeptides from Sequence Information Using Ensemble Classifier and Hybrid Features.

As hormones in the endocrine system and neurotransmitters in the immune system, neuropeptides (NPs) provide many opportunities for the discovery of new drugs and targets for nervous system disorders. In spite of their importance in the hormonal regulations and immune responses, the bioinformatics predictor for the identification of NPs is lacking. In this study, we develop a predictor for the identification of NPs, named PredNeuroP, based on a two-layer stacking method. In this ensemble predictor, 45 models are introduced as base-learners by combining nine feature descriptors with five machine learning algorithms. Then, we select eight base-learners referring to the sum of accuracy and Pearson correlation coefficient of base-learner pairs on the first-layer learning. On the second-layer learning, the outputs of these advisable base-learners are imported into logistic regression classifier to train the final model, and the outputs are the final predicting results. The accuracy of PredNeuroP is 0.893 and 0.872 on the training and test data sets, respectively. The consistent performance on these data sets approves the practicability of our predictor. Therefore, we expect that PredNeuroP would provide an important advancement in the discovery of NPs as new drugs for the treatment of nervous system disorders. The data sets and Python code are available at https://github.com/xialab-ahu/PredNeuroP.

[Role of integrin-linked kinase in renal tubular epithelial-mesenchymal transition and the regulatory effect of urokinase on its expression in mice with obstructive nephropathy].

OBJECTIVE: To investigate the role of integrin-linked kinase (ILK) on renal tubular epithelial-mesenchymal transition and the regulatory effect of urokinase on LIK expression in mice with obstructive nephropathy. METHODS: Normal male mice were randomly divided into sham-operated group (n=20), unilateral ureteral obstruction (UUO) group (n=28), and UUO with urokinase treatment group (uPA, n=28), and UUO was induced surgically in the latter two groups. The mice were sacrificed on days l, 3, 7 and 14 after the surgery, and renal interstitial fibrosis (RIF) was graded according to the result of Masson staining. The expression of ILK in the renal tissues of the rats was examined by immunofluorescence staining and Western blotting, and the expression of E-cadherin was detected by immunohistochemistry. RT-PCR was used to examine the mRNA expressions of ILK, E-cadherin and alpha-smooth muscle actin (alpha-SMA). RESULTS: The expressions of ILK mRNA and protein were significantly increased in UUO group, but significantly decreased by treatment with uPA (P<0.05). The expression of alpha-SMA mRNA level was significantly increased, while E-cadherin decreased in mice with UUO on day 3 after the surgery. Treatment with uPA significantly inhibited such effects (P<0.05). CONCLUSION: ILK plays an important role in renal interstitial fibrosis by mediating epithelial-mesenchymal transition. Urokinase attenuates renal tubulointerstitial fibrosis in mice with UUO possibly by inhibiting ILK expression and preventing tubular epithelial-mesenchymal transition.

The first skull of the earliest giant panda.

Fossils of the giant panda Ailuropoda (Order Carnivora, Family Ursidae) are largely isolated teeth, mandibles, and a few rare skulls, known from the late Pliocene to late Pleistocene in China and Southeast Asia. Much of this material represents a Pleistocene chronospecies, Ailuropoda baconi, an animal larger than the living giant panda, Ailuropoda melanoleuca. The earliest certain record of Ailuropoda is the late Pliocene chronospecies, Ailuropoda microta, smaller than either A. baconi or A. melanoleuca, and previously known only from teeth and a few mandibles from karst caves in south China. Here, we report the discovery of the first skull of A. microta, establishing its cranial anatomy and demonstrating that the specialized cranial and dental adaptations of Ailuropoda for durophagous feeding behavior centered on bamboo were already evident in this late Pliocene species. The skull from Jinyin cave (Guangxi) and dental remains from other karst localities in southeastern China show that Ailuropoda microta occupied south China from approximately 2 to 2.4 Myr ago after a marked global climatic deterioration. Dental and basicranial anatomy indicate a less specialized morphology early in the history of the lineage and support derivation of the giant panda from the Miocene Asian ursid Ailurarctos.

[Combination therapy with losartan and fosinopril for early diabetic nephropathy].

OBJECTIVE: To observe the effects of combined use of losartan and fosinopril in the treatment of early diabetic nephropathy. METHODS: Fifty-seven patients with diabetic nephropathy were divided equally into group A with treatment with losartan (50 mg) and fosinopril (10 mg) daily, group B with daily losartan treatment (50-100 mg), and group C with fosinopril treatment at the daily dose of 10-20 mg. After the 6-month medication, the patients underwent examinations for changes in the mean arterial blood pressure (MABP), serum creatinine (SCr), blood urea nitrogen (BUN) and 24-h urine protein excretion. RESULTS: Losartan or fosinopril used alone or in combination significantly lowered MABP, 24-h urine protein excretion, SCr and BUN in the patients ( P < 0.05 or P < 0.01). In spite of the absence of significant differences between the patient groups, combination use of the two drugs produced the most obvious reduction of the parameters measured. CONCLUSION: Combined use of losartan and fosinopril may decrease MABP, 24-h urine protein excretion, SCr and BUN to a greater extent than the use of losartan or fosinopril alone.

[The investigation of clinical images of 88 hospital staff with SARS].

OBJECTIVE: To investigate the clinical thoracic images of hospital staff with SARS. METHODS: Thoracic images was analyzed in 88 medical workers with SARS, including 394 chest plain films and 9 CT scans. RESULTS: The typical signs of SARS are multifocal air-space consolidations, and progress over a day from small shadow to becoming generalized. CONCLUSION: Thoracic plain film was necessary for diagnosis of SARS, CT could detect the pulmonary abnormality of SARS early.