[A prospective study of position selection combined with autologous blood intrathoracic infusion in the treatment of postoperative persistent air leakage with an unexpanded lung].

We prospectively studied 17 patients with spontaneous pneumothorax or giant emphysematous bulla at Rizhao Hospital of Traditional Chinese Medicine from October 2020 to March 2022. All patients underwent thoracoscopic interventional therapy, had experienced continued air leakage for 3 days with closed thoracic drainage postoperatively, had an unexpanded lung on CT, and/or failed to intervention with position selection combined with intra-pleural thrombin injection(referred to as "position plus1.0"). They were all treated with position selection combined with autologous blood (100 ml) and thrombin (5 000 U) intra-pleural injection(referred to as "position plus 2.0").The success rate of the "position plus 2.0" intervention was 16/17, and the recurrence rate was 3/17. There were four cases of fever, four cases of pleural effusion, one case of empyema, and no other adverse reactions. This study has shown that the "position plus 2.0" intervention is safe, effective, and simple for patient with persistent air leakage failed to intervention with"position plus 1.0" after thoracoscopic treatment of pulmonary and pleural diseases related to bulla.

[Effectiveness and safety of medical glue assisted argon plasma coagulation union giant emphysematous bulla volume reduction via thoracoscope on the treatment of spontaneous pneumothorax with subpleural bullae].

Objective: To evaluate the effectiveness and safety of a new treatment protocol that combined "medical glue assisted argon plasma coagulation"(hereinafter called "APC plus") and "giant emphysematous bulla volume reduction"(hereinafter called "one thoracoscope plus one needle") via medical thoracoscopy on the spontaneous pneumothorax patients whose chest high resolution CT (HRCT) showed multiple subpleural bullae (SPB) and at least one SPB≥4 cm in diameter. Methods: A retrospective analysis was performed on the clinical data of 46 cases of spontaneous pneumothorax with multiple SPB(at least one SPB≥4 cm in diameter), 42 males and 4 females, aged from 31 to 79 (68.5±10.3) years,from June 2018 to December 2021 in Rizhao Hospital of Traditional Chinese Medicine. The time of air leakage discontinuance, the disappearance rate and reduction degree of target subpleural blebs one week after operation, the degree of reduction and the incidence of postoperative complications were observed. Two-year follow-up after operation was carried out to assess the recurrence rate and its short- and long-term complications. Results: Among the 46 patients, SPB disappeared or nearly disappeared in 39 cases (84.78%), decreased in number or reduced in volume in 5 cases (10.87%), and remained unchanged in 2 cases (4.35%) after the intervention of "APC Plus"; 40 patients stopped leaking within 1 week and 6 cases stopped leaking over a week. Eleven patients finished the 3-year follow-up, 13 finished 2-year follow-up and 6 finished 1-year follow-up, with only 1 relapse. No serious complications occurred in all these 46 patients. Conclusion: "APC plus" combining with "one thoracoscope plus one needle" is safe and effective in the treatment of pneumothorax patients with multiple subpleural bullae of varying sizes.

[Efficacy and safety of giant emphysematous bulla volume reduction via medical thoracoscope].

Objective: To evaluate the efficacy and safety of giant emphysematous bulla (GEB) volume reduction via medical thoracoscope. Methods: This was a prospective, single-arm study conducted between July 2018 and September 2020 in Ri Zhao Hospital of Traditional Chinese Medicine. Patients who met the inclusion criteria were treated with GEB volume reduction via medical thoracoscope and were followed up to evaluate the efficacy and safety of the technique. According to comparison of preoperative and postoperative chest CT results, the self-designed evaluation criteria of imaging efficacy were as follows: complete or nearly complete disappearance of GEB (GEB volume reduction ≥90%), significant reduction of GEB (75%≤GEB volume reduction<90%), reduction of GEB (50%≤GEB volume reduction<75%) and no change (GEB volume reduction<50%). Results: A total of 47 patients were included, among whom 43 were males, with an age M (Q1, Q3) of 63.0 (55.0, 67.0). The CT results showed complete or nearly complete disappearance of GEB in 43 patients, significant reduction of GEB in 3 patients and reduction of GEB in 1 patient before discharge. The degree of dyspnea improved significantly (P<0.05). Arterial partial pressure of carbon dioxide (PaCO2) decreased from (48.2±8.4)mmHg (1 mmHg=0.133 kPa) to (45.4±7.3)mmHg (P<0.05). The 6-minute walk test (6MWT) increased from (245.6±162.4)m to (283.5±152.2)m (P<0.05). Six-month postoperative follow-up was completed in 24 patients, and CT results showed that the efficacy of volume reduction was continuous compared with that before discharge. GEB was further reduced or even disappeared in 3 of the cases. Besides, the degree of dyspnea, 6MWT (384.4±148.2)m and PaCO2 (42.7±6.6)mmHg were improved significantly (P<0.05). The oxygenation index (356.86±61.21)mmHg was significantly higher than that before surgery (295.20±67.16)mmHg and before discharge (294.50±76.69)mmHg (P<0.05). No perioperative deaths occurred. Conclusions: GEB volume can be completely eliminated or significantly reduced by this innovative technique, while PaCO2, the degree of dyspnea and exercise endurance can be significantly improved after operation. The 6-month follow-up after surgery showed that the above benefits continued, and that the oxygenation index improved significantly.

[Efficacy and safety of position selection combined with intra-pleural thrombin injection in the treatment of postoperative persistent air leakage in bullous pulmonary-pleural diseases].

Objective: To explore the efficacy and safety of position selection in combination with intra-pleural thrombin injection in the treatment of persistent air leakage (PAL) after medical thoracoscopic treatment of bullous pulmonary-pleural diseases (e.g. spontaneous pneumothorax or giant emphysematous bulla). Methods: This was a prospective study conducted in Rizhao Hospital of Traditional Chinese Medicine from August 2018 to November 2020. Twenty patients(19 males,1 female) with a mean age of (62.3±8.1) years met the diagnostic criteria for PAL which was defined as the air leak persisted more than 3 days despite of the closed thoracic drainage after medical thoracoscopic treatment of bullous pulmonary-pleural diseases.They received the following treatment procedures (referred to as "position plus"):①Pleural cavity injection (50% glucose 20 ml+thrombin 5 000 U).②Changing the patient's position under continuous negative pressure suction to find the position causing the complete stop or significant reduction of air leakage, and keeping in the position for 24-48 hours.③If the PAL wasn't stopped 48 hours later, the procedures above would be repeated.The duration of air leakage after "position plus", times of pleural cavity injection, condition of lung re-expansion, recurrence of air leakage and complications during hospitalization were recorded. Descriptive statistics were used to summarize the results:¯x±s or M(P25, P75) for continuous variables; frequency and percentages for categoric variables. Results: A total of 20 patients were included. The average duration of air leakage after"position plus" was (1.32±0.97) days. The times of pleural cavity injection required were 1.0(1.0, 1.0).All the patients showed good lung re-expansion in review of imaging after PAL was stopped. One patient had recurrent air leakage during hospitalization. No serious complications occurred. Conclusion: The comprehensive "position plus" intervention method is effective, safe and easily operating for the treatment of PAL after medical thoracoscopic treatment of bullous pulmonary-pleural diseases.

MiR-1-3p suppresses cell proliferation and invasion and targets STC2 in gastric cancer.

OBJECTIVE: MiR-1 has been reported to act as an inhibitory microRNA in gastric cancer (GC). This study aimed to investigate the regulatory mechanism by which miR-1-3p blocks the progression of GC by targeting stanniocalcin 2 (STC2). PATIENTS AND METHODS: The expression level of miR-1-3p in GC was assessed via quantitative Real Time-Polymerase Chain Reaction (qRT-PCR). Expressions of STC2 were measured by qRT-PCR and Western blot analysis. Proliferation and invasion assays were detected by MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide) and transwell assays, respectively. Moreover, the dual-luciferase reporter assay was used to confirm the binding sites between miR-1-3p and STC2. RESULTS: MiR-1-3p was significantly down-regulated in GC. Moreover, abnormal expression of miR-1-3p was correlated with GC tumor size. Functionally, overexpression of miR-1-3p inhibited proliferation and invasion in GC by inhibiting stanniocalcin 2 (STC2) expressions. In contrast, STC2 was significantly up-regulated in GC. Furthermore, miR-1-3p negatively regulated STC2 expression in GC. The upregulation of STC2 weakened the inhibitory effect of miR-1-3p in GC. CONCLUSIONS: MiR-1-3p suppressed cell proliferation and invasion by targeting STC2 in GC, providing a novel therapeutic target for GC.

Fas signaling promotes motility and metastasis through epithelial-mesenchymal transition in gastrointestinal cancer.

Fas signaling was reported to participate in cell apoptosis. However, this pathway has also been shown to promote tumor cell motility, leading to the hypothesis that Fas signaling may induce epithelial-mesenchymal transition (EMT) to promote metastasis. The effects of Fas-ligand (FasL) treatment and inhibition of Fas signaling on colorectal and gastric cancer cells were tested using motility assay, immunofluorescence, RT-PCR and immunoblot analyses. Fas signaling downregulated epithelial markers, upregulated mesenchymal markers and promoted motility in gastrointestinal (GI) cancer cells. FasL treatment also increased the expression of EMT transcriptional factors in the nucleus and induced a spindle shape cell morphology in these cells. Knockdown of Snail or Twist expression significantly decreased FasL-induced motility. The ERK1/2 pathway was activated by Fas signaling and is required for FasL-induced EMT and motility. Moreover, oxaliplatin, a chemotherapeutic agent, induced EMT partly through Fas signaling. Evaluation of human GI clinical specimens showed that FasL expression increased whereas E-cadherin expression decreased during GI cancer progression. Both markers were significantly inversely correlated. Tissue samples with a non-EMT phenotype were mainly distributed in patients with early cancer stages, whereas samples with an EMT phenotype were mostly distributed in patients with advanced cancer stages. A non-EMT phenotype significantly correlated with better prognosis. Altogether, these data indicate that Fas signaling may induce EMT to promote tumor motility and metastasis in GI cancer in vivo and in vitro.

Prediction of RNA-binding proteins by voting systems.

It is important to identify which proteins can interact with RNA for the purpose of protein annotation, since interactions between RNA and proteins influence the structure of the ribosome and play important roles in gene expression. This paper tries to identify proteins that can interact with RNA using voting systems. Firstly through Weka, 34 learning algorithms are chosen for investigation. Then simple majority voting system (SMVS) is used for the prediction of RNA-binding proteins, achieving average ACC (overall prediction accuracy) value of 79.72% and MCC (Matthew's correlation coefficient) value of 59.77% for the independent testing dataset. Then mRMR (minimum redundancy maximum relevance) strategy is used, which is transferred into algorithm selection. In addition, the MCC value of each classifier is assigned to be the weight of the classifier's vote. As a result, best average MCC values are attained when 22 algorithms are selected and integrated through weighted votes, which are 64.70% for the independent testing dataset, and ACC value is 82.04% at this moment.

Support vector machines for predicting protein structural class.

BACKGROUND: We apply a new machine learning method, the so-called Support Vector Machine method, to predict the protein structural class. Support Vector Machine method is performed based on the database derived from SCOP, in which protein domains are classified based on known structures and the evolutionary relationships and the principles that govern their 3-D structure. RESULTS: High rates of both self-consistency and jackknife tests are obtained. The good results indicate that the structural class of a protein is considerably correlated with its amino acid composition. CONCLUSIONS: It is expected that the Support Vector Machine method and the elegant component-coupled method, also named as the covariant discrimination algorithm, if complemented with each other, can provide a powerful computational tool for predicting the structural classes of proteins.

Is it a paradox or misinterpretation?

The paradox recently raised by Wang and Yuan (Proteins 2000;38:165-175) in protein structural class prediction is actually a misinterpretation of the data reported in the literature. The Bayes decision rule, which was deemed by Wang and Yuan to be the most powerful method for predicting protein structural classes based on the amino acid composition, and applied by these investigators to derive the upper limit of prediction rate for structural classes, is actually completely the same as the component-coupled algorithm proposed by previous investigators (Chou et al., Proteins 1998;31:97-103). Owing to lack of a complete or near-complete training data set, the upper limit rate thus derived by these investigators might be both invalid and misleading. Clarification of these points will further stimulate investigation of this interesting area.

Artificial neural network model for predicting membrane protein types.

Membrane proteins can be classified among the following five types: (1) type I membrane protein. (2) type II membrane protein. (3) multipass transmembrane proteins. (4) lipid chain-anchored membrane proteins, and (5) GPI-anchored membrane proteins. T. Kohonen's self-organization model which is a typical neural network is applied for predicting the type of a given membrane protein based on its amino acid composition. As a result, the high rates of self-consistency (94.80%) and cross-validation (77.76%), and stronger fault-tolerant ability were obtained.

Using neural networks for prediction of subcellular location of prokaryotic and eukaryotic proteins.

T. Kohonen's self-organization model, a typical neural network model, was applied to predict the subcellular location of proteins from their amino acid composition. The Reinhardt and Hubbard database was used to examine the performance of the neural network method. The rates of correct prediction for the three possible subcellular location of prokaryotic proteins were 96.1% by the self-consistency test and 84.4% by the jackknife test. The rates of correct prediction for the four possible subcellular location of eukaryotic proteins were 95.6% by the self-consistency test and 70.6% by the jackknife test.

Support vector machines for prediction of protein subcellular location.

Support Vector Machine (SVM), which is one kind of learning machines, was applied to predict the subcellular location of proteins from their amino acid composition. In this research, the proteins are classified into the following 12 groups: (1) chloroplast, (2) cytoplasm, (3) cytoskeleton, (4) endoplasmic reticulum, (5) extracall, (6) Golgi apparatus, (7) lysosome, (8) mitochondria, (9) nucleus, (10) peroxisome, (11) plasma membrane, and (12) vacuole, which have covered almost all the organelles and subcellular compartments in an animal or plant cell. The examination for the self-consistency and the jackknife test of the SVMs method was tested for the three sets: 2022 proteins, 2161 proteins, and 2319 proteins. As a result, the correct rate of self-consistency and jackknife test reaches 91 and 82% for 2022 proteins, 89 and 75% for 2161 proteins, and 85 and 73% for 2319 proteins, respectively. Furthermore, the predicting rate was tested by the three independent testing datasets containing 2240 proteins, 2513 proteins, and 2591 proteins. The correct prediction rates reach 82, 75, and 73% for 2240 proteins, 2513 proteins, and 2591 proteins, respectively.

Artificial neural network method for predicting HIV protease cleavage sites in protein.

Knowledge of the polyprotein cleavage sites by HIV protease will refine our understanding of its specificity, and the information thus acquired will be useful for designing specific and efficient HIV protease inhibitors. The search for inhibitors of HIV protease will be greatly expedited if one can find an accurate, robust, and rapid method for predicting the cleavage sites in proteins by HIV protease. In this paper, Kohonen's self-organization model, which uses typical artificial neural networks, is applied to predict the cleavability of oligopeptides by proteases with multiple and extended specificity subsites. We selected HIV-1 protease as the subject of study. We chose 299 oligopeptides for the training set, and another 63 oligopeptides for the test set. Because of its high rate of correct prediction (58/63 = 92.06%) and stronger fault-tolerant ability, the neural network method should be a useful technique for finding effective inhibitors of HIV protease, which is one of the targets in designing potential drugs against AIDS. The principle of the artificial neural network method can also be applied to analyzing the specificity of any multisubsite enzyme.

Prediction of beta-turns.

Kohonen's self-organization model, a neural network model, is applied to predict the beta-turns in proteins. There are 455 beta-turn tetrapeptides and 3807 non-beta-turn tetrapeptides in the training database. The rates of correct prediction for the 110 beta-turn tetrapeptides and 30,229 non-beta-turn tetrapeptides in the testing database are 81.8% and 90.7%, respectively. The high quality of prediction of neural network model implies that the residue-coupled effect along a polypeptide chain is important for the formation of reversal turns, such as beta-turns, during the process of protein folding.

Artificial neural network method for predicting the specificity of GalNAc-transferase.

The specificity of GalNAc-transferase is consistent with the existence of an extended site composed of nine subsites, denoted by R4, R3, R2, R1, R0, R1', R2', R3', and R4', where the acceptor at R0 is either Ser or Thr to which the reducing monosaccharide is anchored. To predict whether a peptide will react with the enzyme to form a Ser- or Thr-conjugated glycopeptide, a neural network method--Kohonen's self-organization model is proposed in this paper. Three hundred five oligopeptides are chosen for the training site, with another 30 oligopeptides for the test set. Because of its high correct prediction rate (26/30 = 86.7%) and stronger fault-tolerant ability, it is expected that the neural network method can be used as a technique for predicting O-glycosylation and designing effective inhibitors of GalNAc-transferase. It might also be useful for targeting drugs to specific sites in the body and for enzyme replacement therapy for the treatment of genetic disorders.

The breast cancer gene product TSG101: a regulator of ubiquitination?

Sequence analysis is a powerful tool to obtain structural and functional information about genes and their products. Here we show that TSG101, a gene subjected to somatic mutations in breast cancer, contains an amino terminal domain that is a homologue of ubiquitin conjugating enzymes (UBCs) and not, as previously proposed, DNA-binding domains. As the UBC active site residue is replaced in the TSG101 sequence in a similar manner to several other members of the UBC family, we propose a role for TSG101 in regulating the ubiquitination of short-lived gene products.

[Quality assessment of the Chinese traditional drug weilingxian by artificial neural network].

The artificial neural network was used for the assessment of a group of Chinese traditional drug Weilingxian (the roots of Clematis). The results showed a successful rate of 100%. This approach has better fault-tolerance ability and grester speed of prediction.