Appendectomy and asthma: a search for an association in older subjects.

INTRODUCTION: Several risk factors found to be associated with postoperative complications and cancer surgery, which carry a significant morbidity risk to cancer patients. Therefore, prehabilitation is necessary to improve the functional capability and nutritional status of a patient prior to surgery, so that the patient can withstand any postoperative activity and associated deterioration. Thus, this study aims to assess the effectiveness of prehabilitation interventions on the functional status of patients with gastric and oesophageal cancer who underwent esophagectomy and gastrectomy. MATERIAL AND METHODS: An interventional study was carried out among oesophageal and gastric cancer patients who had undergone surgery at the National Cancer Institute of Malaysia. The prehabilitation process took a maximum of two weeks, depending on the patient's optimisation before surgery. The prehabilitation is based on functional capacity (ECOG performance status), muscle function (handgrip strength), cardio-respiratory function (peak flow meter) and nutritional status (calorie and protein). Postoperative outcomes are measured based on the length of hospital stay, complications, and Clavien-Dindo Classification. RESULTS: Thirty-one patients were recruited to undergo a prehabilitation intervention prior to gastrectomy (n=21) and esophagectomy (n=10). Demographically, most of the cancer patients were males (67.7%) with an ideal mean of BMI (23.5±6.0). Physically, the majority of them had physical class (ASA grade) Grade 2 (67.7%), ECOG performance status of 1 (61.3%) and SGA grade B (51.6%). The functional capacity and nutritional status showed a significant improvement after one week of prehabilitation interventions: peak expiratory flow meter (p<0.001), handgrip (p<0.001), ECOG performance (p<0.001), walking distance (p<0.001), incentive spirometry (p<0.001), total body calorie (p<0.001) and total body protein (p=0.004). However, those patients who required two weeks of prehabilitation for optimization showed only significant improvement in peak expiratory flow meter (p<0.001), handgrip (p<0.001), and incentive spirometry (p<0.001). Prehabilitation is significantly associated postoperatively with the length of hospital stay (p=0.028), complications (p=0.011) and Clavien-Dindo Classification (p=0.029). CONCLUSION: Prehabilitation interventions significantly increase the functional capacity and nutritional status of cancer patients preoperatively; concurrently reducing hospital stays and complications postoperatively. However, certain cancer patients might require over two weeks of prehabilitation to improve the patient's functional capacity and reduce complications postoperatively.

[The value of renal color Doppler ultrasound in evaluating the severity and prognosis of patients with acute organophosphorus pesticide poisoning complicated by acute kidney injury].

Objective: To analyze the value of renal color Doppler ultrasound examination and clinical indicators in evaluating the severity and prognosis of acute organophosphorus pesticide poisoning (AOPP) complicated by acute kidney injury (AKI) . Methods: In November 2019, 86 AOPP patients complicated by AKI who were admitted from May 2018 to May 2019 were selected as the observation group, and they were divided into AKI stage 1 group (n=37) , AKI stage 2 group (n=32) and AKI stage 3 group (n=17) . 40 healthy people were selected as the control group. The differences in power Doppler ultrasound (PDU) score, renal interlobular artery resistance index (RI) value and related clinical indicators of each group were measured and analyzed, and the correlations between the indicators were analyzed. At the same time, binary logistic regression was used to analyze the risk factors of death in AOPP patients complicated by AKI. Results: There were statistically significant differences in Acute Physiology and Chronic Health Evaluation (APACHE) Ⅱscore, mean arterial pressure (MAP) , serum creatinine (SCr) and the length of continuous renal replacement therapy (CRRT) between different groups (P<0.05) . Compared with the control group, the APACHE Ⅱscores and SCr of patients in the AKI stage 2 and resistance index AKI stage 3 groups increased, while the MAP decreased (P<0.05) . Compared with the control group, AKI stage 1 group and AKI stage 2 group, the PDU score of patients in the AKI stage 3 group was significantly decreased, and the renal interlobular artery RI value was significantly increased (P<0.05) . SCr was positively correlated with the RI value of renal interlobular arteries and CRRT days (r=0.435, 0.713, P<0.05) , and was negatively correlated with renal PDU score (r=-0.643, P<0.05) . The renal PDU score was negatively correlated with the RI value of renal interlobular arteries and CRRT days (r=-0.350, -0.556, P<0.01) . Binary logistic regression analysis showed that SCr (OR=1.017, 95%CI: 1.004-1.041) and APACHE Ⅱ score (OR=1.289, 95%CI: 1.019-1.827) were risk factors for death in patients with AOPP complicated by AKI (P<0.05) . Conclusion: Both PDU score and the RI value of renal interlobular artery can reflect the severity and stage of patients with AOPP complicated by AKI to a certain extent, but neither of them is a key factor affecting the death of patients.

Prediction of transporter family from protein sequence by support vector machine approach.

Transporters play key roles in cellular transport and metabolic processes, and in facilitating drug delivery and excretion. These proteins are classified into families based on the transporter classification (TC) system. Determination of the TC family of transporters facilitates the study of their cellular and pharmacological functions. Methods for predicting TC family without sequence alignments or clustering are particularly useful for studying novel transporters whose function cannot be determined by sequence similarity. This work explores the use of a machine learning method, support vector machines (SVMs), for predicting the family of transporters from their sequence without the use of sequence similarity. A total of 10,636 transporters in 13 TC subclasses, 1914 transporters in eight TC families, and 168,341 nontransporter proteins are used to train and test the SVM prediction system. Testing results by using a separate set of 4351 transporters and 83,151 nontransporter proteins show that the overall accuracy for predicting members of these TC subclasses and families is 83.4% and 88.0%, respectively, and that of nonmembers is 99.3% and 96.6%, respectively. The accuracies for predicting members and nonmembers of individual TC subclasses are in the range of 70.7-96.1% and 97.6-99.9%, respectively, and those of individual TC families are in the range of 60.6-97.1% and 91.5-99.4%, respectively. A further test by using 26,139 transmembrane proteins outside each of the 13 TC subclasses shows that 90.4-99.6% of these are correctly predicted. Our study suggests that the SVM is potentially useful for facilitating functional study of transporters irrespective of sequence similarity.

Predicting functional family of novel enzymes irrespective of sequence similarity: a statistical learning approach.

The function of a protein that has no sequence homolog of known function is difficult to assign on the basis of sequence similarity. The same problem may arise for homologous proteins of different functions if one is newly discovered and the other is the only known protein of similar sequence. It is desirable to explore methods that are not based on sequence similarity. One approach is to assign functional family of a protein to provide useful hint about its function. Several groups have employed a statistical learning method, support vector machines (SVMs), for predicting protein functional family directly from sequence irrespective of sequence similarity. These studies showed that SVM prediction accuracy is at a level useful for functional family assignment. But its capability for assignment of distantly related proteins and homologous proteins of different functions has not been critically and adequately assessed. Here SVM is tested for functional family assignment of two groups of enzymes. One consists of 50 enzymes that have no homolog of known function from PSI-BLAST search of protein databases. The other contains eight pairs of homologous enzymes of different families. SVM correctly assigns 72% of the enzymes in the first group and 62% of the enzyme pairs in the second group, suggesting that it is potentially useful for facilitating functional study of novel proteins. A web version of our software, SVMProt, is accessible at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi.

SVM-Prot: Web-based support vector machine software for functional classification of a protein from its primary sequence.

Prediction of protein function is of significance in studying biological processes. One approach for function prediction is to classify a protein into functional family. Support vector machine (SVM) is a useful method for such classification, which may involve proteins with diverse sequence distribution. We have developed a web-based software, SVMProt, for SVM classification of a protein into functional family from its primary sequence. SVMProt classification system is trained from representative proteins of a number of functional families and seed proteins of Pfam curated protein families. It currently covers 54 functional families and additional families will be added in the near future. The computed accuracy for protein family classification is found to be in the range of 69.1-99.6%. SVMProt shows a certain degree of capability for the classification of distantly related proteins and homologous proteins of different function and thus may be used as a protein function prediction tool that complements sequence alignment methods. SVMProt can be accessed at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi.

Assessment of approximate string matching in a biomedical text retrieval problem.

Text-based search is widely used for biomedical data mining and knowledge discovery. Character errors in literatures affect the accuracy of data mining. Methods for solving this problem are being explored. This work tests the usefulness of the Smith-Waterman algorithm with affine gap penalty as a method for biomedical literature retrieval. Names of medicinal herbs collected from herbal medicine literatures are matched with those from medicinal chemistry literatures by using this algorithm at different string identity levels (80-100%). The optimum performance is at string identity of 88%, at which the recall and precision are 96.9% and 97.3%, respectively. Our study suggests that the Smith-Waterman algorithm is useful for improving the success rate of biomedical text retrieval.

A computer method for validating traditional Chinese medicine herbal prescriptions.

Traditional Chinese medicine (TCM) has been widely practiced and is considered as an alternative to conventional medicine. TCM herbal prescriptions contain a mixture of herbs that collectively exert therapeutic actions and modulating effects. Traditionally defined herbal properties, related to the pharmacodynamic, pharmacokinetic and toxicological, as well as physicochemical properties of their principal ingredients, have been used as the basis for formulating TCM multi-herb prescriptions. These properties are used in this work to develop a computer program for predicting whether a multi-herb recipe is a valid TCM prescription. This program is based on a statistical learning method, support vector machine (SVM), and it is trained by using 575 well-known TCM prescriptions and 1961 non-TCM recipes generated by random combination of TCM herbs. Testing results by using 72 well-known TCM prescriptions and 5039 non-TCM recipes showed that 73.6% of the TCM prescriptions and 99.9% of non-TCM recipes are correctly classified by this system. A further test by using 48 TCM prescriptions published in recent years found that 68.7% of these are correctly classified. These accuracies are comparable to those of SVM classification of other biological systems. Our study indicates the potential of SVM for facilitating the analysis of TCM prescriptions.

Antimicrobial peptide LL-37 on surfaces presenting carboxylate anions.

Antimicrobial peptides (AMPs) are part of the immune system in a wide range of organisms. They generally carry positive charges under physiological conditions, allowing them to accumulate on the negatively charged bacterial membrane as the first step of bactericidal action. The concentration range of AMPs necessary for rapid killing of bacteria tested in vitro is much higher than levels found at epithelial surfaces and body fluids in vivo, and close to the a level that is toxic to the host cells. It is likely that AMPs in vivo are localized and act cooperatively to enhance antimicrobial activity, while the global concentration is low thus demonstrating low toxicity to host cells. Herein we employed well-defined mixed self-assembled monolayers (SAMs) to localize LL-37, one of the most studied AMPs, via electrostatic interactions. We systematically varied the surface density of LL-37, and found that the immobilized AMPs not only attracted bacteria Pseudomonas aeruginosa to the surface, but also killed nearly all bacteria when above a threshold density. More significantly, the AMPs displayed low toxicity to human corneal epithelial cells. The results indicated that localization of AMPs on suitable polyanion substrates facilitated the bactericidal activity while minimizing the cytotoxicity of AMPs.

Enzyme family classification by support vector machines.

One approach for facilitating protein function prediction is to classify proteins into functional families. Recent studies on the classification of G-protein coupled receptors and other proteins suggest that a statistical learning method, Support vector machines (SVM), may be potentially useful for protein classification into functional families. In this work, SVM is applied and tested on the classification of enzymes into functional families defined by the Enzyme Nomenclature Committee of IUBMB. SVM classification system for each family is trained from representative enzymes of that family and seed proteins of Pfam curated protein families. The classification accuracy for enzymes from 46 families and for non-enzymes is in the range of 50.0% to 95.7% and 79.0% to 100% respectively. The corresponding Matthews correlation coefficient is in the range of 54.1% to 96.1%. Moreover, 80.3% of the 8,291 correctly classified enzymes are uniquely classified into a specific enzyme family by using a scoring function, indicating that SVM may have certain level of unique prediction capability. Testing results also suggest that SVM in some cases is capable of classification of distantly related enzymes and homologous enzymes of different functions. Effort is being made to use a more comprehensive set of enzymes as training sets and to incorporate multi-class SVM classification systems to further enhance the unique prediction accuracy. Our results suggest the potential of SVM for enzyme family classification and for facilitating protein function prediction. Our software is accessible at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi.

Prediction of torsade-causing potential of drugs by support vector machine approach.

In an effort to facilitate drug discovery, computational methods for facilitating the prediction of various adverse drug reactions (ADRs) have been developed. So far, attention has not been sufficiently paid to the development of methods for the prediction of serious ADRs that occur less frequently. Some of these ADRs, such as torsade de pointes (TdP), are important issues in the approval of drugs for certain diseases. Thus there is a need to develop tools for facilitating the prediction of these ADRs. This work explores the use of a statistical learning method, support vector machine (SVM), for TdP prediction. TdP involves multiple mechanisms and SVM is a method suitable for such a problem. Our SVM classification system used a set of linear solvation energy relationship (LSER) descriptors and was optimized by leave-one-out cross validation procedure. Its prediction accuracy was evaluated by using an independent set of agents and by comparison with results obtained from other commonly used classification methods using the same dataset and optimization procedure. The accuracies for the SVM prediction of TdP-causing agents and non-TdP-causing agents are 97.4 and 84.6% respectively; one is substantially improved against and the other is comparable to the results obtained by other classification methods useful for multiple-mechanism prediction problems. This indicates the potential of SVM in facilitating the prediction of TdP-causing risk of small molecules and perhaps other ADRs that involve multiple mechanisms.

Protein function classification via support vector machine approach.

Support vector machine (SVM) is introduced as a method for the classification of proteins into functionally distinguished classes. Studies are conducted on a number of protein classes including RNA-binding proteins; protein homodimers, proteins responsible for drug absorption, proteins involved in drug distribution and excretion, and drug metabolizing enzymes. Testing accuracy for the classification of these protein classes is found to be in the range of 84-96%. This suggests the usefulness of SVM in the classification of protein functional classes and its potential application in protein function prediction.

Prediction of functional class of the SARS coronavirus proteins by a statistical learning method.

The complete genome of severe acute respiratory syndrome coronavirus (SARS-CoV) reveals the existence of putative proteins unique to SARS-CoV. Identification of their function facilitates a mechanistic understanding of SARS infection and drug development for its treatment. The sequence of the majority of these putative proteins has no significant similarity to those of known proteins, which complicates the task of using sequence analysis tools to probe their function. Support vector machines (SVM), useful for predicting the functional class of distantly related proteins, is employed to ascribe a possible functional class to SARS-CoV proteins. Testing results indicate that SVM is able to predict the functional class of 73% of the known SARS-CoV proteins with available sequences and 67% of 18 other novel viral proteins. A combination of the sequence comparison method BLAST and SVMProt can further improve the prediction accuracy of SMVProt such that the functional class of two additional SARS-CoV proteins is correctly predicted. Our study suggests that the SARS-CoV genome possibly contains a putative voltage-gated ion channel, structural proteins, a carbon-oxygen lyase, oxidoreductases acting on the CH-OH group of donors, and an ATP-binding cassette transporter. A web version of our software, SVMProt, is accessible at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi .

Prediction of functional class of novel viral proteins by a statistical learning method irrespective of sequence similarity.

The function of a substantial percentage of the putative protein-coding open reading frames (ORFs) in viral genomes is unknown. As their sequence is not similar to that of proteins of known function, the function of these ORFs cannot be assigned on the basis of sequence similarity. Methods complement or in combination with sequence similarity-based approaches are being explored. The web-based software SVMProt (http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi) to some extent assigns protein functional family irrespective of sequence similarity and has been found to be useful for studying distantly related proteins [Cai, C.Z., Han, L.Y., Ji, Z.L., Chen, X., Chen, Y.Z., 2003. SVM-Prot: web-based support vector machine software for functional classification of a protein from its primary sequence. Nucleic Acids Res. 31(13): 3692-3697]. Here 25 novel viral proteins are selected to test the capability of SVMProt for functional family assignment of viral proteins whose function cannot be confidently predicted on by sequence similarity methods at present. These proteins are without a sequence homolog in the Swissprot database, with its precise function provided in the literature, and not included in the training sets of SVMProt. The predicted functional classes of 72% of these proteins match the literature-described function, which is compared to the overall accuracy of 87% for SVMProt functional class assignment of 34582 proteins. This suggests that SVMProt to some extent is capable of functional class assignment irrespective of sequence similarity and it is potentially useful for facilitating functional study of novel viral proteins.

Prediction of functional class of novel bacterial proteins without the use of sequence similarity by a statistical learning method.

A substantial percentage of the putative protein-encoding open reading frames (ORFs) in bacterial genomes have no homolog of known function, and their function cannot be confidently assigned on the basis of sequence similarity. Methods not based on sequence similarity are needed and being developed. One method, SVMProt (http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi), predicts protein functional family irrespective of sequence similarity (Nucleic Acids Res. 2003;31:3692-3697). While it has been tested on a large number of proteins, its capability for non-homologous proteins has so far been evaluated for a relatively small number of proteins, and additional tests are needed to more fully assess SVMProt. In this work, 90 novel bacterial proteins (non-homologous to known proteins) are used to evaluate the capability of SVMProt. These proteins are such that none of their homologs are in the Swiss-Prot database, their functions not clearly described in the literature, and they themselves and their homologs are not included in the training sets of SVMProt. They represent proteins whose function cannot be confidently predicted by sequence similarity methods at present. The predicted functional class of 76.7% of each of these proteins shows various levels of consistency with the literature-described function, compared to the overall accuracy of 87% for the SVMProt functional class assignment of 34,582 proteins that have at least one homolog of known function. Our study suggests that SVMProt is capable of assigning functional class for novel bacterial proteins at a level not too much lower than that of sequence alignment methods for homologous proteins.