Lotus leaf extract can attenuate salpingitis in laying hens by inhibiting apoptosis.

This study aimed to determine whether the lotus leaf extract (LLE) had the effect of treating salpingitis in laying hens. First, the salpingitis model was established by the method of bacterial infection. Differential genes between salpingitis and healthy laying hens were identified by transcriptome sequencing, and GO and KEGG enrichment analyses were performed. Groups of treatment of antibiotics and LLE were established to verify the feasibility of the lotus leaf extract in treating salpingitis. Furthermore, the active component and pharmacological effects of LLE were identified using the UPLC-Q-TOF-MS and network pharmacology technique. At last, the mechanism of LLE treating salpingitis was further evaluated by DF-1 cells infected with bacteria. The results showed that LLE significantly reduced the levels of TLR4 and IFN-γ (P < 0.05), accelerated the levels of IgA and IgG (P < 0.05), regulated the levels of SOD and MDA (P < 0.05) in laying hens with salpingitis. A total of 1,874 differential genes were obtained according to the transcriptome sequencing. It was revealed a significant role in cell cycle and apoptosis by enrichment analysis. In addition, among the 28 components identified by UPLC-Q-TOF-MS, 20 components acted on 58 genes, including CDK1, BIRC5, and CA2 for treating salpingitis. After bacterial infection, cells were damaged and unable to complete the normal progression of the cell cycle, leading to cell cycle arrest and further apoptosis formation. However, with the intervention of LLE, bacterial infection was resisted. The cells proliferation was extensively restored, and the expression of NO was increased. The addition of LLE significantly decreased cell apoptosis. The G1 phase increased, the S phase and the G2 phase decreased in the model group; after the intervention of LLE, the G1 phase gradually returned to the average level, and G2 and S phases increased. The mRNA expression levels of BIRC5, CDK1, and CA2 were consistent with the predicted results in network pharmacology. At the same time, the mRNA expression levels of Caspase-3 and Caspase-7 were reduced after added with LLE. The mRNA expression levels of TNF-α, TRADD, FADD, Caspase-8, Caspase-10, and Caspase-9 (P < 0.05), which would inhibit death receptor activation and decrease the apoptotic cascade, were upregulated after bacterial infection. However, the results in LLE groups were downregulated (P < 0.05). Meanwhile, the mRNA expression levels of BCL-2 in LLE groups were increased significantly compared with it in model group (P < 0.05). Notably, LLE administration inhibited apoptosis and regulated the cell cycle distribution in the salpingitis induced by bacterial infection. These results indicated that the LLE attenuated bacterial-induced salpingitis by modulating apoptosis and immune function in laying hens.

Effects of sulfated ß-glucan from Saccharomyces cerevisiae on growth performance, antioxidant ability, nonspecific immunity, and intestinal flora of the red swamp crayfish (Procambarus clarkii).

The aim of this study was to examine the combined effects of sulfated ß-Glucan from Saccharomyces cerevisiae (sGSC) on growth performance, antioxidant ability, nonspecific immunity, and intestinal flora of the red swamp crayfish (Procambarus clarkii). Four experimental diets (sGSC25, sGSC50, sGSC100 and sGSC200) with different levels of sGSC (0.025%, 0.05%, 0.1% and 0.2% in diet, respectively) were fed to juvenile crayfish (average weight: 2.5 ± 0.5 g) for 8 weeks. The control diet was given with 2000 mg/kg GSC (GSC200 group). The based control diet was given without sGSC or GSC (blank group). Each group had 3 parallel test pools, 20 crayfish were reared in each pool. At the end of the growth trial, adding dietary 0.025%-0.1% sGSC could significantly improve the growth performance, antioxidant capacity and immunity of crayfish. Compared with GSC, sGSC had a better effect at lower concentration. Higher concentration of sGSC (>0.1%) would cause some side effects. sGSC also could improve the structure of the intestinal flora and optimize the function of the flora. sGSC would increase the abundances of probiotics such as Hafnia and Acinetobacter, and decreases the abundances of maleficent bacteria such as Enterobacteriaceae. Higher concentration of sGSC (>0.1%) would increase the abundance of Aeromonas. To conclude, 0.025%-0.1% sGSC can be used as a supplement in crayfish feed to increase growth, immunity, and antioxidant capacity and improve the structure of intestinal flora. These results provided a theoretical basis for the application of sGSC instead of GSC in crayfish breeding. It will be necessary to further study the optimal concentration of sGSC in feed additives in different growth stages of crayfish in the future.

A novel magnesium-rich tricalcium aluminate for simultaneous removal of ammonium and phosphorus: Response surface methodology and mechanism investigation.

Coexisting ammonium (NH4+-N) and phosphate (PO43--P) in wastewater is one of the main causes of eutrophication, which poses severe risks to aquatic ecosystem and human health worldwide. Herein, magnesium-rich tricalcium aluminate (Mg/C3A), which was constructed by incorporating Mg into cement-based material C3A via solid-state reaction, was employed in the simultaneous removal of NH4+-N and PO43--P. Considering the wastewater with unbalanced N/P ratio and fluctuant pH, the effect of multiple factors (Mg/C3A dosage, pH, initial contaminant concentration, and temperature) on the removal of both ions were systematically investigated by employing response surface methodology technique. The results demonstrated that the impact order of the factors on the NH4+ removal by Mg/C3A was: temperature > Mg/C3A dosage > initial NH4+ concentration > pH > initial PO43- concentration; the impact order on the PO43- removal was: initial PO43- concentration > Mg/C3A dosage > temperature > pH > initial NH4+ concentration. The maximum removal amount of NH4+ (54.13 mg g-1) and PO43- (56.47 mg g-1) were obtained at: Mg/C3A dosage = 3 g L-1, initial NH4+ concentration = 160 mg L-1, initial PO43- concentration = 160 mg L-1, temperature = 308 K, and pH = 7. In addition, the possible interactive influence mechanisms were elucidated in depth. Mg2+ played a major role in the PO43- removal by forming struvite (MgNH4PO4·6H2O) and newberyite (MgHPO4·3H2O). OH- released from Mg/C3A hydration mainly contributed to NH4+ removal. This work showed that Mg-rich C3A is a promising candidate for simultaneous removal of NH4+ and PO43-, shedding light on practical water remediation.

Exploring the relationship between potato components and Maillard reaction derivative harmful products using multivariate statistical analysis.

The correlation between potato components and Maillard reaction derivative harmful products (MRDHPs) formation during heat-processing was assessed in nine commercial potato varieties in China. Principal component analysis (PCA) combined with canonical correlation analysis (CCA) approach was performed to explore their relationships. The variables contributing most to the PCA results were extracted for CCA, and the results indicated that several amino acids, including lysine, tryptophan, alanine, phenylalanine, aspartate, and glutamate, have significant impacts on acrylamide and ß-carboline heterocyclic amine formation. Moreover, 5-O-caffeoylquinic acid, 3-O-caffeoylquinic acid, α-solanine, and α-chaconine were also important factors associated with acrylamide and ß-carboline heterocyclic amine formation. Optimally using raw potato varieties based on the impacts of these factors can help control MRDHP formation during thermal processing. For the first time, such approach was applied, which may be a useful tool for discovering the correlation of food components and MRDHPs.

Efficacy of Banxia Xiexin decoction for chronic atrophic gastritis: A systematic review and meta-analysis.

BACKGROUND: Banxia Xiexin decoction (BXD), a classical formula of traditional Chinese medicine (TCM), has been wildly used for chronic atrophic gastritis (CAG) patients with the cold-heat complex syndrome in China, and achieved satisfied effects. However, the clinical effects of it remains unclear. PURPOSE: The purpose of this article is to evaluate the clinical efficacy and safety of BXD for CAG treatment. METHODS: We searched seven electronic databases including Ovid, Embase, PubMed, Cochrane Library, Wan-fang database, VIP (Chinese Scientific Journals Database) and CNKI (China National Knowledge Infrastructure) from their inception to September 21, 2020. We used Jadad scale and Cochrane Collaboration's risk of bias tool to make evaluation of methodological quality. Revman 5.3 statistical software was used for statistical processing to evaluate the clinical efficacy and safety of BXD. RESULTS: 26 randomized controlled trials (RCTs) totaling 1985 patients were identified for analysis. Meta-analysis showed that BXD treatment was more effective (RR 1.29; 95%CI 1.24, 1.35; P<0.00001) and safe (MD 0.33; 95%CI 0.18, 0.58; P = 0.0002) than Chinese patent medicine + western medicine. Furthermore, BXD had improvement on symptoms scores such as stomach distending pain, and belching. Besides, BXD was more effective in inhibiting Helicobacter Pylori (HP), improving HP-related inflammation, and relieving the degree of glandular atrophy, intestinal metaplasia (IM), and dysplasia of gastric mucosa (GM). CONCLUSIONS: The meta-analysis showed that BXD was more effective and safer for CAG patients than the control group. However, due to limitations of methodological quality and small sample size of the included studies, further standardized research of rigorous design should be needed.

Analysis and Recognition of Traditional Chinese Medicine Pulse Based on the Hilbert-Huang Transform and Random Forest in Patients with Coronary Heart Disease.

Objective. This research provides objective and quantitative parameters of the traditional Chinese medicine (TCM) pulse conditions for distinguishing between patients with the coronary heart disease (CHD) and normal people by using the proposed classification approach based on Hilbert-Huang transform (HHT) and random forest. Methods. The energy and the sample entropy features were extracted by applying the HHT to TCM pulse by treating these pulse signals as time series. By using the random forest classifier, the extracted two types of features and their combination were, respectively, used as input data to establish classification model. Results. Statistical results showed that there were significant differences in the pulse energy and sample entropy between the CHD group and the normal group. Moreover, the energy features, sample entropy features, and their combination were inputted as pulse feature vectors; the corresponding average recognition rates were 84%, 76.35%, and 90.21%, respectively. Conclusion. The proposed approach could be appropriately used to analyze pulses of patients with CHD, which can lay a foundation for research on objective and quantitative criteria on disease diagnosis or Zheng differentiation.

Analysis of the diagnostic consistency of Chinese medicine specialists in cardiovascular disease cases and syndrome identification based on the relevant feature for each label learning method.

OBJECTIVE: To analyze the diagnostic consistency of Chinese medicine (CM) specialists in patients with cardiovascular disease and to study syndrome classification and identification based on the multi-label learning method. METHODS: Using self-developed CM clinical scales to collect cases, inquiry information, complexity, tongue manifestation and pulse manifestation were assessed. The number of cases collected was 2,218. Firstly, each case was differentiated by two CM specialists according to the same diagnostic criteria. The consistency of the diagnosis based on Cohen's Kappa coefficient was analyzed. Secondly, take the same diagnosis syndromes of two specialists as the results of the cases. According to injury information in the CM scale "yes" or "no" was assigned "1" or "0", and according to the syndrome type in each case "yes" or "no" was assigned "1" or "0". CM information data on cardiovascular disease cases were established. We studied CM syndrome classification and identification based on the relevant feature for each label (REAL) learning method, and the diagnostic rate of the syndrome was studied using the REAL method when the number of features selected was 5, 10, 15, 20, 30, 50, 70, and 100, respectively. RESULTS: The syndromes with good diagnostic consistency were Heart (Xin)-qi deficiency, Heart-yang deficiency, Heart-yin deficiency, phlegm, stagnation of blood and stagnation of qi. Syndromes with poor diagnostic consistency were heart-blood deficiency and blood deficiency of Heart and Liver (Gan). The highest diagnostic rates using the REAL method were Heart-yang deficiency followed by Heart-qi deficiency. A different number of features, such as 5, 10, 15, 20, 30, 40, 50, 70, and 100, respectively, were selected and the diagnostic accuracy based on five features showed the highest diagnostic accuracy. The top five features which had a strong correlation with the syndromes were in accordance with the CM theory. CONCLUSIONS: CM syndrome differentiation is strongly subjective and it is difficult to obtain good diagnostic consistency. The REAL method fully considers the relationship between syndrome types and injury symptoms, and is suitable for the establishment of models for CM syndrome classification and identification. This method can probably provide the prerequisite for objectivity and standardization of CM differentiation.

Symptom clustering in chronic gastritis based on spectral clustering.

OBJECTIVE: Apply spectral clustering to analyze the patterns of symptoms in patients with chronic gastritis (CG). METHODS: Based on 919 CG subjects, we applied mutual information feature selection to choose the positively correlated symptoms with each pattern. Then, we used the Shi and Malik spectral clustering algorithm to select the top 20 correlated symptoms. RESULTS: We ascertained the results of six patterns. There were three categories for the pattern of accumulation of damp heat in the spleen-stomach (0.00332). There were six categories for the pattern of dampness obstructing the spleen-stomach (0.02466). There were two categories for the pattern of spleen-stomach Qi deficiency (0.013 89). There were three categories for the pattern of spleen-stomach deficiency cold (0.009 15). There were five categories for the pattern of liver-Qi stagnation (0.01910). There were four categories for the pattern of stagnant heat in the liver-stomach (0.00585). CONCLUSION: Most of the spectral clustering results of the symptoms of CG patterns were in accordance with clinical experience and Traditional Chinese Medicine theory. Most categories suggested the nature and/or location of the disease.

Objective Auscultation of TCM Based on Wavelet Packet Fractal Dimension and Support Vector Machine.

This study was conducted to illustrate that auscultation features based on the fractal dimension combined with wavelet packet transform (WPT) were conducive to the identification the pattern of syndromes of Traditional Chinese Medicine (TCM). The WPT and the fractal dimension were employed to extract features of auscultation signals of 137 patients with lung Qi-deficient pattern, 49 patients with lung Yin-deficient pattern, and 43 healthy subjects. With these features, the classification model was constructed based on multiclass support vector machine (SVM). When all auscultation signals were trained by SVM to decide the patterns of TCM syndromes, the overall recognition rate of model was 79.49%; when male and female auscultation signals were trained, respectively, to decide the patterns, the overall recognition rate of model reached 86.05%. The results showed that the methods proposed in this paper were effective to analyze auscultation signals, and the performance of model can be greatly improved when the distinction of gender was considered.

Deep learning based syndrome diagnosis of chronic gastritis.

In Traditional Chinese Medicine (TCM), most of the algorithms used to solve problems of syndrome diagnosis are superficial structure algorithms and not considering the cognitive perspective from the brain. However, in clinical practice, there is complex and nonlinear relationship between symptoms (signs) and syndrome. So we employed deep leaning and multilabel learning to construct the syndrome diagnostic model for chronic gastritis (CG) in TCM. The results showed that deep learning could improve the accuracy of syndrome recognition. Moreover, the studies will provide a reference for constructing syndrome diagnostic models and guide clinical practice.

Effect of Shengmai injection () on diaphragmatic contractility in doxorubicin-treated rats.

OBJECTIVE: To explore the diaphragmatic toxicity in doxorubicin (DOX)-treated rats and the related mechanisms, as well as the effects of Shengmai Injection (SMI, ) on the diaphragmatic dysfunction. METHODS: Thirty Sprague-Dawley male rats were randomly divided into three groups: control, DOX-treated and DOX+SMI treated groups. DOX was given to rats in DOX and DOX+SMI groups in 6 equal doses [2.5 mg/kg, intraperitoneal injection (i.p.)], on alternate days, over a period of 2 weeks for a cumulative dose of 15 mg/kg. SMI was given to DOX+SMI rats in 12 doses (3 mL/kg, i.p.) for a period of 2 weeks before the administration of DOX and 2 weeks during the administration of DOX. The rats in the control group received equal volume of normal saline. Subsequently, the twitch and tetanic characteristics and force-frequency relationships, and the malondialdehyde (MDA) levels and the superoxide dismutase (SOD) activities, as well as the mRNA content and proteins of inducible nitric oxide synthase (iNOS) were determined. RESULTS: The DOX-treated rats had decreased the peak twitch tension (Pt), maximal tetanic tension (P0) and force-frequency relationship as compared with the control rats (P<0.01), while the diaphragm contractility in rats treated with SMI were significantly higher than that in DOX-treated rats (P<0.01). The DOX-treated rats had increased MAD levels and decreased SOD activities (P<0.05), and SMI decreased the MDA levels and increased the SOD activities in DOX-treated rats (P<0.05). Ultrastructure of diaphragm in the DOX-treated rats revealed typical alterations including fracture of diaphragm fibers, and edema and degeneration of mitochondria; these changes were relieved by SMI treatment. The mRNA content and protein of iNOS in DOX-treated rats were remarkably higher than those in control rats (P<0.01), while SMI decreased the mRNA expression level of iNOS in DOX-treated rats (P<0.05). CONCLUSIONS: Lipid peroxidation is responsible for DOX-induced diaphragm toxicity. SMI protects diaphragm muscles and their function from DOX impairment, and these beneficial effects may be somehow correlated with the decrease in expression of iNOS and lipid peroxidation.

Statistical validation of Traditional Chinese Medicine syndrome postulates in the context of patients with cardiovascular disease.

OBJECTIVE: Traditional Chinese Medicine (TCM) has many postulates that explain the occurrence and co-occurrence of symptoms using syndrome factors such as yang deficiency and yin deficiency. A fundamental question is whether the syndrome factors have verifiable scientific content or are purely subjective notions. This analysis investigated the issue in the context of patients with cardiovascular disease (CVD). DESIGN: In the past, researchers have tried to show that TCM syndrome factors correspond to real entities by means of laboratory tests, with little success. An alternative approach, called latent tree analysis, has recently been proposed. The idea is to discover latent variables behind symptom variables by analyzing symptom data and comparing them with TCM syndrome factors. If there is a good match, then statistical evidence supports the validity of the relevant TCM postulates. This study used latent tree analysis. SETTING: TCM symptom data of 3021 patients with CVD were collected from the cardiology departments of four hospitals in Shanghai, China, between January 2008 and June 2010. RESULTS: Latent tree analysis of the data yielded a model with 34 latent variables. Many of them correspond to TCM syndrome factors. CONCLUSIONS: The results provide statistical evidence for the validity of TCM postulates in the context of patients with CVD; in other words, they show that TCM postulates are applicable to such patients. This finding is important because it is a precondition for the TCM treatment of those patients.

Application of multilabel learning using the relevant feature for each label in chronic gastritis syndrome diagnosis.

Background. In Traditional Chinese Medicine (TCM), most of the algorithms are used to solve problems of syndrome diagnosis that only focus on one syndrome, that is, single label learning. However, in clinical practice, patients may simultaneously have more than one syndrome, which has its own symptoms (signs). Methods. We employed a multilabel learning using the relevant feature for each label (REAL) algorithm to construct a syndrome diagnostic model for chronic gastritis (CG) in TCM. REAL combines feature selection methods to select the significant symptoms (signs) of CG. The method was tested on 919 patients using the standard scale. Results. The highest prediction accuracy was achieved when 20 features were selected. The features selected with the information gain were more consistent with the TCM theory. The lowest average accuracy was 54% using multi-label neural networks (BP-MLL), whereas the highest was 82% using REAL for constructing the diagnostic model. For coverage, hamming loss, and ranking loss, the values obtained using the REAL algorithm were the lowest at 0.160, 0.142, and 0.177, respectively. Conclusion. REAL extracts the relevant symptoms (signs) for each syndrome and improves its recognition accuracy. Moreover, the studies will provide a reference for constructing syndrome diagnostic models and guide clinical practice.

Study on intelligent syndrome differentiation in traditional Chinese medicine based on multiple information fusion methods.

Numerous researchers have taken the solid step forward towards the objectification research of Traditional Chinese Medicine (TCM) four diagnostic methods. However, it is deficient in studies on information fusion of the four diagnostic methods. We establish four-diagnosis syndrome differentiation model of TCM based on information fusion technology. The objective detection instruments of four-diagnostic method are applied to collect four-diagnosis objective information of 506 cases of clinical heart-system patients. Then multiple information fusion methods are adopted to establish recognition model of syndromes. The results of our experiments show that recognition rates of the six syndromes using multi-label learning is better than OCON artificial neural network and multiple support vector machine.

[Feature extraction and recognition of traditional Chinese medicine pulse based on hemodynamic principles].

In this paper, factors contributing to the formation of pulse wave were analyzed based on hemodynamic principles. It is considered that formation of pulse wave was related to its propagation and reflection characteristics. Propagation of the pulse wave was characterized by pulse wave velocity, and reflection of the pulse wave was characterized by reflection coefficient. Pulse wave velocity and reflection coefficient were proposed as the eigenvectors of pulse wave in pulse diagnosis of traditional Chinese medicine, and support vector machine (SVM) was used to recognize slippery pulse, stringy pulse and plain pulse. Pulse wave velocity and reflection coefficient of the slippery, stringy and plain pulses in healthy people were calculated in this study, and SVM with Gaussian radial basis function was used for classifying. Results showed that pulse wave velocity and reflection coefficient with physiological and pathological significance had advantages in distinguishing slippery pulse, stringy pulse and plain pulse, which offered a new idea for recognizing pulse condition.

Modelling of inquiry diagnosis for coronary heart disease in Traditional Chinese Medicine by using multi-label learning.

BACKGROUND: Coronary heart disease (CHD) is a common cardiovascular disease that is extremely harmful to humans. In Traditional Chinese Medicine (TCM), the diagnosis and treatment of CHD have a long history and ample experience. However, the non-standard inquiry information influences the diagnosis and treatment in TCM to a certain extent. In this paper, we study the standardization of inquiry information in the diagnosis of CHD and design a diagnostic model to provide methodological reference for the construction of quantization diagnosis for syndromes of CHD. In the diagnosis of CHD in TCM, there could be several patterns of syndromes for one patient, while the conventional single label data mining techniques could only build one model at a time. Here a novel multi-label learning (MLL) technique is explored to solve this problem. METHODS: Standardization scale on inquiry diagnosis for CHD in TCM is designed, and the inquiry diagnostic model is constructed based on collected data by the MLL techniques. In this study, one popular MLL algorithm, ML-kNN, is compared with other two MLL algorithms RankSVM and BPMLL as well as one commonly used single learning algorithm, k-nearest neighbour (kNN) algorithm. Furthermore the influence of symptom selection to the diagnostic model is investigated. After the symptoms are removed by their frequency from low to high; the diagnostic models are constructed on the remained symptom subsets. RESULTS: A total of 555 cases are collected for the modelling of inquiry diagnosis of CHD. The patients are diagnosed clinically by fusing inspection, pulse feeling, palpation and the standardized inquiry information. Models of six syndromes are constructed by ML-kNN, RankSVM, BPMLL and kNN, whose mean results of accuracy of diagnosis reach 77%, 71%, 75% and 74% respectively. After removing symptoms of low frequencies, the mean accuracy results of modelling by ML-kNN, RankSVM, BPMLL and kNN reach 78%, 73%, 75% and 76% when 52 symptoms are remained. CONCLUSIONS: The novel MLL techniques facilitate building standardized inquiry models in CHD diagnosis and show a practical approach to solve the problem of labelling multi-syndromes simultaneously.

[Development and evaluation of an inquiry scale for diagnosis of heart system syndromes in traditional Chinese medicine].

OBJECTIVE: To develop an inquiry scale for diagnosis of heart system syndromes, and to discuss the provisional standardization of the inquiry method in traditional Chinese medicine (TCM). METHODS: Based on scale-making method, Chinese medicine theory and literature searching, an inquiry scale for diagnosis of heart system syndromes in TCM was developed. Statistics method, frequency counting and Delphi method were used for analysis. The inquiry scale was revised and tested repeatedly to check the test reliability, internal consistency reliability, and content validity, etc. RESULTS: The inquiry scale for diagnosis of heart system syndromes mainly covered basic data, chief complaint, history of present illness (accompanying symptoms) and past history, with appendix of inspection and palpation information as well as diagnosis made according to traditional Chinese and Western medicine. Among them, general inquiries covered fever and chills, sweating, head-body and chest-belly symptoms, taste and diet, stool and urine, sleep, mood, and gynecologic symptoms, which were scaled in 8 dimensions. And 66 symptom variables were screened finally. The scale had a good content validity and its coefficient alpha was 0.82. For the results of test-retest reliability, the Kappa values of using the scale for diagnosis of heart-qi deficiency, heart-yang deficiency, turbid phlegm, and cold coagulation twice by the same doctor ranged from 0.74 to 1, showing that the consistency of the scale was relatively high. The Kappa values of evaluation of scorer reliability in diagnosis of heart-qi deficiency, heart-yang deficiency, and heart-yin deficiency were also high, which were 0.63, 0.72, 1 and 0.48 respectively. Other results of diagnosis had low-consistency or even no diagnostic agreement. CONCLUSION: The research on the scale for inquiry in TCM indicates that it is feasible for the standardization of inquiry scale for diagnosis of heart system syndromes in TCM, offering a reference for research on the inquiry scales for other systems.

Weighted-support vector machines for predicting membrane protein types based on pseudo-amino acid composition.

Membrane proteins are generally classified into the following five types: (1) type I membrane proteins, (2) type II membrane proteins, (3) multipass transmembrane proteins, (4) lipid chain-anchored membrane proteins and (5) GPI-anchored membrane proteins. Prediction of membrane protein types has become one of the growing hot topics in bioinformatics. Currently, we are facing two critical challenges in this area: first, how to take into account the extremely complicated sequence-order effects, and second, how to deal with the highly uneven sizes of the subsets in a training dataset. In this paper, stimulated by the concept of using the pseudo-amino acid composition to incorporate the sequence-order effects, the spectral analysis technique is introduced to represent the statistical sample of a protein. Based on such a framework, the weighted support vector machine (SVM) algorithm is applied. The new approach has remarkable power in dealing with the bias caused by the situation when one subset in the training dataset contains many more samples than the other. The new method is particularly useful when our focus is aimed at proteins belonging to small subsets. The results obtained by the self-consistency test, jackknife test and independent dataset test are encouraging, indicating that the current approach may serve as a powerful complementary tool to other existing methods for predicting the types of membrane proteins.