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Background: Antipsychotic medications are associated with weight gain and metabolic derangement. However, comprehensive evidence for the efficacy of co-commenced pharmacological treatments to mitigate initial weight gain is limited. Metformin has been shown to be effective in reducing weight among people on antipsychotic medications who are already overweight, but the potential benefits of metformin co-commencement in mitigating antipsychotic-induced weight gain has not been systematically reviewed. Method: We conducted a systematic review of PubMed, EMBASE, PsychInfo, CINAHL, the Cochrane database, and China National Knowledge Infrastructure from inception to 18 November 2023. We undertook a meta-analysis of concomitant commencement of metformin versus placebo for attenuation of weight gain and metabolic syndrome for people with schizophrenia commencing a new antipsychotic. Results: Fourteen studies from Australia, United States, Venezuela, and China with 1126 participants were included. We found that metformin was superior to placebo in terms of attenuating weight gain (−3.12 kg, 95% CI −4.22 to −2.01 kg). Metformin also significantly attenuated derangement of fasting glucose levels, total cholesterol, and total triglyceride levels. Sensitivity analysis on study quality, duration, and antipsychotic agent did not impact the results. Meta-analysis was also conducted on adverse drug reactions (ADR) reported in each study which showed no significant difference in ADR incidence between metformin and placebo groups. Subgroup analysis on antipsychotic-naïve participants and participants switching to new antipsychotic did not impact the results. Conclusion: Metformin led to statistically significant and clinically meaningful attenuation of weight gain as well as attenuation of several other metabolic parameters when commenced concomitantly with antipsychotic medications. Co-commencement of metformin with antipsychotic medications, where tolerated, should be considered in the clinical setting with aim to improve long-term cardiometabolic outcomes for patients with long-term need of antipsychotic treatments.

Background: Most reports have indicated the antioxidant capacity of quinoa seeds. However, the leaves of Quinoa (Chenopodium quinoa Willd.) are usually worthless and little known about their biological activities. In this study, the antioxidant and immunomodulatory potential of the quinoa leaf extracts were explored. Methods: The crude leaf extracts of quinoa were extracted using water, 50% ethanol or 95% ethanol as solvent, denoted WQL, 50% EQL and 95% EQL, respectively. The antioxidant activities of quinoa leaf extracts were assessed by the ability of 1,1-diphenyl-2-picrylhydrazyl (DPPH) scavenging and iron chelating. The total phenolic content was determined. Inhibition of nitric oxide (NO) production in the lipopolysaccharide (LPS)-induced murine macrophage RAW 264.7 cells was examined to gauge the anti-inflammatory activity. Results: The 95% EQL showed a higher level of total phenolic content (569.5 mg GAE/g extract) and better DPPH scavenging activity. The WQL exhibited a better iron chelating capacity (28.9% at 10 mg/ml). The iron chelating activity of the 95% EQL increased in a concentration-dependent manner, which ranged from 10.9% up to 53.9%. The 50% EQL and 95% EQL significantly inhibited NO production in the LPSstimulated RAW 264.7 cells. Conclusion: We demonstrate that the extracts of quinoa leaves possess the biological activities of antioxidant and anti-inflammatory. Our finding suggests that the leaf extract of quinoa has potential to be utilized for natural health products.

Abstract Background To identify pretreatment carcinoembryonic antigen (CEA) levels as a risk factor for para-aortic lymph node (PALN) recurrence following concurrent chemoradiotherapy (CCRT) for cervical cancer. Methods From March 1995 to January 2008, 188 patients with squamous cell carcinoma (SCC) of the uterine cervix were analyzed retrospectively. No patient received PALN irradiation as the initial treatment. CEA and squamous cell carcinoma antigen (SCC-Ag) were measured before and after radiotherapy. PALN recurrence was detected by computer tomography (CT) scans. We analyzed the actuarial rates of PALN recurrence by using Kaplan-Meier curves. Multivariate analyses were carried out with Cox regression models. We stratified the risk groups based on the hazard ratios (HR). Results Both pretreatment CEA levels ≥ 10 ng/mL and SCC-Ag levels < 10 ng/mL (p < 0.001, HR = 8.838), SCC-Ag levels ≥ 40 ng/mL (p < 0.001, HR = 12.551), and SCC-Ag levels of 10-40 ng/mL (p < 0.001, HR = 4.2464) were significant factors for PALN recurrence. The corresponding 5-year PALN recurrence rates were 51.5%, 84.8%, and 27.5%, respectively. The 5-year PALN recurrence rate for patients with both low (< 10 ng/mL) SCC and CEA was only 9.6%. CEA levels ≥ 10 ng/mL or SCC-Ag levels ≥ 10 ng/mL at PALN recurrence were associated with overall survival after an isolated PALN recurrence. Pretreatment CEA levels ≥ 10 ng/mL were also associated with survival after an isolated PALN recurrence. Conclusions Pretreatment CEA ≥ 10 ng/mL is an additional risk factor of PALN relapse following definitive CCRT for SCC of the uterine cervix in patients with pretreatment SCC-Ag levels < 10 ng/mL. More comprehensive examinations before CCRT and intensive follow-up schedules are suggested for early detection and salvage in patients with SCC-Ag or CEA levels ≥ 10 ng/mL.

Abstract Background While occurring enzymatically in biological systems, O-linked glycosylation affects protein folding, localization and trafficking, protein solubility, antigenicity, biological activity, as well as cell-cell interactions on membrane proteins. Catalytic enzymes involve glycotransferases, sugar-transferring enzymes and glycosidases which trim specific monosaccharides from precursors to form intermediate structures. Due to the difficulty of experimental identification, several works have used computational methods to identify glycosylation sites. Results By investigating glycosylated sites that contain various motifs between Transmembrane (TM) and non-Transmembrane (non-TM) proteins, this work presents a novel method, GlycoRBF, that implements radial basis function (RBF) networks with significant amino acid pairs (SAAPs) for identifying O-linked glycosylated serine and threonine on TM proteins and non-TM proteins. Additionally, a membrane topology is considered for reducing the false positives on glycosylated TM proteins. Based on an evaluation using five-fold cross-validation, the consideration of a membrane topology can reduce 31.4% of the false positives when identifying O-linked glycosylation sites on TM proteins. Via an independent test, GlycoRBF outperforms previous O-linked glycosylation site prediction schemes. Conclusion A case study of Cyclic AMP-dependent transcription factor ATF-6 alpha was presented to demonstrate the effectiveness of GlycoRBF. Web-based GlycoRBF, which can be accessed at http://GlycoRBF.bioinfo.tw, can identify O-linked glycosylated serine and threonine effectively and efficiently. Moreover, the structural topology of Transmembrane (TM) proteins with glycosylation sites is provided to users. The stand-alone version of GlycoRBF is also available for high throughput data analysis.

Abstract Background Though prediction of protein secondary structures has been an active research issue in bioinformatics for quite a few years and many approaches have been proposed, a new challenge emerges as the sizes of contemporary protein structure databases continue to grow rapidly. The new challenge concerns how we can effectively exploit all the information implicitly deposited in the protein structure databases and deliver ever-improving prediction accuracy as the databases expand rapidly. Findings The new challenge is addressed in this article by proposing a predictor designed with a novel kernel density estimation algorithm. One main distinctive feature of the kernel density estimation based approach is that the average execution time taken by the training process is in the order of O(nlogn), where n is the number of instances in the training dataset. In the experiments reported in this article, the proposed predictor delivered an average Q3 (three-state prediction accuracy) score of 80.3% and an average SOV (segment overlap) score of 76.9% for a set of 27 benchmark protein chains extracted from the EVA server that are longer than 100 residues. Conclusion The experimental results reported in this article reveal that we can continue to achieve higher prediction accuracy of protein secondary structures by effectively exploiting the structural information deposited in fast-growing protein structure databases. In this respect, the kernel density estimation based approach enjoys a distinctive advantage with its low time complexity for carrying out the training process.

Abstract Background More and more disordered regions have been discovered in protein sequences, and many of them are found to be functionally significant. Previous studies reveal that disordered regions of a protein can be predicted by its primary structure, the amino acid sequence. One observation that has been widely accepted is that ordered regions usually have compositional bias toward hydrophobic amino acids, and disordered regions are toward charged amino acids. Recent studies further show that employing evolutionary information such as position specific scoring matrices (PSSMs) improves the prediction accuracy of protein disorder. As more and more machine learning techniques have been introduced to protein disorder detection, extracting more useful features with biological insights attracts more attention. Results This paper first studies the effect of a condensed position specific scoring matrix with respect to physicochemical properties (PSSMP) on the prediction accuracy, where the PSSMP is derived by merging several amino acid columns of a PSSM belonging to a certain property into a single column. Next, we decompose each conventional physicochemical property of amino acids into two disjoint groups which have a propensity for order and disorder respectively, and show by experiments that some of the new properties perform better than their parent properties in predicting protein disorder. In order to get an effective and compact feature set on this problem, we propose a hybrid feature selection method that inherits the efficiency of uni-variant analysis and the effectiveness of the stepwise feature selection that explores combinations of multiple features. The experimental results show that the selected feature set improves the performance of a classifier built with Radial Basis Function Networks (RBFN) in comparison with the feature set constructed with PSSMs or PSSMPs that adopt simply the conventional physicochemical properties. Conclusion Distinguishing disordered regions from ordered regions in protein sequences facilitates the exploration of protein structures and functions. Results based on independent testing data reveal that the proposed predicting model DisPSSMP performs the best among several of the existing packages doing similar tasks, without either under-predicting or over-predicting the disordered regions. Furthermore, the selected properties are demonstrated to be useful in finding discriminating patterns for order/disorder classification.