Computer-assisted Screening Research Articles

Cost-Effectiveness of Computer-Assisted Cytology in a Primary hrHPV-Based Cervical Cancer Screening Programme.

Computer-assisted screening (CAS) shows equal performance compared to manual screening, although results are heterogeneous. Furthermore, using CAS may save costs through a potentially increased screening productivity of technicians, therefore also offering a solution for temporary and structural capacity shortage. We evaluated the circumstances under which CAS will be cost-effective compared to manual cytology triage in a primary HPV-based cervical screening programme. Microsimulation model MISCAN-Cervix was used to evaluate 198 different CAS scenarios with varying probabilities to detect cervical intraepithelial neoplasia grade 1 (CIN1) and CIN3 and cost reductions per test, compared to manual cytology triage. Cost-effectiveness was evaluated by costs per (quality-adjusted) life year ((QA)LY) gained. CAS will be cost-effective in all scenarios, except for the following combinations: (1) no cost reduction and an increased probability of detecting CIN1, (2) a cost reduction of €2 per test and an increased probability of detecting CIN1 from 4% onwards or (3) a cost reduction of €4 per test and an increased probability of detecting CIN1 from 6% onwards, compared to manual cytology triage. All CAS scenarios with any reduction in the probability of detecting CIN1 (i.e., increased CIN2+ specificity), or a reduction in costs from €6 per test onwards suggested a more cost-effective strategy compared to manual cytology triage. As we based our analysis on a realistic range in costs and test performance, the implementation of CAS is likely to be cost-effective. Our results can be used as a guideline to advise when to choose CAS instead of manual cytology triage.

Enhancing the inhibitory activities of polyphenols in passion fruit peel on α-Amylase and α-Glucosidase via β-Glucosidase-producing Lactobacillus fermentation

Passion fruit peel contained various phenolic compounds with α-glucosidase and α-amylase inhibitory activities in vitro. However, the bound state and glycosylated forms hindered their active expression. In this article, β-glucosidase-producing Lactobacilli were inoculated into fermented peels for targeted metabolism. The results showed that the release of bound polyphenols and the increase of free polyphenols were significantly correlated with β-glucosidase activity. The strain G1 with high enzyme activity degraded 48.20% of the bound polyphenols after 24 h. The ability of extracts to inhibit α-glucosidase and α-amylase activities was significantly enhanced and closely related to the content of free polyphenols (r = −0.96 and −0.88, P < 0.001). Computer-assisted screening identified 38 phenolic compounds as potential dual inhibitors of α-glucosidase and α-amylase. Among them, 22 α-glucosidase inhibitors and 24 α-amylase inhibitors were identified for the first time. Various inhibitors, including phenolic acids, flavones, and flavonols, increased in content after fermentation. The total content of 14 glycosylated polyphenols decreased to 28.38% at 48 h, which was opposite to the aglycone. Molecular docking indicated that glycosylated polyphenols bind to β-glucosidase through hydrogen bonds and van der Waals forces. Different glycosylations at the C-3 position of quercetin bound at same sites to β-glucosidase. This study contributes to the theory of enhancing the functional activity of natural products through targeted fermentation transformation.

De Novo Biosynthesis of Difucosyllactose by Artificial Pathway Construction and α1,3/4-Fucosyltransferase Rational Design in Escherichia coli.

Difucosyllactose (DFL) is a significant and plentiful oligosaccharide found in human breast milk. In this study, an artificial metabolic pathway of DFL was designed, focusing on the de novo biosynthesis of GDP-fucose from only glycerol. This was achieved by engineering Escherichia coli to endogenously overexpress genes manB, manC, gmd, and wcaG and heterologously overexpress a pair of fucosyltransferases to produce DFL from lactose. The introduction of α-1,2-fucosyltransferase from Helicobacter pylori (FucT2) along with α-1,3/4-fucosyltransferase (HP3/4FT) addressed rate-limiting challenges in enzymatic catalysis and allowed for highly efficient conversion of lactose into DFL. Based on these results, molecular modification of HP3/4FT was performed based on computer-assisted screening and structure-based rational design. The best-performing mutant, MH5, containing a combination of five mutated sites (F49K/Y131D/Y197N/E338D/R369A) of HP3/4FT was obtained. The best strain BLC09-58 harboring MH5 yielded 45.81 g/L of extracellular DFL in 5-L fed-batch cultures, which was the highest titer reported to date.

Efficient and fast screening and separation based on computer-aided screening and complex chromatography methods for lipoxygenase inhibitors from Ganoderma lucidum.

Accurate screening and targeted preparative isolation of active substances from natural medicines have long been technical challenges in natural medicine research. This study outlines a new approach for improving the efficiency of natural product preparation, focusing on the rapid and accurate screening of potential active ingredients in Ganoderma lucidum and efficient preparation of lipoxidase inhibitors, with the aim of providing new ideas for the treatment of Alzheimer's disease with G.lucidum. The medicinal plant G.lucidum was selected through ultrafiltration coupled with liquid chromatography and mass spectrometry (UF-LC-MS) and computer-assisted screening for lipoxygenase (LOX) inhibitors. In addition, the inhibitory effect of the active compounds on LOX was studied using enzymatic reaction kinetics, and the underlying mechanism is discussed. Finally, based on the earlier activity screening guidelines, the identified ligands were isolated and purified through complex chromatography (high-speed countercurrent chromatography and semi-preparative high-performance liquid chromatography). Five active ingredients, ganoderic acids A, B, C2, D2, and F, were identified and isolated from G.lucidum. We improved the efficiency and purity of active compound preparation using virtual computer screening and enzyme inhibition assays combined with complex chromatography. The innovative methods of UF-LC-MS, computer-aided screening, and complex chromatography provide powerful tools for screening and separating LOX inhibitors from complex matrices and provide a favourable platform for the large-scale production of bioactive substances and nutrients.

Feasibility and acceptability of a computer-assisted screening and brief intervention for lifestyles in patients with chronic conditions

Feasibility and acceptability of a computer-assisted screening and brief intervention for lifestyles in patients with chronic conditions

Biological evaluation of carbazoyl hydrazine derivatives as potential Pim-1 kinase inhibitors for the treatment of human liver cancer

In this study, we investigated the anticancer activity of novel carbazoyl hydrazine derivatives as inhibitors of Proviral Integration site for Moloney murine leukemia virus kinase 1 (Pim-1) in liver cancer cells in vitro. The obtained compounds were characterized using 1H NMR, 13C NMR, HRMS, and FTIR. Through computer-assisted screening, antitumor activity testing, and Pim-1 inhibitory activity testing, most of the compounds exhibited significant inhibitory activity against liver cancer cell lines. Among them, compound N'-(2-(9H-carbazol-9-yl)acetyl)-4-fluorobenzohydrazide (4r) demonstrated a remarkable anticancer effect by inducing G2/M phase cell cycle arrest and apoptosis. It was identified as a highly efficient lead compound in our screening process. Overall, our study provides valuable insights into the anticancer activity of novel carbazoyl hydrazine derivatives as Pim-1 inhibitors in liver cancer cells. The identified lead compound 4r demonstrates promising potential for further exploration and development in future studies for liver cancer treatment.

Recent advances in computer-aided virtual screening and docking optimization for aptamer.

Aptamers, as artificially synthesized short nucleotide sequences, have been widely used in protein analysis, gene engineering, and molecular diagnostics. Currently, the screening process of aptamers still relies on the traditional SELEX process, which is cumbersome and complex. Moreover, the success rate of aptamer screening through the SELEX process is not high, which has become a major challenge. In recent years, the development of computers has facilitated virtual screening, which can greatly accelerate the screening process of aptamers through computer-assisted screening. However, the accuracy and precision of current virtual screening software on the market vary. Therefore, this work summarizes the docking characteristics of four mainstream molecular docking software programs, including Auto dock, Auto dock Vina, MOE, and hex Dock, in recent years. Moreover, the accuracy and prediction performance of these four molecular docking software programs for aptamer docking based on experimental data is also evaluated. This will guide researchers in the selection of molecular docking software. Additionally, this review provides a detailed overview of the application of computer-aided virtual screening in aptamer screening, thus providing a direction for future development in this field.

Automated Detection of Intracranial Hemorrhage from Head CT Scans Applying Deep Learning Techniques in Traumatic Brain Injuries: A Comparative Review

AbstractTraumatic brain injury (TBI) is not only an acute condition but also a chronic disease with long-term consequences. Intracranial hematomas are considered the primary consequences that occur in TBI and may have devastating effects that may lead to mass effect on the brain and eventually cause secondary brain injury. Emergent detection of hematoma in computed tomography (CT) scans and assessment of three major determinants, namely, location, volume, and size, is crucial for prognosis and decision-making, and artificial intelligence (AI) using deep learning techniques, such as convolutional neural networks (CNN) has received extended attention after demonstrations that it could perform at least as well as humans in imaging classification tasks. This article conducts a comparative review of medical and technological literature to update and establish evidence as to how technology can be utilized rightly for increasing the efficiency of the clinical workflow in emergency cases. A systematic and comprehensive literature search was conducted in the electronic database of PubMed and Google Scholar from 2013 to 2023 to identify studies related to the automated detection of intracranial hemorrhage (ICH). Inclusion and exclusion criteria were set to filter out the most relevant articles. We identified 15 studies on the development and validation of computer-assisted screening and analysis algorithms that used head CT scans. Our review shows that AI algorithms can prioritize radiology worklists to reduce time to screen for ICH in the head scans sufficiently and may also identify subtle ICH overlooked by radiologists, and that automated ICH detection tool holds promise for introduction into routine clinical practice.

Design, Synthesis and Antitumor Activity of FAK/PLK1 Dual Inhibitors with Quinazolinone as the Skeleton

Febrifugine is a kind of quinazolinone compound with high biological activity from a Chinese herb called Chang Shan (Dichroa febrifuga). Febrifugine and its derivatives possess extensive biological activities, some of which exhibited anti-tumor activities as FAK inhibitors. However, they are not very effective at inhibiting tumor metastasis, perhaps because tumors gain energy through compensatory activation of other signaling pathways that promote cell migration and invasion. Therefore, seventeen novel febrifugine derivatives with quinazolinone skeleton were designed, synthesized and acted as potential FAK/PLK1 dual inhibitors. These compounds were determined by 1 H-NMR, 13 C-NMR and MS. Most of the compounds exhibited good inhibitory activity against cancer cell lines by computer-assisted screening, antitumor activity test and FAK/PLK1 inhibitory activity test, wherein compound 3b was screened as a high-efficiency lead compound.

Design, Synthesis and Molecular Docking of 1,3,4-Oxadiazole-2(3H)-thione Derivatives Containing 1,4-Benzodioxane Skeleton as Potential FabH Inhibitors.

Fatty acid biosynthesis is essential for bacterial survival. Of these promising targets, β-ketoacyl-acyl carrier protein (ACP) synthase III (FabH) is the most attractive target. A series of novel 1,3,4-oxadiazole-2(3H)-thione derivatives containing 1,4-benzodioxane skeleton targeting FabH were designed and synthesized. These compounds were determined by 1 H-NMR, 13 C-NMR, MS and further confirmed by crystallographic diffraction study for compound 7m and 7n. Most of the compounds exhibited good inhibitory activity against bacteria by computer-assisted screening, antibacterial activity test and E. coli FabH inhibitory activity test, wherein compounds 7e and 7q exhibited the most significant inhibitory activities. Besides, compound 7q showed the best E. coli FabH inhibitory activity (IC50 =2.45 μΜ). Computational docking studies also showed that compound 7q interacts with FabH critical residues in the active site.

Two Years of Cytology and HPV Co-Testing in Germany: Initial Experience.

Introduction On 1 January 2020 the screening programme for the prevention of cervical cancer in women from the age of 35 years of the Statutory Health Insurance (GKV) in Germany changed from an annual cytology examination to cytological and HPV co-testing carried out every three years. A large standard diagnostics laboratory has been using liquid-based cytology (LBC) with computer-assisted screening (CAS) since 1 January 2020 to assess the samples. Patients and Methods The cytological and HPV results for all cases examined with co-testing from 01.01.2020 to 31.12.2021 (n=395759) are reported and the cytology results obtained using co-testing are compared with the results obtained using only conventional primary cytology screening from the two previous years (n=588192). Cytology tests were carried out using LBC and computer-assisted screening. A DNA PCR test which can identify 14 types of HPV was used for HPV testing. The cytology results are reported using the Munich Nomenclature III, which is mandatory in Germany, and converted to The Bethesda System (TBS). Problems occurring during the implementation phase are described here. Results A total of 983951 cases who had primary screening between 01.01.2018 and 31.12.2021 were analysed. The HR HPV-positive rate with co-testing for all age groups was 6.41%. Of this group, 16.31% were positive for HPV-16, 4.43% for HPV-18, and 71.40% had one or more of the other 12 HR HPV types. Several different HPV types were identified in 7.86% of cases. The HPV-positive rate for cases with unremarkable cytological findings was 4.03%. 0.46% of tests were technically invalid. The results of primary cytology screening for 2020/21 (LBC) were: Pap 0 (TBS: unsatisfactory) 0.09%, Pap I and Pap II-a (NILM) 96.82%, Pap II-p/g (~ASC-US/AGC) 1.23%, Pap III-p/g (~ASC-H/AGC) 0.19%, Pap III D1 (LSIL) 1.08%, Pap III D2 (HSIL) 0.31%, Pap IVa/b-p/g (HSIL/AIS) 0.18%, and Pap V-p/g (carcinoma) 0.01%. The rates for 2018/19 (conventional cytology without routine testing for HPV) were significantly higher for Pap II-p/g (1.64%) and significantly lower for Pap III-p/g (0.13%), Pap III D1 (0.45%), Pap III D2 (0.10%) and Pap IVa/b-p/g (0.05%). Conclusion Evaluation of the data for the two first years of cytology and HPV co-testing from a standard diagnostics laboratory found low HR HPV-positive rates. As regards the cytology tests, the Pap II-p/g rate was significantly lower and the ≥Pap III rate was significantly higher compared to the two previous years. This points to a probable higher sensitivity and specificity of the new method.

Feature engineering and machine learning for computer-assisted screening of children with speech disorders.

Auditory perceptual analysis (APA) is the main method for clinical assessment of speech-language deficits, which are one of the most prevalent childhood disabilities. However, results from APA are susceptible to intra- and inter-rater variabilities. There are also other limitations of manual or hand transcription-based speech disorder diagnostic methods. There is increased interest in developing automated methods that quantify speech patterns for diagnosing speech disorders in children to address these limitations. Landmark (LM) analysis is an approach that characterizes acoustic events occurring due to sufficiently precise articulatory movements. This work investigates the utilization of LMs for automatic speech disorder detection in children. Besides the LM-based features that have been proposed in existing research, we propose a set of novel knowledge-based features that have not been proposed before. A systematic study and comparison of different linear and nonlinear machine learning classification techniques based on the raw features and the proposed features is conducted to assess the effectiveness of the novel features in classifying speech disorder patients from normal speakers.

Design, Synthesis and Biological Activity Testing of Library of Sphk1 Inhibitors.

Our team discovered a moderate SphK1 inhibitor, SAMS10 (IC50 = 9.8 μM), which was screened by computer-assisted screening. In this study, we developed a series of novel diaryl derivatives with improved antiproliferative activities by modifying the structure of the lead compound SAMS10. A total of 50 new compounds were synthesized. Among these compounds, the most potent compound, named CHJ04022Rb, has significant anticancer activity in melanoma A375 cell line (IC50 = 2.95 μM). Further underlying mechanism studies indicated that CHJ04022R exhibited inhibition effect against PI3K/NF-κB signaling pathways, inhibited the migration of A375 cells, promoted apoptosis and exerted antiproliferative effect by inducing G2/M phase arrest in A375 cells. Furthermore, acute toxicity experiment indicated CHJ04022R exhibited good safety in vivo. Additionally, it showed a dose-dependent inhibitory effect on the growth of xenograft tumor in nude mice. Therefore, CHJ04022R may be a potential candidate for the treatment of melanoma.

Fat-based studies for computer-assisted screening of child obesity using thermal imaging based on deep learning techniques: a comparison with quantum machine learning approach

Fat-based studies for computer-assisted screening of child obesity using thermal imaging based on deep learning techniques: a comparison with quantum machine learning approach

A Rapid Cytological Screening as pre-Endoscopy Screening for Early Esophageal Squamous Cell Lesions: A Prospective Pilot Study from a Chinese Academic Center.

Background: Cytological detection of early esophageal squamous cell carcinoma (ESCC) remains challenging. Therefore, we introduced a rapid cytological screening method and evaluated its efficacy as a pre-endoscopy screening for early ESCC and precursor lesions. Methods: This method consisted of a sponge sample retrieval, automatic liquid-based cytological treatment and slides preparation, computer-assisted screening and manual diagnosis. Efficacy for detection of early ESCC and precursor lesions was evaluated. Also, diagnostic efficiency was compared with manual diagnosis. Results: Eighty-three patients with early ESCC and precursor lesions and 2,090 asymptomatic participants with high risks of ESCC were enrolled. Whole procedure was accomplished within two working days. Abnormal cells were detected in all 83 patients, and in 272 (13.01%) subjects among 2,090 asymptomatic participants. Early ESCC, high-grade intraepithelial neoplasia, low-grade intraepithelial neoplasia and reflux esophagitis and normal endoscopic findings were detected in 8, 13, 11, 187 and 53 participants with abnormal cells, respectively. The calculated sensitivity, specificity, positive predictive value and negative predictive value for detection of early ESCC and precursor lesions were 100%, 88.34%, 11.76%, and 100%, respectively. Compared with manual diagnosis, this method was accomplished in a shorter time duration (5.4 ± 0.45 min vs 320.2 ± 132.4 min, p < 0.001), a higher diagnostic accuracy (96.7% vs74.4%, p = 0.015) and a better inter-observer agreement (93.3% vs66.7%, K = 0.286, p < 0.001). Conclusions: Our study provides a promising methodology as pre-endoscopy screening for early ESCC and precursor lesions.

Deep-learning based detection of COVID-19 using lung ultrasound imagery

BackgroundThe COVID-19 pandemic has exposed the vulnerability of healthcare services worldwide, especially in underdeveloped countries. There is a clear need to develop novel computer-assisted diagnosis tools to provide rapid and cost-effective screening in places where massive traditional testing is not feasible. Lung ultrasound is a portable, easy to disinfect, low cost and non-invasive tool that can be used to identify lung diseases. Computer-assisted analysis of lung ultrasound imagery is a relatively recent approach that has shown great potential for diagnosing pulmonary conditions, being a viable alternative for screening and diagnosing COVID-19.ObjectiveTo evaluate and compare the performance of deep-learning techniques for detecting COVID-19 infections from lung ultrasound imagery.MethodsWe adapted different pre-trained deep learning architectures, including VGG19, InceptionV3, Xception, and ResNet50. We used the publicly available POCUS dataset comprising 3326 lung ultrasound frames of healthy, COVID-19, and pneumonia patients for training and fine-tuning. We conducted two experiments considering three classes (COVID-19, pneumonia, and healthy) and two classes (COVID-19 versus pneumonia and COVID-19 versus non-COVID-19) of predictive models. The obtained results were also compared with the POCOVID-net model. For performance evaluation, we calculated per-class classification metrics (Precision, Recall, and F1-score) and overall metrics (Accuracy, Balanced Accuracy, and Area Under the Receiver Operating Characteristic Curve). Lastly, we performed a statistical analysis of performance results using ANOVA and Friedman tests followed by post-hoc analysis using the Wilcoxon signed-rank test with the Holm’s step-down correction.ResultsInceptionV3 network achieved the best average accuracy (89.1%), balanced accuracy (89.3%), and area under the receiver operating curve (97.1%) for COVID-19 detection from bacterial pneumonia and healthy lung ultrasound data. The ANOVA and Friedman tests found statistically significant performance differences between models for accuracy, balanced accuracy and area under the receiver operating curve. Post-hoc analysis showed statistically significant differences between the performance obtained with the InceptionV3-based model and POCOVID-net, VGG19-, and ResNet50-based models. No statistically significant differences were found in the performance obtained with InceptionV3- and Xception-based models.ConclusionsDeep learning techniques for computer-assisted analysis of lung ultrasound imagery provide a promising avenue for COVID-19 screening and diagnosis. Particularly, we found that the InceptionV3 network provides the most promising predictive results from all AI-based techniques evaluated in this work. InceptionV3- and Xception-based models can be used to further develop a viable computer-assisted screening tool for COVID-19 based on ultrasound imagery.

Quantitative detection of cervical cancer based on time series information from smear images

Existing cervical cancer detection methods usually screen the samples based on separated cells. Cell misclassification leads to poor robustness and accuracy, quantitative analysis is missed. Global smear information and cell relationship are also not fully utilized. Aiming at the mentioned limitations, a cervical quantitative detection framework which combines the fine-tuned Long Short-Term Memory Fully Convolutional Network and Fuzzy Nonlinear Regression is proposed. Time series-based​ screening improves the detection performance. Deoxyribonucleic Acid (DNA) value is better expressed by the rectified method using soft computing. A cervical dataset containing 657 samples is used for training and validation, accuracy, sensitivity, and specificity of 98.3%, 98.1% and 97.9% are achieved with the time series features, providing an automatic and effective way for the computer-assisted screening of cervical cancer.

Evaluating the relationship between citation set size, team size and screening methods used in systematic reviews: a cross-sectional study

BackgroundStandard practice for conducting systematic reviews (SRs) is time consuming and involves the study team screening hundreds or thousands of citations. As the volume of medical literature grows, the citation set sizes and corresponding screening efforts increase. While larger team size and alternate screening methods have the potential to reduce workload and decrease SR completion times, it is unknown whether investigators adapt team size or methods in response to citation set sizes. Using a cross-sectional design, we sought to understand how citation set size impacts (1) the total number of authors or individuals contributing to screening and (2) screening methods.MethodsMEDLINE was searched in April 2019 for SRs on any health topic. A total of 1880 unique publications were identified and sorted into five citation set size categories (after deduplication): < 1,000, 1,001–2,500, 2,501–5,000, 5,001–10,000, and > 10,000. A random sample of 259 SRs were selected (~ 50 per category) for data extraction and analysis.ResultsWith the exception of the pairwise t test comparing the under 1000 and over 10,000 categories (median 5 vs. 6, p = 0.049) no statistically significant relationship was evident between author number and citation set size. While visual inspection was suggestive, statistical testing did not consistently identify a relationship between citation set size and number of screeners (title-abstract, full text) or data extractors. However, logistic regression identified investigators were significantly more likely to deviate from gold-standard screening methods (i.e. independent duplicate screening) with larger citation sets. For every doubling of citation size, the odds of using gold-standard screening decreased by 15 and 20% at title-abstract and full text review, respectively. Finally, few SRs reported using crowdsourcing (n = 2) or computer-assisted screening (n = 1).ConclusionsLarge citation set sizes present a challenge to SR teams, especially when faced with time-sensitive health policy questions. Our study suggests that with increasing citation set size, authors are less likely to adhere to gold-standard screening methods. It is possible that adjunct screening methods, such as crowdsourcing (large team) and computer-assisted technologies, may provide a viable solution for authors to complete their SRs in a timely manner.

Computer-assisted screening of mycobacterial growth inhibitors: Exclusion of frequent hitters with the assistance of the multiple target screening method.

The emergence of frequent hitters (FHs) remains a challenge in drug discovery. We have previously used in silico structure-based drug screening (SBDS) to identify antimycobacterial candidates. However, excluding FHs has not been integrated into the SBDS system. A dataset comprising 15,000 docking score (protein-compound affinity matrix) was constructed by multiple target screening (MTS): DOCK-GOLD two-step docking simulations with 154,118 compounds versus the 30 target proteins essential for mycobacterial survival. After extraction of 141 compounds from the protein-compound affinity matrix, compounds determined to be FHs or false positives were excluded. Antimycobacterial properties of the top nine compounds selected through SBDS were experimentally evaluated. Nine compounds designated KS1-KS9 were selected for experimental evaluation. Among the selected compounds, KS3, identified as adenosylhomocysteinase inhibitor, showed a potent inhibitory effect on antimycobacterial growth (inhibitory concentration [IC]50 = 1.2 M). However, the compound also showed potent cytotoxicity. The MTS method is applicable in SBDS for the identification of enzyme-specific inhibitors.

Analysis of the SNARE Stx8 recycling reveals that the retromer-sorting motif has undergone evolutionary divergence.

Fsv1/Stx8 is a Schizosaccharomyces pombe protein similar to mammalian syntaxin 8. stx8Δ cells are sensitive to salts, and the prevacuolar endosome (PVE) is altered in stx8Δ cells. These defects depend on the SNARE domain, data that confirm the conserved function of syntaxin8 and Stx8 in vesicle fusion at the PVE. Stx8 localizes at the trans-Golgi network (TGN) and the prevacuolar endosome (PVE), and its recycling depends on the retromer component Vps35, and on the sorting nexins Vps5, Vps17, and Snx3. Several experimental approaches demonstrate that Stx8 is a cargo of the Snx3-retromer. Using extensive truncation and alanine scanning mutagenesis, we identified the Stx8 sorting signal. This signal is an IEMeaM sequence that is located in an unstructured protein region, must be distant from the transmembrane (TM) helix, and where the 133I, 134E, 135M, and 138M residues are all essential for recycling. This sorting motif is different from those described for most retromer cargoes, which include aromatic residues, and resembles the sorting motif of mammalian polycystin-2 (PC2). Comparison of Stx8 and PC2 motifs leads to an IEMxx(I/M) consensus. Computer-assisted screening for this and for a loose Ψ(E/D)ΨXXΨ motif (where Ψ is a hydrophobic residue with large aliphatic chain) shows that syntaxin 8 and PC2 homologues from other organisms bear variation of this motif. The phylogeny of the Stx8 sorting motifs from the Schizosaccharomyces species shows that their divergence is similar to that of the genus, showing that they have undergone evolutionary divergence. A preliminary analysis of the motifs in syntaxin 8 and PC2 sequences from various organisms suggests that they might have also undergone evolutionary divergence, what suggests that the presence of almost-identical motifs in Stx8 and PC2 might be a case of convergent evolution.