EGeRepDR: An enhanced genetic-based representation learning for drug repurposing using multiple biomedical sources.

MOTIVATION: Drug repurposing (DR) is an imminent approach for identifying novel therapeutic indications for the available drugs and discovering novel drugs for previously untreatable diseases. Nowadays, DR has major attention in the pharmaceutical industry due to the high cost and time of launching new drugs to the market through traditional drug development. DR task majorly depends on genetic information since the drugs revert the modified Gene Expression (GE) of diseases to normal. Many of the existing studies have not considered the genetic importance of predicting the potential candidates. METHOD: We proposed a novel multimodal framework that utilizes genetic aspects of drugs and diseases such as genes, pathways, gene signatures, or expression to enhance the performance of DR using various data sources. Firstly, the heterogeneous biological network (HBN) is constructed with three types of nodes namely drug, disease, and gene, and 4 types of edges similarities (drug, gene, and disease), drug-gene, gene-disease, and drug-disease. Next, a modified graph auto-encoder (GAE*) model is applied to learn the representation of drug and disease nodes using the topological structure and edge information. Secondly, the HBN is enhanced with the information extracted from biomedical literature and ontology using a novel semi-supervised pattern embedding-based bootstrapping model and novel DR perspective representation learning respectively to improve the prediction performance. Finally, our proposed system uses a neural network model to generate the probability score of drug-disease pairs. RESULTS: We demonstrate the efficiency of the proposed model on various datasets and achieved outstanding performance in 5-fold cross-validation (AUC = 0.99, AUPR = 0.98). Further, we validated the top-ranked potential candidates using pathway analysis and proved that the known and predicted candidates share common genes in the pathways.

Stratified support pattern-based internet-assisted self-management therapy for diabetes mellitus -mild cognitive impairment: a randomized controlled trial protocol.

BACKGROUND: Mild cognitive impairment (MCI) associated with diabetes mellitus (DM) is common among older adults, and self-management is critical to controlling disease progression. However, both MCI and DM are heterogeneous diseases, and existing integrated self-management interventions do not consider patient differences. Grouping patients by disease characteristics could help to individualize disease management and improve the use of available resources. The current study sought to explore the feasibility and effectiveness of a stratified support model for DM-MCI patients. METHODS: Eighty-four DM-MCI patients will be randomly divided into an intervention group and a control group in a 1:1 ratio. The intervention group will receive a self-management intervention using the stratified support pattern-based internet-assisted therapy (SISMT), while the control group will receive the health manual intervention (HMI). The study recruiter will be blinded to the group allocation and unable to foresee which group the next participant will be assigned to. At the same time, the allocation will be also hidden from the research evaluators and participants. After 12 weeks and 24 weeks, cognitive function, blood glucose, self-management ability, psychological status, health literacy, and self-management behavior of patients in both groups will be measured and compared. DISCUSSION: This study developed a stratified support pattern-based internet-assisted to provide self-management intervention for patients with DM-MCI. The impact of different models and forms of self-management intervention on cognitive function, blood glucose management, and psychological status health literacy and self-management behavior of patients will be assessed. The results of this study will inform related intervention research on the stratified support pattern-based internet-assisted self-management therapy, and help to slow the decline of cognitive function in patients with DM-MCI. TRIAL REGISTRATION: ChiCTR2200061991. Registered 16 July 2022.

Development of a behaviour pattern-based testing approach for coupled socioeconomic and environmental models.

Understanding the interactions between human and environmental systems is key to sustainable environmental management. Dynamically Coupled Socioeconomic system dynamics models integrated with physically-based Environmental Models (DCSEMs) are promising tools to appropriately capture the non-linear relationships between complex socioeconomic and biophysical systems, thereby supporting sustainable environmental management. However, existing approaches for testing integrated models are commonly based on the point-to-point analysis of model outputs, which is not suitable for DCSEMs that are behaviour pattern oriented. Consequently, the lack of well-defined behaviour pattern-based approaches has limited the adaptability of DCSEMs. To address this gap, this study proposes a novel behaviour pattern-based model testing approach that includes global sensitivity analysis (GSA), auto-calibration algorithms, and evaluation to assess behaviour pattern similarities between model outputs and real-world trends. The proposed approach is demonstrated through a real-world case study, in which an existing DCSEM is calibrated and evaluated to simulate water table depth in the Rechna Doab region of Pakistan. Compared to the conventional numerical point approach, the proposed approach is better suited for DCSEMs, as it replicates observed system behaviour patterns (as opposed to observed point values) over time. Furthermore, the outcomes of the Theil inequality statistical analysis and parameter distribution analysis provide evidence that the suggested approach is effective in testing and improving the performance of the DCSEM by capturing the spatial heterogeneity within the study area. The proposed behaviour-pattern testing procedure is a useful approach for model testing in data-limited, spatially-distributed DCSEMs.

Patterns of Reasons for Not Disclosing Personal Activities and Feelings to Mothers and Fathers among Ethnically Diverse Adolescents.

Adolescents' routine disclosure and self (non)disclosure to parents have been distinguished conceptually, but rarely empirically. Using latent profile analyses (LPA), these two types of (non)disclosure were operationalized and examined in terms of the patterns of reasons middle adolescents endorsed for not disclosing personal activities and personal feelings to mothers and fathers and their correlates. This was studied in a sample of 489 U.S. Chinese, Mexican, and European heritage middle adolescents (Mage = 16.37, SD = 0.77, 55% females). Three profiles emerged for both mothers and fathers: A majority profile for mothers consisting of adolescents who viewed personal activities and feelings as personal (i.e., private and not harmful), and much smaller sanction-driven and self-conscious profiles. With fathers, personal concerns were separated in the private profile, which also emphasized that fathers would not listen or understand, a harmless profile, and as with mothers, a sanction-driven profile. Overall, but varying in frequency for different profiles, middle adolescents emphasized personal concerns for not disclosing routine personal activities and psychological concerns for self nondisclosure. The profiles also differed by ethnicity/race, generational status, and trust in mothers and fathers. The father private profile and sanction-driven profiles with both parents were associated with more depressive symptoms and problem behavior relative to the other profiles. The results provide insight into why middle adolescents of diverse ethnicities do not disclose personal information to parents.

Silva cumulative score and its relationship with prognosis in Endocervical adenocarcinoma.

BACKGROUND: The Silva system has been demonstrated to have a good predictive value of lymph node metastasis (LNM) in endocervical adenocarcinoma (EAC). Tumours were classified based on the highest identified pattern of invasion in this system, this may not exactly reflect the true situation when it presents with a "mixed pattern" in some cases. Recent study has shown that patients with lymphovascular invasion (LVI) have worse prognosis in EAC. Here we design a Silva cumulative score (SCS) system which also combined the LVI status to explore its prognostic role in EAC patients. METHODS: A total of 120 patients with EAC were included in this study. Clinicopathological characteristics were retrospectively retrieved from the medical records and follow-up data were obtained. The clinicopathological information included age at diagnosis, depth of invasion (DOI), LNM, LVI, Silva classification, and SCS. SCS is a classification system based on the sum score of different Silva pattern which is founded on morphological phenomena. The relationships between the pathological characteristics and prognoses were analyzed. RESULTS: According to the Silva system, 11 (9.2%), 22 (18.3%) and 87 (72.5%) patients had patterns A, B, and C, respectively. Patients with pattern C had the highest incidence of LVI and LNM (p < 0.05). Although the Kaplan-Meier curves demonstrated that survival decreased with increasing Silva classification for A-C cancers, there was no statistically significant difference [disease-free survival (DFS): p = 0.181; overall survival (OS): p = 0.205]. There were 45 cases presented as mixed-type of Silva patterns. According to the SCS, 23 cases (19.2%) were rated as grade I, 31 cases (25.8%) as grade II and 66 (55.0%) cases as grade III. Patients with SCS grade III had the highest incidence of LVI and LNM (p < 0.05). Kaplan-Meier analysis revealed that patients with higher SCS had significantly shorter DFS and OS than those with lower SCS (p < 0.05). High SCS was an independent predictor of poorer OS and DFS (p < 0.05) in patients with EAC. CONCLUSIONS: The application of the Silva system could effectively predict the LNM of patients and may be helpful in selecting an appropriate surgical procedure. The SCS system we designed showed a good predictive value for DFS and OS in EAC.

Using Silva pattern system to predict prognosis and plan treatment of invasive endocervical adenocarcinoma: a single-center retrospective analysis.

BACKGROUND: This study evaluated the prognostic value of the Silva pattern system for invasive endocervical adenocarcinoma (EAC) by analysing its association with clinical and pathological features to provide more appropriate clinical management. METHODS: A retrospective analysis including 63 patients with pathological diagnosis of invasive EAC was performed from March 2011 to December 2016 at our hospital. All pathological slides were reviewed by three senior pathologists, and cases were stratified into patterns A, B, or C by consensus according to the Silva pattern system criteria. Clinicopathological characteristics and follow-up of the three Silva subgroups were analysed. RESULTS: Silva A, B, and C EAC patients were compared based on tumour size, clinical stage, lymphovascular invasion (LVI), and depth of invasion (DOI). The differences were found to be statistically significant (p < 0.01). There was no statistically significant difference in the proportion of lymph node metastasis among the three groups (p > 0.05) or in the recurrence and mortality rates of patients with Silva A, B, and C EAC (p > 0.05). Single factor analysis showed that tumour size, clinical stage, lymph node metastasis, LVI, and DOI were related to postoperative recurrence, whereas age, Silva classification, and postoperative recurrence were not correlated. CONCLUSION: The Silva classification system can predict lymph node status and prognosis of invasive EAC, but it cannot be used as an independent indicator. Individualized treatment plans should be adopted for patients with EAC.

Practical immunohistochemistry in the classification of salivary gland neoplasms.

Diagnosis of salivary gland neoplasms can be challenging for surgical pathologists due to low incidence of tumors as well as overlapping histologic features. On small biopsy, the most important information to be conveyed for clinical management is the distinction between a benign/low grade tumor and a high grade carcinoma. This review will discuss the differential diagnosis of salivary gland tumors based on four broad morphologic patterns: basaloid/tubular/cribriform, (micro)cystic/secretory/mucinous, solid-nested/clear-spindled, and oncocytic/oncocytoid. With the assistance of immunohistochemistry, demonstration of the number of cell types (mainly epithelial versus myoepithelial/basal) can further subclassify tumors within these morphologic categories. Additional tumor-specific immunomarkers are useful in some cases. Underlying tumor-specific genetic anomalies can be of value, however, immunohistochemical correlates are only available for some. When used judiciously, in the correct morphologic context, and with knowledge of their limitations, immunohistochemical stains can aid in differentiating tumors with similar morphology.

Excitation-emission fluorescence matrix acquired from glutathione capped CdSeS/ZnS quantum dots in combination with chemometric tools for pattern-based sensing of neurotransmitters.

The presented work concerns pattern-based sensing with quantum dots for the identification and quantification of neurotransmitters by means of excitation-emission fluorescence spectroscopy (2D fluorescence). In the framework of this study, glutathione capped CdSeS/ZnS nanocrystals were used as non-specific nanoreceptors capable of differentiated interaction with neurotransmitters. The pattern-based sensing with QDs was realized by using excitation-emission fluorescence spectroscopy to provide analyte-specific multidimensional optical information. These characteristic fluorescent response patterns were processed by unfolded partial least squares-discriminant analysis, showing that satisfactory identification of all investigated neurotransmitters: dopamine, norepinephrine, epinephrine, serotonin, GABA, and acetylcholine, can be achieved through the proposed sensing strategy. The impact of the considered fluorescence signal (datum, i.e. zeroth-order data acquired per sample; spectrum, i.e. first-order data acquired per sample; excitation-emission matrix, i.e. second-order data acquired per sample) on the sensing capability of glutathione capped QDs was also verified. The best performance parameters such as accuracy, precision, sensitivity, and specificity were obtained using excitation-emission matrices (88.9-93.3%, 0.93-0.95, 0.89-0.93, and 0.99-1.00, respectively). Thus, it was revealed that excitation-emission fluorescence spectroscopy may improve the recognition of neurotransmitters while using only one type of nanoreceptor. Furthermore, is was demonstrated that the proposed excitation-emission fluorescence spectroscopy assisted QD assay coupled with unfolded partial least squares regression can be successfully utilized for quantitative determination of catecholamine neurotransmitters at the micromolar concentration range with R2 in the range 0.916-0.987. Consequently, the proposed sensing strategy has the potential to significantly simplify the sensing element and to expand the pool of bioanalytes so far detectable with the use of QDs.

Beta-Lactam Antibiotic Discrimination Using a Macromolecular Sensor in Water at Neutral pH.

Penicillins and cephalosporins belong to the ß-lactam antibiotic family, which accounts for more than half of the world market for antibiotics. Misuse of antibiotics harms human health and the environment. Here, we describe an easy, fast, and sensitive optical method for the sensing and discrimination of two penicillin and five cephalosporin antibiotics in buffered water at pH 7.4, using fifth-generation poly (amidoamine) (PAMAM) dendrimers and calcein, a commercially available macromolecular polyelectrolyte and a fluorescent dye, respectively. In aqueous solution at pH 7.4, the dendrimer and dye self-assemble to form a sensor that interacts with carboxylate-containing antibiotics through electrostatic interaction, monitored through changes in the dye's spectroscopic properties. This response was captured through absorbance, fluorescence emission, and fluorescence anisotropy. The resulting data set was processed through linear discriminant analysis (LDA), a common pattern-base recognition method, for the differentiation of cephalosporins and penicillins. By pre-hydrolysis of the ß-lactam rings under basic conditions, we were able to increase the charge density of the analytes, allowing us to discriminate the seven analytes at a concentration of 5 mM, with a limit of discrimination of 1 mM.

Morphological classification of pancreatic ductal adenocarcinoma that predicts molecular subtypes and correlates with clinical outcome.

INTRODUCTION: Transcriptional analyses have identified several distinct molecular subtypes in pancreatic ductal adenocarcinoma (PDAC) that have prognostic and potential therapeutic significance. However, to date, an indepth, clinicomorphological correlation of these molecular subtypes has not been performed. We sought to identify specific morphological patterns to compare with known molecular subtypes, interrogate their biological significance, and furthermore reappraise the current grading system in PDAC. DESIGN: We first assessed 86 primary, chemotherapy-naive PDAC resection specimens with matched RNA-Seq data for specific, reproducible morphological patterns. Differential expression was applied to the gene expression data using the morphological features. We next compared the differentially expressed gene signatures with previously published molecular subtypes. Overall survival (OS) was correlated with the morphological and molecular subtypes. RESULTS: We identified four morphological patterns that segregated into two components ('gland forming' and 'non-gland forming') based on the presence/absence of well-formed glands. A morphological cut-off (≥40% 'non-gland forming') was established using RNA-Seq data, which identified two groups (A and B) with gene signatures that correlated with known molecular subtypes. There was a significant difference in OS between the groups. The morphological groups remained significantly prognostic within cancers that were moderately differentiated and classified as 'classical' using RNA-Seq. CONCLUSION: Our study has demonstrated that PDACs can be morphologically classified into distinct and biologically relevant categories which predict known molecular subtypes. These results provide the basis for an improved taxonomy of PDAC, which may lend itself to future treatment strategies and the development of deep learning models.

Differential Sensing of Saccharides Based on an Array of Fluorinated Benzosiloxaborole Receptors.

Fluorinated benzosiloxaboroles-silicon congeners of benzoxaboroles, were synthesized and tested as molecular receptors for mono- and disaccharides. The receptors differed in the Lewis acidity of the boron center as well as in the number of potential binding sites. The calculated stability constants indicated different binding affinity of benzosiloxaborole derivatives towards selected saccharides, enabling their classification using a receptor array-based sensing. Unique fluorescence fingerprints were created on the basis of competitive interactions of the studied receptors with both Alizarin Red S (ARS) and tested saccharide molecules. Detailed chemometric analysis of the obtained fluorescence data (based on partial least squares-discriminant analysis and hierarchical clustering analysis) provided the differential sensing of common saccharides, in particular the differentiation between glucose and fructose. In addition, DFT calculations were carried out to shed light on the binding mechanism under different pH conditions.

Pattern-based strategic surgical capacity allocation.

Strategic allocation of limited operating room (OR) capacity to surgeons is crucial for the coordination of surgical work flow, including planning of consultation and surgery days, and staff assignment to perioperative teams. However, it is a challenging problem in practice, since the capacity allocation needs to be cyclic for schedule predictability and surgical team coordination, and also needs to satisfy surgeons' preferences. It is further complicated by the practice of surgeons sharing ORs. In this study, we propose a mathematical optimization model to coordinate capacity allocation among surgeons in order to improve the utilization of surgical capacity. We introduce the concept of capacity allocation patterns to account for schedule cyclicity and surgeons' preferences. Further, we develop a data-driven approach to coordinate OR sharing among surgeons based on their historical OR usage. The proposed methodology is applied to a case study with data from a surgical division at Mayo Clinic. Compared with the state-of-the-practice, the proposed approach shows a substantial potential in reducing the maximum number of ORs allocated daily to the division with little overtime. With a solution time of less than 0.5 s, the proposed methodology can be readily used as a decision support tool in surgical practice.

Clinical significance of the pattern-based classification in endocervical adenocarcinoma, usual and variants.

BACKGROUND: The Silva system is a pattern-based classification system that stratifies endocervical adenocarcinomas (AC) into 3 categories to assess the risk of lymph node (LN) metastasis. This study aimed to evaluate whether this novel risk stratification system is applicable to all endocervical AC, including usual and variant, and to suggest a suitable management plan for cervical AC. METHODS: We retrospectively retrieved consecutive pathology cases with a final diagnosis of endocervical AC treated via radical hysterectomy and pelvic lymphadenectomy. Specimens were classified by consensus according to the Silva system based on "pattern of invasion" as A, B, or C, further clinical/pathologic features were assessed according to pattern-based classification. RESULTS: A total of 76 cases of invasive cervical AC were evaluated. Of these, 63 (82.9%) were categorized as usual-type endocervical AC and 13 (17.1%) as special types. Among those with usual and variants, all patients with pattern A tumor had no LN metastasis and did not develop recurrence. Likewise, multivariate analysis revealed that LN metastasis and pattern C or B tumors are significant independent predictors of disease-free survival (DFS). Although pattern A tumors had no LN metastasis, they also developed complications after surgery, similar to pattern B or C tumors. CONCLUSION: Regardless of histologic subtypes, pattern A tumors had no LN metastasis and no recurrence. Thus, the Silva classification system can influence the clinical management of all types of endocervical AC. Conservative management is reasonable in all patients with endocervical AC with pattern A tumors.

Quantum Dots-Assisted 2D Fluorescence for Pattern Based Sensing of Amino Acids, Oligopeptides and Neurotransmitters.

Quantum dots (QDs) are very attractive nanomaterials for analytical chemistry, due to high photostability, large surface area featuring numerous ways of bioconjugation with biomolecules, usually high quantum yield and long decay times. Their broad absorption spectra and narrow, sharp emission spectra of size-tunable fluorescence make them ideal tools for pattern-based sensing. However, almost always they are applied for specific sensing with zero-dimensional (0D) signal reporting (only peak heights or peak shifts are considered), without taking advantage of greater amount of information hidden in 1D signal (emission spectra), or huge amount of information hidden in 2D fluorescence maps (Excitation-Emission Matrixes, EEMs). Therefore, in this work we propose opposite strategy-non-specific interactions of QDs, which are usually avoided and regarded as their disadvantage, were exploited here for 2D fluorescence fingerprinting. Analyte-specific multivariate fluorescence response of QDs is decoded with the use of Partial Least Squares-Discriminant Analysis. Even though only one type of QDs is studied, the proposed pattern-based method enables to obtain satisfactory accuracy for all studied compounds-various neurotransmitters, amino-acids and oligopeptides. This is a proof of principle of the possibility of the identification of various bioanalytes by such fluorescence fingerprinting with the use of QDs.

Pattern-based formulation: clinical case 5.

OBJECTIVE:: To demonstrate how the Pattern-based Formulation can be used to integrate biological, psychological and sociocultural factors in constructing the case formulation in a patient who developed schizophrenia and post-psychotic depression. CONCLUSIONS:: Three new patterns are introduced and used to construct a comprehensive case formulation. This expands the suite of patterns in the pattern-based method of psychiatric case formulation, and further demonstrates its broad utility as an educational resource in psychiatry training.

Case formulation using Pattern-based Formulation (PBF) methodology: clinical case 2.

OBJECTIVES: To teach psychiatric case formulation; to build a repertoire of patterns that can be reused as building blocks in constructing case formulations. METHOD: Pattern-based Formulation. RESULTS: Demonstration of a case formulation and introducing three patterns. CONCLUSION: The demonstration will assist learning case formulation using the Pattern-based Formulation, while the three patterns introduced can be reused when formulating relevant cases.

Case formulation using pattern-based formulation methodology: clinical case 3.

OBJECTIVE: The objective of this study is to determine if pattern-based formulation (PBF) can accurately contribute to case formulation. CONCLUSIONS: The application of three PBFs accurately contributed to the development of this patient's case formulation. The case formulation demonstrated here, and the patterns introduced in this paper, will serve as educational materials for teaching psychiatric case formulation.

Case formulation using pattern-based formulation (PBF) methodology: clinical case 4.

OBJECTIVES: To provide a further example of the utility of the pattern-based model in formulation, and to introduce some further patterns. METHODS: A case study was carried out using the Pattern-based Formulation (PBF). RESULTS: Based on the case of a patient with a past history of trauma who developed late onset somatic symptoms, post-traumatic stress disorder and major depression, the PBF approach enabled development of a comprehensive formulation to explain the patient's current presentation. Four patterns were utilised. CONCLUSIONS: The PBF method of using patterns as building blocks enables development of a psychobiological formulation that can accommodate considerable complexity. PBF represents a broadly applicable method that may assist psychiatry trainees and others to develop good quality formulations.

A Glimpse of Our Journey into the Design of Optical Probes in Self-assembled Surfactant Aggregates.

Dynamic self-assembling amphiphilic surfactant molecules, popularly known as "micelles", have received widespread attention, due to their ability to modulate the photophysical properties of various organic dyes upon encapsulation. Along with their well-known use as cleaning agents, catalysts in organic reactions, and even for drug delivery purposes, these surfactant assemblies also show promising pertinence in the recognition of both ionic and nonionic targeted analytes. Low micropolarity and relatively hydrophobic environments promote their interaction with ionic analytes, whereas neutral species mostly affect the aggregation pattern of the probe molecules upon partitioning inside the micellar hydrophobic milieu. The environment-sensitive nature of micelle-based self-assembled probes also prompts us to devise new sensor arrays for the recognition of multiple analytes. While this account will largely focus on our own work in developing surfactant-triggered self-assembled sensors, our findings have been placed in the context of the relevant contributions from others during their strategic evolution.

New pattern-based personalized risk stratification system for endocervical adenocarcinoma with important clinical implications and surgical outcome.

We present a recently introduced three tier pattern-based histopathologic system to stratify endocervical adenocarcinoma (EAC) that better correlates with lymph node (LN) metastases than FIGO staging alone, and has the advantage of safely predicting node-negative disease in a large proportion of EAC patients. The system consists of stratifying EAC into one of three patterns: pattern A tumors characterized by well-demarcated glands frequently forming clusters or groups with relative lobular architecture and lacking destructive stromal invasion or lymphovascular invasion (LVI), pattern B tumors demonstrating localized destructive invasion (small clusters or individual tumor cells within desmoplastic stroma often arising from pattern A glands), and pattern C tumors with diffusely infiltrative glands and associated desmoplastic response. Three hundred and fifty-two cases were included; mean follow-up 52.8 months. Seventy-three patients (21%) had pattern A tumors; all were stage I and there were no LN metastases or recurrences. Pattern B was seen in 90 tumors (26%); all were stage I and LVI was seen in 24 cases (26.6%). Nodal disease was found in only 4 (4.4%) pattern B tumors (one IA2, two IB1, one IB not further specified (NOS)), each of which showed LVI. Pattern C was found in 189 cases (54%), 117 had LVI (61.9%) and 17% were stage II or greater. Forty-five (23.8%) patients showed LN metastases (one IA1, 14 IB1, 5 IB2, 5 IB NOS, 11 II, 5 III and 4 IV) and recurrences were recorded in 41 (21.7%) patients. This new risk stratification system identifies a subset of stage I patients with essentially no risk of nodal disease, suggesting that patients with pattern A tumors can be spared lymphadenectomy. Patients with pattern B tumors rarely present with LN metastases, and sentinel LN examination could potentially identify these patients. Surgical treatment with nodal resection is justified in patients with pattern C tumors.