Application of European Society of Cardiology guidelines for evaluating acute coronary syndrome risk in low-risk patients with cocaine-associated chest pain: Findings from the RISK study - An observational analysis

Background

Cocaine was the drug of choice in 4.7 % of all recreational drug-related emergency department visits. Of these patients, 40 % present with cocaine-associated chest pain, of whom 4.7 % develop an acute coronary syndrome. The American Heart Association recommends a 12-hour observation period for these patients.

Objective

This study primarily aimed to ascertain whether the European Society of Cardiology non-ST-elevation myocardial infarction guidelines can be safely applied to rule-out acute coronary syndrome in low-risk patients with cocaine-associated chest pain.

Methods

For this prospective observational cohort study, patients, aged 18-45 years old, who presented with cocaine-associated chest pain and were risk stratified as low risk according to the European Society of Cardiology non-ST-elevation myocardial infarction guidelines and therefore discharged home without prolonged observation period, were included. They were followed to assess major adverse cardiac events four weeks after presentation to the emergency department or chest pain unit. Cocaine use was confirmed with urine toxicology screening.

Results

A total of 107 patients were included and analysed. The accuracy of the self-reported history of recent cocaine use was 94 %. Post-discharge cocaine use persisted among 32 % of patients. None of the included 107 patients died and major adverse cardiac event within four weeks did not occur among 97 patients with available data regarding MACE.

Conclusion

Ruling out an acute coronary syndrome using the European Society of Cardiology non-ST-elevation myocardial infarction guidelines is likely to be safe for patients with cocaine-associated chest pain, however this study was underpowered to reach definitive conclusions.

CRISPR/Cas9-mediated knock-in of a fluorescent reporter into the target locus of interest in human pluripotent stem cells

The method presented herein is associated with the Lab Resource article titled "Generation of αMHC-EGFP knock-in in human pluripotent stem cell line, SNUe003-A-3, using CRISPR/CAS9-based gene targeting" [1]. The cardiac muscle-specific protein, α-myosin heavy chain (αMHC), is encoded by the human MYH6 gene, which is expressed in both the atria and ventricles during embryonic development and is predominantly expressed in the atria after birth [2]. Herein, the methods used to achieve CRISPR/SpCas9-mediated introduction of an EGFP reporter into αMHC, the target locus in human pluripotent stem cells (hPSCs) for cardiac lineage tracing and clinical cell sorting are described. The CRISPR-Cas9 system enables efficient replacement of the stop codon in the last exon of αMHC with a 2A non-joining peptide (T2A)-EGFP cassette. First, hPSCs are transfected with the donor construct and Cas9/sgRNA plasmids via electroporation and selected with neomycin for approximately 3 weeks. Thereafter, the established cell line exhibits typical characteristics of human embryonic stem cells (hESCs). When these cells differentiate into cardiomyocytes, the expression of EGFP is confirmed using confocal microscopy, flow cytometry analysis, and immunostaining.•The line enables monitoring of cell maturation events during human cardiac development.•The line is a valuable platform for cardiotoxicity tests and drug screening.•This method has already been employed in two original studies, as previously reported for reporter cell line generation using CRISPR/Cas9.

PostBP: A Python library to analyze outputs from wildfire growth models

Wildfire is an important natural disturbance agent in Canadian forests, but it has also caused significant economic damage nationwide. Spatial fire growth models have emerged as important tools for representing wildfire dynamics across diverse landscapes, enabling the mapping of key wildfire hazard metrics such as location-specific burn probabilities or likelihoods of fire ignition. While these summary metrics have gained popularity, they often fall short in capturing the directional spread of wildfires and their potential spread distances. The metrics depicting the directional spread of wildfire can be derived from raw outputs generated with fire growth models, such as the perimeters and ignition locations of individual fires, but extracting this information requires complex data processing. To address this data gap, we present PostBP, an open-source Python package designed for post-processing the raw outputs of fire growth models - the ignition locations and perimeters of individual fires simulated over multiple stochastic iterations - into a matrix of fire spread likelihoods between all pairs of forest patches in a landscape. The PostBP also generates several other summary outputs, such as the source-sink ratio and the fire spread rose diagram. We provide an overview of PostBP's capabilities and demonstrate its practical application to a forested landscape.•Wildfire growth models generate large amounts of outputs, which are hard to summarize for practical decision-making.•The PostBP package calculates the summary metrics characterizing the directional spread of wildfires.•The fire risk summaries generated with PostBP can support the assessments of wildfire risk and mitigation measures.

MiRNA-3163 limits ovarian cancer stem-like cells via targeting SOX-2 transcription factor

Cancer stem cells (CSCs) are pivotal in both cancer progression and the acquisition of drug resistance. MicroRNAs (miRNAs) play a crucial role in modulating CSC properties and are being explored as potential targets for therapeutic interventions. MiR-3163 is primarily known for its tumor suppressive properties in various human malignancies, with lower expression reported across different cancer types. However, its role in regulating the ovarian CSC phenotype and the underlying mechanism remain largely unknown. Here, we report a remarkable downregulation of miR-3163 in ovarian cancer stem-like cells (CSLCs). Enforced expression of miR-3163 in ovarian adherent and CSLCs, significantly disrupts the stemness phenotype. Moreover, downregulation of miR-3163 expression in ovarian cancer cells (OV2008 and OVCAR-3) inhibits the stem-like cells characterized by CD44+CD117+ expression. Sphere formation assay results reveal that overexpression of miR-3163 in ovarian cancer cells significantly inhibits spheroid formation ability, confirming the regulatory properties of miR-3163 on ovarian CSLCs. Mechanistic investigation reveals that miR-3163 depletes ovarian CSLCs via targeting SOX-2. Furthermore, we establish SOX-2 as a direct target of miR-3163 through dual-luciferase assay. Taken together, our study demonstrates that overexpression of miR-3163 could be a promising strategy for efficiently eradicating the CSC population to prevent chemoresistance and tumor relapse in ovarian cancer patients.

Design, proof-of-concept of single robotic hair transplant mechanisms for both harvest and implant of hair grafts

The design and development of a prototype for a singular robotic hair transplant system capable of harvesting and implanting hair grafts were executed in this study. To establish a proof-of-concept for hair transplant procedures involving harvesting and implantation, a test system using a spherical phantom of the scalp was selected. The developed prototype of the robotic hair transplant system demonstrates the potential to reduce the duration that grafts remain without a blood supply, thereby minimizing hair graft damage. Additionally, the overall operation time for follicular unit extraction is comparatively shorter than that of conventional systems. Results from the robot vision tests indicate an 89.6% accuracy for hair graft detection with a 4 mm hair length phantom and 97.4% for a 2 mm hair length phantom. In the robot position control test, the root mean square error was found to be 1.268°, with a standard error of the mean of 0.203°. These outcomes suggest that the proposed system performs effectively under the conditions of a spherical phantom with a 2 mm hair length and a 5 mm distance between harvests.

Development and refinement of a communication guide to help young adults in Texas reconnect and reduce loneliness

Objective

Loneliness among young people is a contemporary public health crisis exacerbated by the COVID-19 pandemic. The present research examined the development of a modest yet meaningful health communication intervention resource that would become an asset in a larger health campaign: a communication guide aimed at helping young people reach out and reconnect with others.

Methods

Study One established the need for a loneliness intervention in Texas with survey data (N = 795). A communication guide was developed based on research and theory. Study Two employed focus group interviews with potential audience members (N = 31) to critically assess and inform revisions to the communication guide.

Results

Study One results indicated that a substantial proportion of young adult Texans felt as though their social connection had decreased and their loneliness had increased since the onset of the pandemic. Themes in focus group responses from Study Two suggested several strengths of the communication guide and some opportunities for revision.

Conclusion

A communication guide with tips for reconnecting could be a valuable tool to empower young people and promote social connection.

Innovation

This study involved the development and refinement of a new communication resource that was informed by a priority audience of a major health communication campaign.

Cluster-based radiomics reveal spatial heterogeneity of bevacizumab response for treatment of radiotherapy-induced cerebral necrosis

Background

Bevacizumab is used in the treatment of radiation necrosis (RN), which is a debilitating toxicity following head and neck radiotherapy. However, there is no biomarker to predict if a patient would respond to bevacizumab.

Purpose

We aimed to develop a cluster-based radiomics approach to characterize the spatial heterogeneity of RN and map their responses to bevacizumab.

Methods

118 consecutive nasopharyngeal carcinoma patients diagnosed with RN were enrolled. We divided 152 lesions from the patients into 101 for training, and 51 for validation. We extracted voxel-level radiomics features from each lesion segmented on T1-weighted+contrast and T2 FLAIR sequences of pre- and post-bevacizumab magnetic resonance images, followed by a three-step analysis involving individual- and population-level clustering, before delta-radiomics to derive five radiomics clusters within the lesions. We tested the association of each cluster with response to bevacizumab and developed a clinico-radiomics model using clinical predictors and cluster-specific features.

Results

71 (70.3%) and 34 (66.7%) lesions had responded to bevacizumab in the training and validation datasets, respectively. Two radiomics clusters were spatially mapped to the edema region, and the volume changes were significantly associated with bevacizumab response (OR:11.12 [95% CI: 2.54-73.47], P = 0.004; and 1.63[1.07-2.78], P = 0.042). The combined clinico-radiomics model based on textural features extracted from the most significant cluster improved the prediction of bevacizumab response, compared with a clinical-only model (AUC:0.755 [0.645-0.865] to 0.852 [0.764-0.940], training; 0.708 [0.554-0.861] to 0.816 [0.699-0.933], validation).

Conclusion

Our radiomics approach yielded intralesional resolution, enabling a more refined feature selection for predicting bevacizumab efficacy in the treatment of RN.

Sleep health among medical students in Abakaliki Nigeria: A descriptive study

Background

Sleep health focuses on those measurable characteristics of sleep that are most clearly associated with physical, mental, and neurobehavioral well-being, and not necessarily the absence of sleep disorder. Sleep health is characterised by subjective satisfaction, appropriate timing, adequate duration, high efficiency, and sustained alertness during waking hours. Adequate and restful sleep is particularly crucial for medical students, who face unique challenges due to the demanding nature of their academic and clinical responsibilities. There is limited data on sleep health among medical students in Nigeria.

Objectives

This research study investigated the pattern of sleep health among medical students in Abakaliki Nigeria.

Methods

This cross-sectional observational study was undertaken among the medical students of 2 public institutions in Abakaliki Nigeria from 16th to 23rd June 2023.

Results

Out of the 288 medical students (males- 53.1 %, females- 46.9 %), good sleep health was recorded in 6.6 %. The mean SATED sleep score was 4.9 ± 1.7 (male- 4.9 ± 1.8, female- 4.9 ± 1.6) (p-value = 1) and it was significantly lower among the final-year students. Age difference, sex difference, and presence of chronic headache did not significantly affect the SATED sleep score. The mean sleep duration was 6.1 ± 1.5 hours (male- 6.1 ± 1.6, female- 6.0 ± 1.4). Sleep duration (54.5 %) had the best rating while sleep efficiency (44 %) had the lowest rating among the assessed sleep domains.

Conclusion

Sleep health is poor among medical students in Abakaliki Nigeria and significantly poorer among final-year medical students.

Promotion of hMDSC differentiation by combined action of scaffold material and TGF-β superfamily growth factors

Objective

Herein we propose a combined action of collagen type I (CA) or synthetic collagen-like-peptide functionalized with the cell adhesive RGD motif (PEG-CLP-RGD) hydrogels and selected growth factors to promote chondrogenic differentiation of human muscle-derived stem cells (hMDSCs) under normal and reduced oxygen conditions.

Methods

hMDSCs were set for differentiation towards chondrogenic lineage using BMP-7 and TGF-β3. Cells were seeded onto hydrogels loaded with growth factors (75ng/scaffold) and cultured for 28 days under normal (21%) and severe hypoxic (1%) conditions. Chondrogenesis was evaluated by monitoring collagen type II and GAG deposition, and quantification of ACAN expression by RT-PCR.

Results

Sustained release of TGFβ3 from the hydrogels was observed, 8.7 ± 0.5% of the initially loaded amount diffused out after 24 h from both substrates. For the BMP-7 growth factor, 14.8 ± 0.3% and 18.2 ± 0.6% of the initially loaded amount diffused out after 24 h from CA and CLP-RGD, respectively. The key findings of this study are: i) the self-supporting hydrogels themselves can stimulate hMDSC chondrogenesis by inducing gene expression of cartilage-specific proteoglycan aggrecan and ECM production; ii) the effect of dual BMP-7 and TGF-β3 loading was more pronounced on CA hydrogel under normal oxygen conditions; iii) dual loading on PEG-CLP-RGD hydrogels did not have the synergistic effect, TGF-β3 was more effective under both oxygen conditions; iv) BMP-7 can improve chondrogenic effect of TGF-β3 on CA scaffolds, and hydrogels loaded with both growth factors can induce cartilage formation in hMDSC cultures.

Conclusion

Our results support the potential strategy of combining implantable hydrogels functionalized with differentiation factors toward improving cartilaginous repair.

Dental and oral management in the perioperative period of surgery: A scoping review

Dental and oral management (DOM) is a long-established treatment modality. This scoping review aimed to narratively review previous studies, examine the effects of perioperative DOM, and identify the available evidence. A literature search was conducted using the PubMed electronic database for studies published between January 1, 2000, and March 8, 2022. The search yielded 43 studies, most of which were published in the last 10 years. The results of this study confirmed that improved perioperative oral hygiene is effective in preventing postoperative pneumonia. Our results also suggested that preoperative DOM is effective in preventing postoperative surgical site infections. Perioperative DOM is effective in reducing the incidence of postoperative pneumonia, SSI, and postsurgical complications. Further studies are needed to elucidate the various mechanism of DOM and to examine efficient intervention methods and timing.

RBProkCNN: Deep learning on appropriate contextual evolutionary information for RNA binding protein discovery in prokaryotes

RNA-binding proteins (RBPs) are central to key functions such as post-transcriptional regulation, mRNA stability, and adaptation to varied environmental conditions in prokaryotes. While the majority of research has concentrated on eukaryotic RBPs, recent developments underscore the crucial involvement of prokaryotic RBPs. Although computational methods have emerged in recent years to identify RBPs, they have fallen short in accurately identifying prokaryotic RBPs due to their generic nature. To bridge this gap, we introduce RBProkCNN, a novel machine learning-driven computational model meticulously designed for the accurate prediction of prokaryotic RBPs. The prediction process involves the utilization of eight shallow learning algorithms and four deep learning models, incorporating PSSM-based evolutionary features. By leveraging a convolutional neural network (CNN) and evolutionarily significant features selected through extreme gradient boosting variable importance measure, RBProkCNN achieved the highest accuracy in five-fold cross-validation, yielding 98.04% auROC and 98.19% auPRC. Furthermore, RBProkCNN demonstrated robust performance with an independent dataset, showcasing a commendable 95.77% auROC and 95.78% auPRC. Noteworthy is its superior predictive accuracy when compared to several state-of-the-art existing models. RBProkCNN is available as an online prediction tool (https://iasri-sg.icar.gov.in/rbprokcnn/), offering free access to interested users. This tool represents a substantial contribution, enriching the array of resources available for the accurate and efficient prediction of prokaryotic RBPs.

Integration of single-nucleus and exosome RNA sequencing dissected inter-cellular communication and biomarkers in pancreatic ductal adenocarcinoma

Background

Mounting evidence underscores the importance of cell communication within the tumor microenvironment, which is pivotal in tumor proliferation, invasion, and metastasis. Exosomes play a crucial role in cell-to-cell communication. Although single-cell RNA sequencing (scRNA-seq) provides insights into individual cell transcriptional characteristics, it falls short of comprehensively capturing exosome-mediated intercellular communication.

Method

We analyzed Pancreatic Ductal Adenocarcinoma (PDAC) tissues, separating supernatant and precipitate for exosome purification and single-cell nucleus suspension. We then constructed Single-nucleus RNA sequencing (snRNA-seq) and small RNA-seq libraries from these components. Our bioinformatic analysis integrated these sequences with ligand-receptor analysis and public miRNA data to map the cell communication network.

Results

We established intercellular communication networks using bioinformatic analysis to track exosome miRNA effects and ligand-receptor pairs. Significantly, hsa-miR-1293 emerged as a prognostic biomarker for pancreatic cancer, linked to immune evasion, increased myeloid-derived suppressor cells, and poorer prognosis. Targeting this miRNA may enhance anti-tumor immunity and improve outcomes.

Conclusion

Our study offers a novel approach to constructing intercellular communication networks using snRNA-seq and exosome-small RNA sequencing. By integrating miRNA tracing with ligand-receptor analysis, we illuminate the complex interactions in the pancreatic cancer microenvironment, highlighting the pivotal role of miRNAs and identifying potential biomarkers and therapeutic targets.

On image search in histopathology

Pathology images of histopathology can be acquired from camera-mounted microscopes or whole-slide scanners. Utilizing similarity calculations to match patients based on these images holds significant potential in research and clinical contexts. Recent advancements in search technologies allow for implicit quantification of tissue morphology across diverse primary sites, facilitating comparisons, and enabling inferences about diagnosis, and potentially prognosis, and predictions for new patients when compared against a curated database of diagnosed and treated cases. In this article, we comprehensively review the latest developments in image search technologies for histopathology, offering a concise overview tailored for computational pathology researchers seeking effective, fast, and efficient image search methods in their work.

Comparative evaluation of slide scanners, scan settings, and cytopreparations for digital urine cytology

Background

Acquiring well-focused digital images of cytology slides with scanners can be challenging due to the 3-dimensional nature of the slides. This study evaluates performances of whole-slide images (WSIs) obtained from 2 different cytopreparations by 2 distinct scanners with 3 focus modes.

Methods

Fourteen urine specimens were collected from patients with urothelial carcinoma. Each specimen was equally divided into 2 portions, prepared with Cytospin and ThinPrep methods and scanned for WSIs using Leica (Aperio AT2) and Hamamatsu (NanoZoomer S360) scanners, respectively. The scan settings included 3 focus modes (default, semi-auto, and manual) for single-layer scanning, along with a manual focus mode for 21 Z-layers scanning. Performance metrics were evaluated including scanning success rate, artificial intelligence (AI) algorithm-inferred atypical cell numbers and coverage rate (atypical cell numbers in single or multiple Z-layers divided by the total atypical cell numbers in 21 Z-layers), scanning time, and image file size.

Results

The default mode had scanning success rates of 85.7% or 92.9%, depending on the scanner used. The semi-auto mode increased success to 92.9% or 100%, and manual even further to 100%. However, these changes did not affect the standardized median atypical cell numbers and coverage rates. The selection of scanners, cytopreparations, and Z-stacking influenced standardized median atypical cell numbers and coverage rates, scanning times, and image file sizes.

Discussion

Both scanners showed satisfactory scanning. We recommend using semi-auto or manual focus modes to achieve a scanning success rate of up to 100%. Additionally, a minimum of 9-layer Z-stacking at 1 μm intervals is required to cover 80% of atypical cells. These advanced focus methods do not impact the number of atypical cells or their coverage rate. While Z-stacking enhances the AI algorithm's inferred quantity and coverage rates of atypical cells, it simultaneously results in longer scanning times and larger image file sizes.

Biological characteristics of Muse cells derived from MenSCs and their application in acute liver injury and intracerebral hemorrhage diseases

The increasing interest in multilineage differentiating stress-enduring (Muse) cells within the field of regenerative medicine is attributed to their exceptional homing capabilities, prolonged viability in adverse conditions, and enhanced three-germ-layer differentiate ability, surpassing their parent mesenchymal stem cells. Given their abundant sources, non-invasive collection procedure, and periodic availability, human menstrual blood-derived endometrium stem cells (MenSCs) have been extensively investigated as a potential resource for stem cell-based therapies. However, there is no established modality to isolate Muse cells from MenSCs and disparity in gene expression profiles between Muse cells and MenSCs remain unknown. In this study, Muse cells were isolated from MenSCs by long-time trypsin incubation method. Muse cells expressed pluripotency markers and could realize multilineage differentiation in vitro. Compared with MenSCs, Muse cells showed enhanced homing ability and superior therapeutic efficacy in animal models of acute liver injury (ALI) and intracerebral hemorrhage (ICH). Furthermore, the RNA-seq analysis offers insights into the mechanism underlying the disparity in trypsin resistance and migration ability between Muse and MenSCs cells. This research offers a significant foundation for further exploration of cell-based therapies using MenSCs-derived Muse cells in the context of various human diseases, highlighting their promising application in the field of regenerative medicine.

Parents' information needs during the first year at home with their very premature born child; a qualitative study

Objective

To obtain insights into parents' information needs during the first year at home with their very preterm (VP) born infant.

Methods

We conducted semi-structured interviews with parents of VP infants participating in a post-discharge responsive parenting intervention (TOP program). Online interviews were audiotaped and transcribed verbatim. Inductive thematic analysis was performed by two independent coders.

Results

Ten participants were interviewed and had various and changing information needs during the developmental trajectory of their infant. Three main themes emerged; (1) Help me understand and cope, (2) Be fully responsible for my baby, and (3) Teach me to do it myself. Available and used sources, such as the Internet, did not meet their information needs. Participants preferred their available and knowledgeable healthcare professionals for reassurance, tailored information, and practical guidance.

Conclusion

This study identified parents' information needs during the first year at home with their VP infant and uncovered underlying re-appearing needs to gain confidence in child-caring abilities and autonomy in decision-making about their infants' care.

Innovation

This study provides valuable information for healthcare professionals and eHealth developers to support parental self-efficacy during the first year after preterm birth.

Comprehensive integration of single-cell RNA and transcriptome RNA sequencing to establish a pyroptosis-related signature for improving prognostic prediction of gastric cancer

Cell pyroptosis, a Gasdermin-dependent programmed cell death characterized by inflammasome, plays a complex and dynamic role in Gastric cancer (GC), a serious threat to human health. Therefore, the value of pyroptosis-related genes (PRGs) as prognostic biomarkers and therapeutic indicators for patients needs to be exploited in GC. This study integrates single-cell RNA sequencing (scRNA-seq) dataset GSE183904 with GC transcriptome data from the TCGA database, focusing on the expression and distribution of PRGs in GC at the single-cell level. The prognostic signature of PRGs was established by using Cox and LASSO analyses. The differences in long-term prognosis, immune infiltration, mutation profile, CD274 and response to chemotherapeutic drugs between the two groups were analyzed and evaluated. A tissue array was used to verify the expression of six PRGs, CD274, CD163 and FoxP3. C12orf75, VCAN, RGS2, MKNK2, SOCS3 and TNFAIP2 were successfully screened out to establish a signature to potently predict the survival time of GC patients. A webserver (https://pumc.shinyapps.io/GastricCancer/) for prognostic prediction in GC patients was developed based on this signature. High-risk score patients typically had worse prognoses, resistance to classical chemotherapy, and a more immunosuppressive tumor microenvironment. VCAN, TNFAIP2 and SOCS3 were greatly elevated in the GC while RGS2 and MKNK2 were decreased in the tumor samples. Further, VCAN was positively related to the infiltrations of Tregs and M2 TAMs in GC TME and the CD274 in tumor cells. In summary, a potent pyroptosis-related signature was established to accurately forecast the survival time and treatment responsiveness of GC patients.

Single-cell multi-omics in the study of digestive system cancers

Digestive system cancers are prevalent diseases with a high mortality rate, posing a significant threat to public health and economic burden. The diagnosis and treatment of digestive system cancer confront conventional cancer problems, such as tumor heterogeneity and drug resistance. Single-cell sequencing (SCS) emerged at times required and has developed from single-cell RNA-seq (scRNA-seq) to the single-cell multi-omics era represented by single-cell spatial transcriptomics (ST). This article comprehensively reviews the advances of single-cell omics technology in the study of digestive system tumors. While analyzing and summarizing the research cases, vital details on the sequencing platform, sample information, sampling method, and key findings are provided. Meanwhile, we summarize the commonly used SCS platforms and their features, as well as the advantages of multi-omics technologies in combination. Finally, the development trends and prospects of the application of single-cell multi-omics technology in digestive system cancer research are prospected.

Molecular interactions between a diphenyl scaffold and PED/PEA15: Implications for type II diabetes therapeutics targeting PED/PEA15 - Phospholipase D1 interaction

In a recent study, we have identified BPH03 as a promising scaffold for the development of compounds aimed at modulating the interaction between PED/PEA15 (Phosphoprotein Enriched in Diabetes/Phosphoprotein Enriched in Astrocytes 15) and PLD1 (phospholipase D1), with potential applications in type II diabetes therapy. PED/PEA15 is known to be overexpressed in certain forms of diabetes, where it binds to PLD1, thereby reducing insulin-stimulated glucose transport. The inhibition of this interaction reestablishes basal glucose transport, indicating PED as a potential target of ligands capable to recover glucose tolerance and insulin sensitivity. In this study, we employ computational methods to provide a detailed description of BPH03 interaction with PED, evidencing the presence of a hidden druggable pocket within its PLD1 binding surface. We also elucidate the conformational changes that occur during PED interaction with BPH03. Moreover, we report new NMR data supporting the in-silico findings and indicating that BPH03 disrupts the PED/PLD1 interface displacing PLD1 from its interaction with PED. Our study represents a significant advancement toward the development of potential therapeutics for the treatment of type II diabetes.

Deciphering and designing microbial communities by genome-scale metabolic modelling

Microbial communities are shaped by the complex interactions among organisms and the environment. Genome-scale metabolic models (GEMs) can provide deeper insights into the complexity and ecological properties of various microbial communities, revealing their intricate interactions. Many researchers have modified GEMs for the microbial communities based on specific needs. Thus, GEMs need to be comprehensively summarized to better understand the trends in their development. In this review, we summarized the key developments in deciphering and designing microbial communities using different GEMs. A timeline of selected highlights in GEMs indicated that this area is evolving from the single-strain level to the microbial community level. Then, we outlined a framework for constructing GEMs of microbial communities. We also summarized the models and resources of static and dynamic community-level GEMs. We focused on the role of external environmental and intracellular resources in shaping the assembly of microbial communities. Finally, we discussed the key challenges and future directions of GEMs, focusing on the integration of GEMs with quorum sensing mechanisms, microbial ecology interactions, machine learning algorithms, and automatic modeling, all of which contribute to consortia-based applications in different fields.

MKG-GC: A multi-task learning-based knowledge graph construction framework with personalized application to gastric cancer

Over the past decade, information for precision disease medicine has accumulated in the form of textual data. To effectively utilize this expanding medical text, we proposed a multi-task learning-based framework based on hard parameter sharing for knowledge graph construction (MKG), and then used it to automatically extract gastric cancer (GC)-related biomedical knowledge from the literature and identify GC drug candidates. In MKG, we designed three separate modules, MT-BGIPN, MT-SGTF and MT-ScBERT, for entity recognition, entity normalization, and relation classification, respectively. To address the challenges posed by the long and irregular naming of medical entities, the MT-BGIPN utilized bidirectional gated recurrent unit and interactive pointer network techniques, significantly improving entity recognition accuracy to an average F1 value of 84.5% across datasets. In MT-SGTF, we employed the term frequency-inverse document frequency and the gated attention unit. These combine both semantic and characteristic features of entities, resulting in an average Hits@ 1 score of 94.5% across five datasets. The MT-ScBERT integrated cross-text, entity, and context features, yielding an average F1 value of 86.9% across 11 relation classification datasets. Based on the MKG, we then developed a specific knowledge graph for GC (MKG-GC), which encompasses a total of 9129 entities and 88,482 triplets. Lastly, the MKG-GC was used to predict potential GC drugs using a pre-trained language model called BioKGE-BERT and a drug-disease discriminant model based on CNN-BiLSTM. Remarkably, nine out of the top ten predicted drugs have been previously reported as effective for gastric cancer treatment. Finally, an online platform was created for exploration and visualization of MKG-GC at https://www.yanglab-mi.org.cn/MKG-GC/.

Modeling measles transmission in adults and children: Implications to vaccination for eradication

Despite the availability of successful vaccines, measles outbreaks have occurred frequently in recent years, presumably due to the lack of proper vaccination implementation. Moreover, measles cases in adult groups, albeit small in number, indicate that the previously neglected adult group may need to be brought into vaccine coverage to achieve WHO's goal of measles eradication from the globe. In this study, we develop a novel transmission dynamics model to describe measles cases in adults and children to evaluate the role of adult infection in persistent measles cases and vaccination programs for eradication. Analysis of our model, validated by measles cases from outbreaks in Nepal, provides the vaccination reproduction number (conditions for measles eradication or persistence) and the role of contact network size. Our results highlight that while children are primary targets for measles outbreaks, a small number of infections in adults may act as a reservoir for measles, causing obstacles to eradication. Furthermore, our model analysis shows that while impactful controls can be achieved by children-focused vaccines, a combined adult-child vaccination program may help assert eradication of the disease.

Schistosomiasis transmission in Zimbabwe: Modelling based on machine learning

Zimbabwe, located in Southern Africa, faces a significant public health challenge due to schistosomiasis. We investigated this issue with emphasis on risk prediction of schistosomiasis for the entire population. To this end, we reviewed available data on schistosomiasis in Zimbabwe from a literature search covering the 1980-2022 period considering the potential impact of 26 environmental and socioeconomic variables obtained from public sources. We studied the population requiring praziquantel with regard to whether or not mass drug administration (MDA) had been regularly applied. Three machine-learning algorithms were tested for their ability to predict the prevalence of schistosomiasis in Zimbabwe based on the mean absolute error (MAE), the root mean squared error (RMSE) and the coefficient of determination (R2). The findings revealed different roles of the 26 factors with respect to transmission and there were particular variations between Schistosoma haematobium and S. mansoni infections. We found that the top-five correlation factors, such as the past (rather than current) time, unsettled MDA implementation, constrained economy, high rainfall during the warmest season, and high annual precipitation were closely associated with higher S. haematobium prevalence, while lower elevation, high rainfall during the warmest season, steeper slope, past (rather than current) time, and higher minimum temperature in the coldest month were rather related to higher S. mansoni prevalence. The random forest (RF) algorithm was considered as the formal best model construction method, with MAE = 0.108; RMSE = 0.143; and R2 = 0.517 for S. haematobium, and with the corresponding figures for S. mansoni being 0.053; 0.082; and 0.458. Based on this optimal model, the current total schistosomiasis prevalence in Zimbabwe under MDA implementation was 19.8%, with that of S. haematobium at 13.8% and that of S. mansoni at 7.1%, requiring annual MDA based on a population of 3,003,928. Without MDA, the current total schistosomiasis prevalence would be 23.2%, that of S. haematobium 17.1% and that of S. mansoni prevalence at 7.4%, requiring annual MDA based on a population of 3,521,466. The study reveals that MDA alone is insufficient for schistosomiasis elimination, especially that due to S. mansoni. This study predicts a moderate prevalence of schistosomiasis in Zimbabwe, with its elimination requiring comprehensive control measures beyond the currently used strategies, including health education, snail control, population surveillance and environmental management.

Characterizing drought prediction with deep learning: A literature review

Drought prediction is a complex phenomenon that impacts human activities and the environment. For this reason, predicting its behavior is crucial to mitigating such effects. Deep learning techniques are emerging as a powerful tool for this task. The main goal of this work is to review the state-of-the-art for characterizing the deep learning techniques used in the drought prediction task. The results suggest that the most widely used climate indexes were the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI). Regarding the multispectral index, the Normalized Difference Vegetation Index (NDVI) is the indicator most utilized. On the other hand, countries with a higher production of scientific knowledge in this area are located in Asia and Oceania; meanwhile, America and Africa are the regions with few publications. Concerning deep learning methods, the Long-Short Term Memory network (LSTM) is the algorithm most implemented for this task, either implemented canonically or together with other deep learning techniques (hybrid methods). In conclusion, this review reveals a need for more scientific knowledge about drought prediction using multispectral indices and deep learning techniques in America and Africa; therefore, it is an opportunity to characterize the phenomenon in developing countries.

Analysis of the spatial and morphological characteristics of oligodendrocytes from images of in vitro culture

Oligodendrocytes (OLs) are glial cells responsible for the formation of myelin sheaths in the central nervous system. The characteristic features of the oligodendrocyte lineage, ranging from proliferative and migratory oligodendrocyte progenitor cells (OPC) to myelinating mature OLs, can be observed in vitro cultures of OL lineage cells. Here, we introduce a method for analyzing the spatial distribution of OPCs, which reflects their capacity for proliferation and migration, and the morphological complexity of mature OLs, which reflects their capacity for myelin formation, from immunostaining images of in vitro OL cultures. Through the methods described, we have demonstrated the tendency for OPCs to cluster in an environment with epidermal growth factor (EGF), and the differing morphological complexity of mature OLs according to culture medium and duration of differentiation.•The proliferative and migratory characteristics of OPCs can be evaluated by analyzing their spatial distribution.•The myelin-forming capacity of mature OLs can be measured by analyzing their morphological complexity.•Image-based analyses may be a substitute for more convoluted experiments to assess OL function.