Experimental and computational biophysics to identify vasodilator drugs targeted at TRPV2 using agonists based on the probenecid scaffold

TRP channels are important pharmacological targets in physiopathology. TRPV2 plays distinct roles in cardiac and neuromuscular function, immunity, and metabolism, and is associated with pathologies like muscular dystrophy and cancer. However, TRPV2 pharmacology is unspecific and scarce at best. Using in silico similarity-based chemoinformatics we obtained a set of 270 potential hits for TRPV2 categorized into families based on chemical nature and similarity. Docking the compounds on available rat TRPV2 structures allowed the clustering of drug families in specific ligand binding sites. Starting from a probenecid docking pose in the piperlongumine binding site and using a Gaussian accelerated molecular dynamics approach we have assigned a putative probenecid binding site. In parallel, we measured the EC50 of 7 probenecid derivatives on TRPV2 expressed in Pichia pastoris using a novel medium-throughput Ca2+ influx assay in yeast membranes together with an unbiased and unsupervised data analysis method. We found that 4-(piperidine-1-sulfonyl)-benzoic acid had a better EC50 than probenecid, which is one of the most specific TRPV2 agonists to date. Exploring the TRPV2-dependent anti-hypertensive potential in vivo, we found that 4-(piperidine-1-sulfonyl)-benzoic acid shows a sex-biased vasodilator effect producing larger vascular relaxations in female mice. Overall, this study expands the pharmacological toolbox for TRPV2, a widely expressed membrane protein and orphan drug target.

Identification and validation of protein biomarkers for predicting gastrointestinal stromal tumor recurrence

We conducted a proteomic analysis using mass spectrometry to identify and validate protein biomarkers for accurately predicting recurrence risk in gastrointestinal stromal tumors (GIST) patients, focusing on differentially expressed proteins in metastatic versus primary GIST tissues. We selected five biomarkers-GPX4, RBM4, TPM3, PFKFB2, and PGAM5-and validated their expressions in primary tumors of recurrent and non-recurrent GIST patients via immunohistochemistry. Our analysis of the association between these biomarkers with recurrence-free survival (RFS) and overall survival (OS), along with their interrelationships, revealed that immunohistochemistry confirmed significantly higher expressions of these biomarkers in primary GIST tissues of recurrent patients. Kaplan-Meier survival analysis showed that high expressions of GPX4, RBM4, TPM3, PFKFB2, and PGAM5 correlated with lower RFS, and GPX4 and RBM4 with lower OS. All biomarker pairs showed positive associations, with high expressions correlating with increased recurrence rates, and GPX4 and RBM4 with higher mortality rates. In conclusion, the biomarkers GPX4, RBM4, TPM3, PFKFB2, and PGAM5 are clinically relevant for predicting GIST recurrence, with their high expressions in primary tumors linked to poorer RFS and OS. They serve as potential prognostic indicators, enabling early treatment and improved outcomes. The observed interrelationships among these biomarkers further validate their accuracy in predicting GIST recurrence.

Applications of blood plasma derivatives for cutaneous wound healing: A mini-review of clinical studies

Skin injuries are a global healthcare problem. Chronic ulcers do not heal in a timely fashion, so it is essential to help the body with skin repair. There are some treatments that have been applied to chronic ulcers. One of these treatments is growth factor (GF) therapy. Platelet-rich plasma (PRP) and Platelet-poor plasma (PPP) are two types of plasma derivatives containing many GFs important for wound healing. Several works have reported their application in wound healing and tissue regeneration. The use of autologous PRP is now an adequate alternative in regenerative medicine. It was also demonstrated that PPP is a hemostatic agent for wounds. This review has studied the latest clinical studies, which have applied PRP and PPP to patients with chronic wounds.

Advanced methods to mechanically isolate stromal vascular fraction: A concise review

Adipose tissue is a highly attractive reservoir of stem cells due to its accessibility and abundance, and the SVF within it holds great promise for stem cell-based therapies. The use of mechanical methods for SVF isolation from adipose tissue is preferred over enzymatic methods, as it can be readily applied in clinical settings without additional processing steps. However, there is a lack of consensus on the optimal approach for mechanically isolating SVF. This comprehensive review aims to present and compare the latest mechanical isolation methods for SVF from adipose tissue, including centrifugation, filtration/washing, emulsification, vibration, and mincing/adiponizing. Each of these methods possesses unique advantages and limitations, and yet, no conclusive evidence has emerged demonstrating the superiority of one approach over the others, primarily due to the dearth of well-controlled prospective studies in this field.

Comparison of bioactive substances in novel-developed freeze-dried platelet-rich plasma (PRP) and activated normal PRP, and investigation of bioactive substance levels after long-term storage

Introduction

In recent years, biotherapy in orthopedics has become widespread, and platelet-rich plasma (PRP) has been readily used to treat sports injuries and osteoarthritis. Production of freeze-dried PRP (PRP-FD) results in PRP that is in powder form, allowing it to be stored for long periods at room temperature. Using this technology, we have developed Valuable Platelet-Derived Factor Concentrate Freeze Dry (VFD). However, whether VFD contains sufficient levels of bioactive substances (BS) remains unknown and retains the same levels of BS during long-term storage. In this study, we examined whether VFD contains sufficient amounts of BS and whether they retain these BS levels during long-term storage.

Methods

Peripheral blood was collected from 10 healthy men (mean ± SD: 46.5 ± 15 years old) and various BS, including transforming growth factor β (TGF-β), basic fibroblast growth factor (bFGF), epidermal growth factor (EGF), tissue inhibitors of metalloproteinases-1 (TIMP-1), interleukin-1 receptor antagonist (IL-1ra), matrix metallopeptidase-9 (MMP-9), and interleukin-6 (IL-6), were compared between VFD and normal PRP samples, including both leukocyte-rich PRP (LR-PRP) and leukocyte-poor PRP (LP-PRP). VFD was prepared using two rounds of centrifugation. LP-PRP and LR-PRP were activated by freezing and thawing before measurement. To evaluate the effects of long-term storage, the BS of VFD purified from five professional football players was compared between baseline and 1 year after storage.

Results

In terms of the growth factors, the TGF-β and EGF levels were higher in LR-PRP than in VFD and LP-PRP (p < 0.05), while the bFGF levels were higher in VFD than in the LR-PRP and LP-PRP groups (p < 0.01). In terms of anti-inflammatory cytokines, the TIMP-1 level was lower in VFD than that in the other groups (p < 0.01), whereas the IL-1ra levels were higher in VFD than those in LP-PRP (p < 0.05) and lower than those in LR-PRP (p < 0.01). In terms of inflammatory enzymes and cytokines, the IL-1ra level was higher in VFD than that in LP-PRP (p < 0.05) and lower than that in LR-PRP (p < 0.01), whereas the IL-6 levels did not differ among the groups. Furthermore, the TGF-β, bFGF, TIMP-1, and IL-1ra levels were 5.61 → 3.38 (x103 pg/μL), 61.0 → 63.0 (pg/μL), 3.4 → 2.7 (x105 pg/μL), and 14.9 → 14.5 (x103 pg/μL) at baseline and 1 year later, respectively. No significant differences in the BS levels were observed between baseline and 1 year after storage.

Conclusions

The VFD samples prepared in this study exhibited higher levels of anti-inflammatory cytokines than LP-PRP and contained growth factor levels similar to LP-PRP and LR-PRP. In addition, the BS levels in VFD samples were maintained after one year of storage. These results suggest that VFD can be prepared and stored and may serve as a novel treatment strategy for sports injuries in high-risk groups, such as athletes.

LiViT-Net: A U-Net-like, lightweight Transformer network for retinal vessel segmentation

The intricate task of precisely segmenting retinal vessels from images, which is critical for diagnosing various eye diseases, presents significant challenges for models due to factors such as scale variation, complex anatomical patterns, low contrast, and limitations in training data. Building on these challenges, we offer novel contributions spanning model architecture, loss function design, robustness, and real-time efficacy. To comprehensively address these challenges, a new U-Net-like, lightweight Transformer network for retinal vessel segmentation is presented. By integrating MobileViT+ and a novel local representation in the encoder, our design emphasizes lightweight processing while capturing intricate image structures, enhancing vessel edge precision. A novel joint loss is designed, leveraging the characteristics of weighted cross-entropy and Dice loss to effectively guide the model through the task's challenges, such as foreground-background imbalance and intricate vascular structures. Exhaustive experiments were performed on three prominent retinal image databases. The results underscore the robustness and generalizability of the proposed LiViT-Net, which outperforms other methods in complex scenarios, especially in intricate environments with fine vessels or vessel edges. Importantly, optimized for efficiency, LiViT-Net excels on devices with constrained computational power, as evidenced by its fast performance. To demonstrate the model proposed in this study, a freely accessible and interactive website was established (https://hz-t3.matpool.com:28765?token=aQjYR4hqMI), revealing real-time performance with no login requirements.

Microbial functional pathways based on metatranscriptomic profiling enable effective saliva-based health assessments for precision wellness

It is increasingly recognized that an important step towards improving overall health is to accurately measure biomarkers of health from the molecular activities prevalent in the oral cavity. We present a general methodology for computationally quantifying the activity of microbial functional pathways using metatranscriptomic data. We describe their implementation as a collection of eight oral pathway scores using a large salivary sample dataset (n = 9350), and we evaluate score associations with oropharyngeal disease phenotypes within an unseen independent cohort (n = 14,129). Through this validation, we show that the relevant oral pathway scores are significantly worse in individuals with periodontal disease, acid reflux, and nicotine addiction, compared with controls. Given these associations, we make the case to use these oral pathway scores to provide molecular health insights from simple, non-invasive saliva samples, and as molecular endpoints for actionable interventions to address the associated conditions.

Evaluation of the precision and accuracy in the classification of breast histopathology images using the MobileNetV3 model

Accurate surgical pathological assessment of breast biopsies is essential to the proper management of breast lesions. Identifying histological features, such as nuclear pleomorphism, increased mitotic activity, cellular atypia, patterns of architectural disruption, as well as invasion through basement membranes into surrounding stroma and normal structures, including invasion of vascular and lymphatic spaces, help to classify lesions as malignant. This visual assessment is repeated on numerous slides taken at various sections through the resected tumor, each at different magnifications. Computer vision models have been proposed to assist human pathologists in classification tasks such as these. Using MobileNetV3, a convolutional architecture designed to achieve high accuracy with a compact parameter footprint, we attempted to classify breast cancer images in the BreakHis_v1 breast pathology dataset to determine the performance of this model out-of-the-box. Using transfer learning to take advantage of ImageNet embeddings without special feature extraction, we were able to correctly classify histopathology images broadly as benign or malignant with 0.98 precision, 0.97 recall, and an F1 score of 0.98. The ability to classify into histological subcategories was varied, with the greatest success being with classifying ductal carcinoma (accuracy 0.95), and the lowest success being with lobular carcinoma (accuracy 0.59). Multiclass ROC assessment of performance as a multiclass classifier yielded AUC values ≥0.97 in both benign and malignant subsets. In comparison with previous efforts, using older and larger convolutional network architectures with feature extraction pre-processing, our work highlights that modern, resource-efficient architectures can classify histopathological images with accuracy that at least matches that of previous efforts, without the need for labor-intensive feature extraction protocols. Suggestions to further refine the model are discussed.

Black Americans' perceptions of Alzheimer's disease, a healthy brain, and strategies for brain health promotion

Objectives

Lack of awareness of Alzheimer's disease (AD) among Black Americans may undermine their ability to identify potential AD risk. We examined Black Americans' perceptions and knowledge of AD, and views of a healthy brain, which may contribute to the development of effective and culturally sensitive strategies to address racial disparities in AD.

Methods

We conducted a mixed-methods study, integrating a cross-sectional survey of 258 older (>55 years) Black participants and qualitative interviews with a sub-sample of N = 29. Both data sets were integrated to inform the results.

Results

Participants endorsed having little knowledge of AD. While most participants reported practicing a healthy lifestyle to promote a healthy brain, the range of activities listed were limited. Participants made several suggestions to increase AD awareness, which includes using AD educational materials containing information that would benefit the whole family, not only older adults. Outreach approaches that address both individual behaviors and structural factors were also encouraged.

Conclusion

Our findings identify ongoing needs to improve AD awareness among traditionally under-represented groups.

Innovation

The study utilized novel approaches to examine participants' perspectives of AD that included a diverse sample of research naïve participants, and integrated exploration of participants' views of AD and brain health.

kmerDB: A database encompassing the set of genomic and proteomic sequence information for each species

The decrease in sequencing expenses has facilitated the creation of reference genomes and proteomes for an expanding array of organisms. Nevertheless, no established repository that details organism-specific genomic and proteomic sequences of specific lengths, referred to as kmers, exists to our knowledge. In this article, we present kmerDB, a database accessible through an interactive web interface that provides kmer-based information from genomic and proteomic sequences in a systematic way. kmerDB currently contains 202,340,859,107 base pairs and 19,304,903,356 amino acids, spanning 54,039 and 21,865 reference genomes and proteomes, respectively, as well as 6,905,362 and 149,305,183 genomic and proteomic species-specific sequences, termed quasi-primes. Additionally, we provide access to 5,186,757 nucleic and 214,904,089 peptide sequences absent from every genome and proteome, termed primes. kmerDB features a user-friendly interface offering various search options and filters for easy parsing and searching. The service is available at: www.kmerdb.com.

Microcomputed tomography as a diagnostic tool for detection of lymph node metastasis in non-small cell lung cancer: A decision-support approach for pathological examination "A pilot study for method validation"

Background

Non-small cell lung cancer (NSCLC) patients without lymph node (LN) metastases (pN0) may exhibit different survival rates, even when their T stage is similar. This divergence could be attributed to the current pathology practice, wherein LNs are examined solely in two-dimensional (2D). Unfortunately, adhering to the protocols of 2D pathological examination does not ensure the exhaustive sampling of all excised LNs, thereby leaving room for undetected metastatic foci in the unexplored depths of tissues. The employment of micro-computed tomography (micro-CT) facilitates a three-dimensional (3D) evaluation of all LNs without compromising sample integrity. In our study, we utilized quantitative micro-CT parameters to appraise the metastatic status of formalin-fixed paraffin-embedded (FFPE) LNs.

Methods

Micro-CT scans were conducted on 12 FFPEs obtained from 8 NSCLC patients with histologically confirmed mediastinal LN metastases. Simultaneously, whole-slide images from these FFPEs underwent scanning, and 47 regions of interest (ROIs) (17 metastatic foci, 11 normal lymphoid tissues, 10 adipose tissues, and 9 anthracofibrosis) were marked on scanned images. Quantitative structural variables obtained via micro-CT analysis from tumoral and non-tumoral ROIs, were analyzed.

Result

Significant distinctions were observed in linear density, connectivity, connectivity density, and closed porosity between tumoral and non-tumoral ROIs, as indicated by kappa coefficients of 1, 0.90, 1, and 1, respectively. Receiver operating characteristic analysis substantiated the differentiation between tumoral and non-tumoral ROIs based on thickness, linear density, connectivity, connectivity density, and the percentage of closed porosity.

Conclusions

Quantitative micro-CT parameters demonstrate the ability to distinguish between tumoral and non-tumoral regions of LNs in FFPEs. The discriminatory characteristics of these quantitative micro-CT parameters imply their potential usefulness in developing an artificial intelligence algorithm specifically designed for the 3D identification of LN metastases while preserving the FFPE tissue.

Mesenchymal stem cell therapy in ischemic stroke trials. A systematic review

Cerebrovascular accidents, also known as strokes, are the leading cause of permanent disability in society, presenting significant socioeconomic and healthcare costs. They can be caused by ischemic factors or hemorrhages, with ischemic strokes being the most common among the population. Therapies for patients suffering from this condition are limited and primarily focus on acute-phase treatment. In recent years, there has been an increase in cellular therapies, employing Stem Cells to mitigate or eliminate the consequences arising from this disease. Mesenchymal Stem Cells (MSCs) hold substantial therapeutic potential in Nervous System pathologies due to their low antigenicity and capacity to differentiate into various human tissues, such as adipogenic, chondrogenic, and osteogenic tissues. This study conducts a literature review using the "clinical trials" and "Pubmed" database, summarizing all ongoing clinical trials for ischemic strokes that utilize MSCs as treatment.

A method for chromatin domain partitioning based on hypergraph clustering

For many years, multi-scale models of chromatin domains, such as A/B compartments, sub-compartments, topologically associated domains (TADs), sub-TADs, and loops have been popular. However, existing methods can only identify structures at a single scale and cannot partition multi-scale structures. In this paper, we proposed a method (TORNADOES) for chromatin domain partitioning based on hypergraph clustering. First, we use a density clustering algorithm to identify TADs at different scales based on Hi-C data with different resolutions. Then, by combining ChIP-seq data features and TAD results at different scales, we generate a hypergraph based on these TADs. Finally, we partition the chromatin domain structure at different scales, including A/B, A1, A2, B1, B2, and B3 based on the Laplacian matrix feature of the hypergraph. Similarity comparison experiments and ChIP-seq signal enrichment analysis are performed on the A/B region and sub-TAD levels, respectively, demonstrating that our method can identify chromatin domains with distinct features and provide a deeper understanding of the organizational patterns and functional differences in TADs at the genomic hierarchical structure. Comparative analysis of multiple cell line data shows that TORNADOES can better classify different numbers and types of compartments by changing the factors ChIP-seq data and clustering number used to characterize TAD compared to other methods. Source code for the TORNADOES method can be found at https://github.com/ghaiyan/TORNADOES.

Node features of chromosome structure networks and their connections to genome annotation

The 3D conformations of chromosomes can encode biological significance, and the implications of such structures have been increasingly appreciated recently. Certain chromosome structural features, such as A/B compartmentalization, are frequently extracted from Hi-C pairwise genome contact information (physical association between different regions of the genome) and compared with linear annotations of the genome, such as histone modifications and lamina association. We investigate how additional properties of chromosome structure can be deduced using an abstract graph representation of the contact heatmap, and describe specific network properties that can have a strong connection with some of these biological annotations. We constructed chromosome structure networks (CSNs) from bulk Hi-C data and calculated a set of site-resolved (node-based) network properties. These properties are useful for characterizing certain aspects of chromosomal structure. We examined the ability of network properties to differentiate several scenarios, such as haploid vs diploid cells, partially inverted nuclei vs conventional architecture, depletion of chromosome architectural proteins, and structural changes during cell development. We also examined the connection between network properties and a series of other linear annotations, such as histone modifications and chromatin states including poised promoter and enhancer labels. We found that semi-local network properties exhibit greater capability in characterizing genome annotations compared to diffusive or ultra-local node features. For example, the local square clustering coefficient can be a strong classifier of lamina-associated domains. We demonstrated that network properties can be useful for highlighting large-scale chromosome structure differences that emerge in different biological situations.

Multifunctional type lll recombinant human collagen incorporated sodium alginate hydrogel with sustained release of extra cellular vehicles for wound healing multimodal therapy in diabetic mice

The effective promotion of wound healing poses a substantial challenge for clinical treatment. Despite evidence supporting the role of extracellular vesicles (EVs) in this process, their therapeutic potential is currently restrict by challenges in targeting and maintaining them. The manufacturing process for rhCol III, or recombinant human collagen III, is stable, and the rejection rate is low. We used a cross-linking method to prepare a rhCol III incorporated sodium alginate (SA) hydrogel, which enabled to accomplish an EV sustained release that was site-specific. Cell viability through MTT assay, proliferation and ROS generation were performed with MC3T3-E1cell lines. In addition, diabetic wounds are characterised by an environment of hyper-inflammation and elevated oxidative stress. The rhCol III/SA-EVs hydrogel, which is a delivery vehicle with anti-inflammatory and antioxidant characteristics, promotes wound healing in this setting. The In vivo effectiveness of the created wound dressing on a diabetic wound model was examined in this study. After 21 days of treatment, the wound dressing significantly (p < 0.05) expedited wound healing compared to the control group, and wound closure was approximately 95% without any negative systemic reactions.

Attenuation of implicit motor learning with consecutive exposure to visual errors

Visual errors induced by movement drive implicit corrections of that movement. When similar errors are experienced consecutively, does sensitivity to the error remain consistent each time? This study aimed to investigate the modulation of implicit error sensitivity through continuous exposure to the same errors. In the reaching task using visual error-clamp feedback, participants were presented with the same error in direction and magnitude for four consecutive trials. We found that implicit error sensitivity decreased after exposure to the second error. These results indicate that when visual errors occur consecutively, the sensorimotor system exhibits different responses, even for identical errors. The continuity of errors may be a factor that modulates error sensitivity.

Food insecurity and its contributing determinants among rural households in the south-western region of Bangladesh, 2021: A cross-sectional study

Introduction

The onset of the COVID-19 pandemic has disrupted food access, resulting in substantial consequences for food insecurity and contributing to adverse individual and public health outcomes. To comprehensively evaluate these challenges and grasp their implications for food security, this study aimed to evaluate the contributing determinants of food insecurity among rural households in the southwestern region of Bangladesh.

Study design

A cross-sectional study was conducted using a validated questionnaire in selected 310 rural household respondents from the southwestern region of Bangladesh.

Methods

Household food insecurity status was the outcome variable for the analysis. Multinomial logistic regression analysis was used to explore and predict risk factors correlated with food insecurity among southwestern Bangladeshi households.

Results

We found that 59 % and 27.5 % of households were suffering from moderate food insecurity and severe food insecurity, respectively. The multinomial regression model revealed that respondents residing in Kusthia (RRR = 5.56 CI:2.67-8.4 and RRR = 6.65, CI:3.37-9.22) aged between 30 and 40 years (RRR = 2.32, 95 % CI:1.84-3.77 and RRR = 1.87, 95 % CI:1.48-3.97) and 40-50 years (RRR = 1.86 95 % CI:1.46-3.82 and RRR = 1.95, 95 % CI:1.75-3.26) were significantly associated with mild-to-moderate and severe food insecurity. Respondents with a monthly family income of <58.96 USD (3.38 times and 2.18 times), had ≥5 family members (2.68 times and 1.89 times), and had poor income during the pandemic (4.25 times and 2.75 times) more likely to be moderate and severe food insecure.

Conclusion

The results emphasized that during the COVID-19 lockdown in Bangladesh, rural households faced diverse levels of food insecurity, ranging from moderate to severe. It suggests that efforts to raise awareness and implement support strategies for those at higher risk should not only focus on income but also consider additional factors such as family size, adults aged 30-40 years, and occupation.

Development and evaluation of a training program on non-communicable diseases to empower community health workers in rural India

Objective

We developed and evaluated a training program for Accredited Social Health Activists (ASHAs), female community health workers (CHWs) in India, on non-communicable diseases (NCDs).

Methods

A 5-day training program, developed using government-approved manuals, was tested in a randomised controlled trial in the Tehri-Garhwal district. Quantitative comparisons were undertaken using Student's t-test and two-way ANOVA. ASHAs in the intervention group were asked questions about new skills learnt.

Results

Thirty-six ASHAs (20 intervention, 16 controls) participated (response rate 75.0%). Mean pre-test knowledge score was 43.3/100 points (95% CI 36.7-49.9) for the intervention group and 44.4 (38.9-49.9) for controls. The mean post-test knowledge score increased more in the intervention group (48.5-point increase; P < 0.0001), than in controls (9.8-point increase, P = 0.016; ANOVA interaction term (time*allocation) P < 0.0001). ASHAs in the intervention group reported learning new skills for detecting NCDs.

Conclusion

The training program increased knowledge of ASHAs on NCDs and improved their skills to detect NCDs. Our development and testing process for this training program, coupled with open-source resources, fosters innovation and collaboration in managing NCDs in LMICs.

Innovation

Our novel and adaptable training program incorporates interactive elements, case studies, and real-world scenarios to augment routine communication between ASHAs and community members for preventing NCDs.

Binding affinity between coronavirus spike protein and human ACE2 receptor

Coronaviruses (CoVs) pose a major risk to global public health due to their ability to infect diverse animal species and potential for emergence in humans. The CoV spike protein mediates viral entry into the cell and plays a crucial role in determining the binding affinity to host cell receptors. With particular emphasis on α- and β-coronaviruses that infect humans and domestic animals, current research on CoV receptor use suggests that the exploitation of the angiotensin-converting enzyme 2 (ACE2) receptor poses a significant threat for viral emergence with pandemic potential. This review summarizes the approaches used to study binding interactions between CoV spike proteins and the human ACE2 (hACE2) receptor. Solid-phase enzyme immunoassays and cell binding assays allow qualitative assessment of binding but lack quantitative evaluation of affinity. Surface plasmon resonance, Bio-layer interferometry, and Microscale Thermophoresis on the other hand, provide accurate affinity measurement through equilibrium dissociation constants (KD). In silico modeling predicts affinity through binding structure modeling, protein-protein docking simulations, and binding energy calculations but reveals inconsistent results due to the lack of a standardized approach. Machine learning and deep learning models utilize simulated and experimental protein-protein interaction data to elucidate the critical residues associated with CoV binding affinity to hACE2. Further optimization and standardization of existing approaches for studying binding affinity could aid pandemic preparedness. Specifically, prioritizing surveillance of CoVs that can bind to human receptors stands to mitigate the risk of zoonotic spillover.

Is the prevalence of risk factors, clinical presentations and severity of coronary artery diseases (CAD) in patients with very early and premature CAD are different from mature CAD patients?: A registry- based cross-sectional study

Introduction

The present study aims to compare the risk factors, clinical presentation, and severity of coronary artery involvement in young compared to elderly CAD patients to assess the cardiovascular health status for better disease management and control of these specific patients.

Methods

This registry-based cross-sectional study was conducted using Coronary Angiography and Angioplasty Registry (CAAR) patients in east of Tehran, Iran. The data were extracted from 330 patients with confirmed CAD recorded by the CAAR during July 2021 to August 2023.

Results

The majority of patients in MCAD (68.2%) and VECAD (80%) were male, while the majority of PCAD patients were female (51.8%). Among PCAD patients, the prevalence of diabetes (38.1%) was higher than in other groups. The presence of IHD history in the father (38.1%) and mother (26.3%) was higher in the VECAD group. The mean total cholesterol, LDL, and LDL/HDL ratio were higher in the VECAD group. Among MCAD group (75.4%) compared to PCAD (58.1%) and VECAD (47.2%) groups, the multi-vessel disease was more common.MCAD patients had the highest median Gensini score compared to PCAD and VECAD patients. Also, in male compared to female the mean Gensini score was higher by 8 units (ß = 8.26, 95%CI = 0.24, 16.28).

Conclusion

Modifiable risk factors in young CAD patients are common. High LDL-C levels and smoking were the common modifiable CVD risk factors in young patients, indicating the significant role of these traditional risk factors in early atherosclerosis development alongside inheritable risk-factors such as positive family history that were more common in young CAD patients. While, the severity of coronary artery involvement in individuals with MCAD was higher, but the priority of involvement based on the type of vessel was almost the same in all CAD groups.

Fostering reflective impact orientation in transdisciplinary research-A multi-method workshop format

The primary objective of transdisciplinary research (TDR) is to contribute to the solution of complex 'real-world' problems by integrating heterogeneous knowledge and achieving societal effects. However, establishing a continuous impact orientation during TDR processes remains a challenge, as the necessary tools are not yet sufficiently available. We developed and tested a half-day workshop format for strengthening the impact-oriented project management and research activities of seven TDR projects. Our findings indicate that the reflective impact workshops supported participants in pursuing societal effects systematically. Applying the methodological approach also fosters TDR process qualities such as knowledge integration. Conducted at different project stages, the results can serve as a basis for monitoring and adapting the project design. The reflective approach•includes scientific and non-scientific TDR project team members,•draws on Theory of Change as a conceptual framework and motivates participants to reflect on plausible impact pathways and make implicit assumptions about interlinkages between different forms of societal effects explicit, and•provides results which enable project partners to adjust their project design for greater societal effectiveness.

Temporal association of antimicrobial use in livestock with antimicrobial resistance in non-typhoid Salmonella human infections in the Netherlands, 2008-2019

Background

Antimicrobial use (AMU) in livestock contributes to antimicrobial resistance (AMR) among zoonotic pathogens, such as non-typhoid Salmonella (NTS). Since 2009, the Netherlands has made substantial efforts to reduce AMU in livestock.

Objectives

To assess the association between AMU in livestock and AMR in NTS human isolates. Additionally, associations between AMU in broilers/pigs and AMR in NTS broiler/pig isolates, and between AMR in broilers/pigs and in human NTS isolates were assessed. The focus was on Salmonella Enteritidis (SE) and Salmonella Typhimurium including its monophasic variant (ST/STM).

Methods

A national population registry-based study was conducted in the Netherlands from 2008 to 2019. Multivariable logistic regression models were used to assess the associations between livestock AMU and NTS resistance proportion in humans and broilers/pigs, overall as well as per class-specific antimicrobials. Correlation analysis was performed to relate AMR proportions between human and broiler/pig NTS isolates.

Results

For SE, only a positive association between penicillins use in broilers and resistance to ampicillin among human isolates was significant. For ST/STM, most associations between AMU in livestock and AMR among human isolates were significantly positive, overall and per class-specific antimicrobials, namely for penicillins-ampicillin, tetracyclines-tetracycline and sulfonamides/trimethoprim-sulfamethoxazole/trimethoprim. Significantly positive associations between AMU in broilers/pigs and AMR in broiler/pig ST/STM isolates were also observed, but not between broiler/pig and human AMR levels.

Conclusions

Significant associations were generally found between livestock AMU and AMR in human and broiler/pig ST/STM isolates. However, confounding factors, such as imported meat and travel are of concern. To fully comprehend the impact of livestock AMU on resistance in human NTS isolates, it is imperative to enhance AMR surveillance of NTS.

A simple modification to the classical SIR model to estimate the proportion of under-reported infections using case studies in flu and COVID-19

Background

Under-reporting and, thus, uncertainty around the true incidence of health events is common in all public health reporting systems. While the problem of under-reporting is acknowledged in epidemiology, the guidance and methods available for assessing and correcting the resulting bias are obscure.

Objective

We aim to design a simple modification to the Susceptible - Infected - Removed (SIR) model for estimating the fraction or proportion of reported infection cases.

Methods

The suggested modification involves rescaling of the classical SIR model producing its mathematically equivalent version with explicit dependence on the reporting parameter (true proportion of cases reported). We justify the rescaling using the phase plane analysis of the SIR model system and show how this rescaling parameter can be estimated from the data along with the other model parameters.

Results

We demonstrate how the proposed method is cross-validated using simulated data with known disease cases and then apply it to two empirical reported data sets to estimate the fraction of reported cases in Missoula County, Montana, USA, using: (1) flu data for 2016-2017 and (2) COVID-19 data for fall of 2020.

Conclusions

We establish with the simulated and COVID-19 data that when most of the disease cases are presumed reported, the value of the additional reporting parameter in the modified SIR model is close or equal to one, so that the original SIR model is appropriate for data analysis. Conversely, the flu example shows that when the reporting parameter is close to zero, the original SIR model is not accurately estimating the usual rate parameters, and the re-scaled SIR model should be used. This research demonstrates the role of under-reporting of disease data and the importance of accounting for under-reporting when modeling simulated, endemic, and pandemic disease data. Correctly reporting the "true" number of disease cases will have downstream impacts on predictions of disease dynamics. A simple parameter adjustment to the SIR modeling framework can help alleviate bias and uncertainty around crucial epidemiological metrics (e.g.: basic disease reproduction number) and public health decision making.

Predicting plant disease epidemics using boosted regression trees

Plant epidemics are often associated with weather-related variables. It is difficult to identify weather-related predictors for models predicting plant epidemics. In the article by Shah et al., to predict Fusarium head blight (FHB) epidemics of wheat, they explored a functional approach using scalar-on-function regression to model a binary outcome (FHB epidemic or non-epidemic) with respect to weather time series spanning 140 days relative to anthesis. The scalar-on-function models fit the data better than previously described logistic regression models. In this work, given the same dataset and models, we attempt to reproduce the article by Shah et al. using a different approach, boosted regression trees. After fitting, the classification accuracy and model statistics are surprisingly good.

Decoding phenotypic screening: A comparative analysis of image representations

Biomedical imaging techniques such as high content screening (HCS) are valuable for drug discovery, but high costs limit their use to pharmaceutical companies. To address this issue, The JUMP-CP consortium released a massive open image dataset of chemical and genetic perturbations, providing a valuable resource for deep learning research. In this work, we aim to utilize the JUMP-CP dataset to develop a universal representation model for HCS data, mainly data generated using U2OS cells and CellPainting protocol, using supervised and self-supervised learning approaches. We propose an evaluation protocol that assesses their performance on mode of action and property prediction tasks using a popular phenotypic screening dataset. Results show that the self-supervised approach that uses data from multiple consortium partners provides representation that is more robust to batch effects whilst simultaneously achieving performance on par with standard approaches. Together with other conclusions, it provides recommendations on the training strategy of a representation model for HCS images.