Epidemiology, diagnosis, preoperative evaluation and prognostic assessment of upper-tract urothelial carcinoma (UTUC)

PURPOSE

Upper-tract urothelial carcinoma (UTUC) is a relatively uncommon disease with limited available evidence on specific topics. The purpose of this article was to review the previous literature to summarize the current knowledge about UTUC epidemiology, diagnosis, preoperative evaluation and prognostic assessment.

METHODS

Using MEDLINE, a non-systematic review was performed including articles between January 2000 and February 2016. English language original articles, reviews and editorials were selected based on their clinical relevance.

RESULTS

UTUC accounts for 5-10 % of all urothelial cancers, with an increasing incidence. UTUC and bladder cancer share some common risk factors, even if they are two different entities regarding practical, biological and clinical characteristics. Aristolochic acid plays an important role in UTUC pathogenesis in certain regions. It is further estimated that approximately 10 % of UTUC are part of the hereditary non-polyposis colorectal cancer spectrum disease. UTUC diagnosis remains mainly based on imaging and endoscopy, but development of new technologies is rapidly changing the diagnosis algorithm. To help the decision-making process regarding surgical treatment, extent of lymphadenectomy and selection of neoadjuvant systemic therapies, predictive tools based on preoperative patient and tumor characteristics have been developed.

CONCLUSIONS

Awareness regarding epidemiology, diagnosis, preoperative evaluation and prognostic assessment changes is essential to correctly diagnose and manage UTUC patients, thereby potentially improving their outcomes.

Design of antimicrobial peptides from a cuttlefish database

No antimicrobial peptide has been identified in cephalopods to date. Annotation of transcriptomes or genomes using basic local alignment Search Tool failed to yield any from sequence identities. Therefore, we searched for antimicrobial sequences in the cuttlefish (Sepia officinalis) database by in silico analysis of a transcriptomic database. Using an original approach based on the analysis of cysteine-free antimicrobial peptides selected from our Antimicrobial Peptide Database (APD3), the online prediction tool of the Collection of Anti-Microbial Peptides (CAMP_R3), and a homemade software program, we identified potential antibacterial sequences. Nine peptides less than 25 amino acids long were synthesized. The hydrophobic content of all nine of them ranged from 30 to 70%, and they could form alpha-helices. Three peptides possessed similarities with piscidins, one with BMAP-27, and five were totally new. Their antibacterial activity was evaluated on eight bacteria including the aquatic pathogens Vibrio alginolyticus, Aeromonas salmonicida, or human pathogens such as Salmonella typhimurium, Listeria monocytogenes, or Staphylococcus aureus. Despite the prediction of an antimicrobial potential for eight of the peptides, only two-GR₂₁ and KT₁₉-inhibited more than one bacterial strain with minimal inhibitory concentrations below 25 µM. Some sequences like VA₂₀ and FK₁₉ were hemolytic, while GR₂₁ induced less than 10% of hemolysis on human blood cells at a concentration of 200 µM. GR₂₁ was the only peptide derived from a precursor with a signal peptide, suggesting a real role in cuttlefish immune defense.

Improving breast cancer care coordination and symptom management by using AI driven predictive toolkits

Integrated breast cancer care is complex, marked by multiple hand-offs between primary care and specialists over an extensive period of time. Communication is essential for treatment compliance, lowering error and complication risk, as well as handling co-morbidity. The director role of care, however, becomes often unclear, and patients remain lost across departments. Digital tools can add significant value to care communication but need clarity about the directives to perform in the care team. In effective breast cancer care, multidisciplinary team meetings can drive care planning, create directives and structured data collection. Subsequently, nurse navigators can take the director’s role and become a pivotal determinant for patient care continuity. In the complexity of care, automated AI driven planning can facilitate their tasks, however, human intervention stays needed for psychosocial support and tackling unexpected urgency. Care allocation of patients across centres, is often still done by hand and phone demanding time due to overbooked agenda’s and discontinuous system solutions limited by privacy rules and moreover, competition among providers. Collection of complete outcome information is limited to specific collaborative networks today. With data continuity over time, AI tools can facilitate both care allocation and risk prediction which may unveil non-compliance due to local scarce resources, distance and costs. Applied research is needed to bring AI modelling into clinical practice and drive well-coordinated, patient-centric cancer care in the complex web of modern healthcare today.

Preoperative prostate-specific antigen isoform p2PSA ≤ 22.5 pg/ml predicts advanced prostate cancer in patients undergoing radical prostatectomy

BACKGROUND

The prediction value of prostate-specific antigen (PSA) isoform [-2]proPSA (p2PSA) for detecting advanced prostate cancer (PCa) remains unclear. Our objective was to evaluate the additional clinical utility of p2PSA compared with total PSA (tPSA), free PSA (fPSA), and preoperative Gleason score (Gls) in predicting locally advanced PCa (pT3/T4) with high-accuracy discrimination. The aim was to develop a novel classification based on p2PSA and preoperative Gls for predicting advanced PCa.

MATERIALS AND METHODS

In 208 consecutive men diagnosed with clinically localized PCa who underwent radical prostatectomy, we determined the predictive and discriminatory accuracy of serum tPSA, fPSA, percentage of fPSA to tPSA, p2PSA, p2PSA density, percentage of p2PSA to fPSA, and the Prostate Health Index. The cutoff level of p2PSA with best accuracy was estimated. The novel classification was developed by analyzing the interaction between p2PSA and Gls in predicting pathologic outcomes using a chi-square automatic interaction detection analysis. Decision curve analysis was applied to test the clinical consequences of using the novel classification.

RESULTS

On univariate analyses, p2PSA, p2PSA density, percentage of p2PSA to fPSA, and Prostate Health Index were accurate but were not independent predictors by multivariate analysis. The p2PSA cutoff level of 22.5 pg/ml showed the best accuracy level for predicting and discriminating advanced diseases (area under the curve [AUC] = 0.725, sensitivity = 51.4%, specificity = 81.8%). By chi-square automatic interaction detection, univariate and multivariate analysis, a p2PSA level > 22.5 pg/ml was significantly associated with an increased frequency and risk of advanced disease. In patients with a p2PSA level ≤ 22.5 pg/ml, 91.8% of Gleason sum 6 PCa was organ confined. The combination of p2PSA and Gls enhanced slightly but significantly the predictive and discriminatory accuracy for advanced disease (0.6%-3.6%).

CONCLUSIONS

The p2PSA cutoff level of 22.5 pg/ml can accurately discriminate between organ-confined and advanced PCa. The additional use of p2PSA enhanced slightly the predictive accuracy for advanced PCa (pT3/pT4) and has limited additional predictive value in identifying aggressive PCa (Gls > 7a).

Bio-based alternatives to volatile silicones: Relationships between chemical structure, physicochemical properties and functional performances

Emollient oils are ubiquitous ingredients of personal care products, especially skin care and hair care formulations. They offer excellent spreading properties and give end-use products a soft, pleasant and non-sticky after-feel. Emollients belong to various petro- or bio-based chemical families among which silicone oils, hydrocarbons and esters are the most prominent. Silicones have exceptional physicochemical and sensory properties but their high chemical stability results in very low biodegradability and a high bioaccumulation potential. Nowadays, consumers are increasingly responsive to environmental issues and demand more environmentally friendly products. This awareness strongly encourages cosmetics industries to develop bio-based alternatives to silicone oils. Finding effective silicon-free emollients requires understanding the molecular origin of emollience. This review details the relationships between the molecular structures of emollients and their physicochemical properties as well as the resulting functional performances in order to facilitate the design of alternative oils with suitable physicochemical and sensory properties. The molecular profile of an ideal emollient in terms of chemical function (alkane, ether, ester, carbonate, alcohol), optimal number of carbons and branching is established to obtain an odourless oil with good spreading on the skin. Since none of the carbon-based emollients alone can imitate the non-sticky and dry feel of silicone oils, it is judicious to blend alkanes and esters to significantly improve both the sensory properties and the solubilizing properties of the synergistic mixture towards polar ingredients (sun filters, antioxidants, fragrances). Finally, it is shown how modelling tools (QSPR, COSMO-RS and neural networks) can predict in silico the key properties of hundreds of virtual candidate molecules in order to synthesize only the most promising whose predicted properties are close to the specifications.

Responding to COVID-19 with real-time general practice data in Australia

INTRODUCTION

As SARS-CoV-2 spread around the world, Australia was no exception. Part of the Australian response was a robust primary care approach, involving changes to care models (including telehealth) and the widespread use of data to inform the changes. This paper outlines how a large primary care database responded to provide real-time data to inform policy and practice. Simply extracting the data is not sufficient. Understanding the data is. The POpulation Level Analysis and Reporting (POLAR) program is designed to use GP data for multiple objectives and is built on a pre-existing engagement framework established over a fifteen-year period. Initially developed to provide QA activities for general practices and population level data for General Practice support organisations, the POLAR platform has demonstrated the critical ability to design and deploy real-time data analytics solutions during the COVID-19 pandemic for a variety of stakeholders including state and federal government agencies.

METHODS

The system extracts and processes data from over 1,300 general practices daily. Data is de-identified at the point of collection and encrypted before transfer. Data cleaning for analysis uses a variety of techniques, including Natural Language Processing and coding of free text information. The curated dataset is then distilled into several analytic solutions designed to address specific areas of investigation of interest to various stakeholders. One such analytic solution was a model we created that used multiple data inputs to rank patient geographic areas by the likelihood of a COVID-19 outbreak. The model utilised pathology ordering, COVID-19 related diagnoses, indication of COVID-19 related concern (via progress notes) and also incorporated state based actual confirmed case figures.

RESULTS

Using the methods described, we were able to deliver real-time data feeds to practices, Primary Health Networks (PHN) and other agencies. In addition, we developed a COVID-19 geographic risk stratification based on local government areas (LGAs) to pro-actively inform the primary care response. Providing PHNs with a list of geographic priority hotspots allowed for better targeting and response of Personal Protective Equipment allocation and pop-up clinic placement.

CONCLUSIONS

The program summarised here demonstrates the ability of a well-designed system underpinned by accurate and reliable data, to respond in real-time to a rapidly evolving public health emergency in a way which supports and enhances the health system response.

Predictive approaches to guide the expression of recombinant vaccine targets in Escherichia coli: a case study presentation utilising Absynth Biologics Ltd. proprietary Clostridium difficile vaccine antigens

Bacterial expression systems remain a widely used host for recombinant protein production. However, overexpression of recombinant target proteins in bacterial systems such as Escherichia coli can result in poor solubility and the formation of insoluble aggregates. As a consequence, numerous strategies or alternative engineering approaches have been employed to increase recombinant protein production. In this case study, we present the strategies used to increase the recombinant production and solubility of ‘difficult-to-express’ bacterial antigens, termed Ant2 and Ant3, from Absynth Biologics Ltd.’s Clostridium difficile vaccine programme. Single recombinant antigens (Ant2 and Ant3) and fusion proteins (Ant2-3 and Ant3-2) formed insoluble aggregates (inclusion bodies) when overexpressed in bacterial cells. Further, proteolytic cleavage of Ant2-3 was observed. Optimisation of culture conditions and changes to the construct design to include N-terminal solubility tags did not improve antigen solubility. However, screening of different buffer/additives showed that the addition of 1–15 mM dithiothreitol alone decreased the formation of insoluble aggregates and improved the stability of both Ant2 and Ant3. Structural models were generated for Ant2 and Ant3, and solubility-based prediction tools were employed to determine the role of hydrophobicity and charge on protein production. The results showed that a large non-polar region (containing hydrophobic amino acids) was detected on the surface of Ant2 structures, whereas positively charged regions (containing lysine and arginine amino acids) were observed for Ant3, both of which were associated with poor protein solubility. We present a guide of strategies and predictive approaches that aim to guide the construct design, prior to expression studies, to define and engineer sequences/structures that could lead to increased expression and stability of single and potentially multi-domain (or fusion) antigens in bacterial expression systems.

Value of Data Sharing to Advance Drug Development: A Regulatory Perspective

New tools are needed to evaluate and predict the efficacy and safety of medical products. However, the development of such tools requires collaboration and effective data sharing. In this issue of Therapeutic Innovation & Regulatory Science, scientists from the Critical Path Institute (C-Path) discuss their experiences using patient-level data to facilitate innovation in drug development. We share our perspective on the issues discussed in the C-Path paper and offer suggestions on future efforts.

Cost-effectiveness analysis of genetic tools to predict treatment response in patients with cystic fibrosis

BACKGROUND

Cystic fibrosis (CF) transmembrane conductance regulator (CFTR) modulator therapies show variable efficacy for patients with CF. Patient-derived predictive tools may identify individuals likely to respond to CFTRs, but are not in routine use. We aimed to determine the cost-utility of predictive tool-guided treatment with CFTRs as add-on to standard of care (SoC) for individuals with CF.

METHODS

This economic evaluation compared two strategies using an individual level simulation: (i) Treat All, where all patients received CFTRs plus SoC and (ii) Test→Treat, where patients who tested positive on predictive tools received CFTRs plus SoC and those who tested negative received SoC only. We simulated 50,000 individuals over their lifetime, and estimated costs (2020 CAD) per quality-adjusted life year (QALY) from the healthcare payer's perspective, discounted at 1.5% annually. The model was populated using Canadian CF registry data and published literature. Probabilistic and deterministic sensitivity were conducted.

RESULTS

The Treat All and Test→Treat and strategies yielded 22.41 and 21.36 QALYs, and cost $4.21 M and $3.15 M respectively. Results of probabilistic sensitivity analysis showed that Test→Treat was highly cost-effective compared to Treat All in 100% of simulations at cost-effectiveness thresholds as high as $500,000 per QALY. Test→Treat may save between $931 K to $1.1 M per QALY lost, depending on sensitivity and specificity of predictive tools.

CONCLUSION

The use of predictive tools could optimize the health benefits of CFTR modulators while reducing costs. Our findings support the use of pre-treatment predictive testing and may help inform coverage and reimbursement policies for individuals with CF.

Traumatic brain injury and variants of shock index

Traumatic Brain Injury is a major cause of death and long-term disability. The early identification of patients at high risk of mortality is important for both management and prognosis. Although many modified scoring systems have been developed for improving the prediction accuracy in patients with trauma, few studies have focused on prediction accuracy and application in patients with traumatic brain injury. The shock index (SI) which was first introduced in the 1960s has shown to strongly correlate degree of circulatory shock with increasing SI. In this editorial we comment on a publication by Carteri et al wherein they perform a retrospective analysis studying the predictive potential of SI and its variants in populations with severe traumatic brain injury.

Stakeholder perceptions of preventive approaches to rheumatoid arthritis: qualitative study of healthcare professionals' perspectives on predictive and preventive strategies

BACKGROUND

There is increasing research interest in the development of preventive treatment for individuals at risk of rheumatoid arthritis (RA). Previous studies have explored the perceptions of at-risk groups and patients about predictive and preventive strategies for RA, but little is known about health care professionals' (HCPs) perspectives.

METHODS

One-to-one semi-structured qualitative interviews were conducted (face-to-face or by telephone) with HCPs. Audio recordings of the interviews were transcribed, and the data were analysed by thematic analysis.

RESULTS

Nineteen HCPs (11 female) were interviewed, including ten GPs, six rheumatologists and three rheumatology nurse specialists. The thematic analysis identified four organising themes: 1) Attributes of predictive and preventive approaches; 2) Ethical and psychological concerns; 3) Implementation issues and 4) Learning from management of other conditions. Theme 1 described necessary attributes of predictive and preventive approaches, including the type and performance of predictive tools, the need for a sound evidence base and consideration of risks and benefits associated with preventive treatment. Theme 2 described the ethical and psycho-social concerns that interviewees raised, including the potential negative economic, financial and psychological effects of risk disclosure for 'at-risk' individuals, uncertainty around the development of RA and the potential for benefit associated with the treatments being considered. Theme 3 describes the implementation issues considered, including knowledge and training needs, costs and resource implications of implementing predictive and preventive approaches, the role of different types of HCPs, guidelines and tools needed, and patient characteristics relating to the appropriateness of preventive treatments. Theme 4 describes lessons that could be learned from interviewees' experiences of prediction and prevention in other disease areas, including how preventive treatment is prescribed, existing guidelines and tools for other diseases and issues relating to risk communication.

CONCLUSIONS

For successful implementation of predictive and preventative approaches in RA, HCPs need appropriate training about use and interpretation of predictive tools, communication of results to at-risk individuals, and options for intervention. Evidence of cost-efficiency, appropriate resource allocation, adaptation of official guidelines and careful consideration of the at-risk individuals' psycho-social needs are also needed.

Enhancement of solubility of recombinant alcohol dehydrogenase from Rhodococcus ruber using predictive tool

Solubility is one of key factors influencing the heterologous production of recombinant proteins in biotechnology. Among many aggregation-prone proteins, alcohol dehydrogenase (ADH-A) from Rhodococcus ruber (in this work abbreviated RrADH) shows a great potential in processes involved in the biotransformation of natural compounds. As ADH-A is a potentially high value asset in industrial biotransformation processes, improvement of its solubility would be of major commercial benefit. Predictive tools and in silico analysis provide a fast means for improving protein properties, for selecting appropriate changes, and ultimately for saving costs. We have therefore focused on enhancement of the solubility of RrADH using an online accesible predictive tool Aggrescan 3D 2.0. Selected mutations were introduced into the protein amino acid sequence by using site-directed PCR. This led to a 17% increase in the protein solubility of RrADHmut1 and a 98% increase for RrADHmut2. Moreover, the basic kinetics of the enzyme reaction were positively affected, further optimizing the overall performance of the production process.

The development of early warning scores or alerting systems for the prediction of adverse events in psychiatric patients: a scoping review

BACKGROUND

Adverse events in psychiatric settings present ongoing challenges for both patients and staff. Despite advances in psychiatric interventions and treatments, research on early warning scores and tools to predict patient deterioration is limited. This review provides a summary of the few tools that have been developed in a psychiatric setting, comparing machine learning (ML) and nonmachine learning/traditional methodologies. The outcomes of interest include the selected key variables that contribute to adverse events and the performance and validation measures of the predictive models.

METHODS

Three databases, Ovid MEDLINE, PsycINFO, and Embase, were searched between February 2023 and April 2023 to identify all relevant studies that included a combination of (and were not limited to) the following search terms: "Early warning," "Alerting tool," and "Psychiatry". Peer-reviewed primary research publications were included without imposing any date restrictions. A total of 1,193 studies were screened. A total of 9 studies met the inclusion and exclusion criteria and were included in this review. The PICOS model, the Joanna Briggs Institute (JBI) Reviewer's Manual, and PRISMA guidelines were applied.

RESULTS

This review identified nine studies that developed predictive models for adverse events in psychiatric settings. Encompassing 41,566 participants across studies that used both ML and non-ML algorithmic approaches, performance metrics, primarily AUC ROC, varied among studies between 0.62 and 0.95. The best performing model that had also been validated was the random forest (RF) ML model, with a score of 0.87 and a high sensitivity of 74% and a specificity of 88%.

CONCLUSION

Currently, few predictive models have been developed for adverse events and patient deterioration in psychiatric settings. The findings of this review suggest that the use of ML and non-ML algorithms show moderate to good performance in predicting adverse events at the hospitals/units where the tool was developed. Understanding these models and the methodology of the studies is crucial for enhancing patient care as well as staff and patient safety research. Further research on the development and implementation of predictive tools in psychiatry should be carried out to assess the feasibility and efficacy of the tool in psychiatric patients.

Modified prediction model for poor short-term prognosis in patients with chronic subdural hematoma after burr hole drainage: a retrospective cohort study

To the Editor,I would like to acknowledge the valuable efforts taken to enhance the knowledge through the article "Prediction model for poor short-term prognosis in patients with chronic subdural hematoma (CSDH) after burr hole drainage: a retrospective cohort study" [1]. We thoroughly read this article published in your journal and learned the aim behind this study. This article has described every aspect of determining prognosis postoperatively in patients after evacuation of chronic subdural hematoma via a single burr hole. This research has immense potential to add valuable insights into preexisting literature. Nonetheless, we still have some uncertainties to address, for which I would like to offer a few critical reflections that could be very valuable in further research in this aspect. Incorporating these additional factors could significantly enhance the comprehensiveness of this model.