Predicting which patients with cancer will see a psychiatrist or counsellor from their initial oncology consultation document using natural language processing.

BACKGROUND: Patients with cancer often have unmet psychosocial needs. Early detection of who requires referral to a counsellor or psychiatrist may improve their care. This work used natural language processing to predict which patients will see a counsellor or psychiatrist from a patient's initial oncology consultation document. We believe this is the first use of artificial intelligence to predict psychiatric outcomes from non-psychiatric medical documents. METHODS: This retrospective prognostic study used data from 47,625 patients at BC Cancer. We analyzed initial oncology consultation documents using traditional and neural language models to predict whether patients would see a counsellor or psychiatrist in the 12 months following their initial oncology consultation. RESULTS: Here, we show our best models achieved a balanced accuracy (receiver-operating-characteristic area-under-curve) of 73.1% (0.824) for predicting seeing a psychiatrist, and 71.0% (0.784) for seeing a counsellor. Different words and phrases are important for predicting each outcome. CONCLUSION: These results suggest natural language processing can be used to predict psychosocial needs of patients with cancer from their initial oncology consultation document. Future research could extend this work to predict the psychosocial needs of medical patients in other settings.

Patients with cancer often need support for their mental health. Early detection of who requires referral to a counsellor or psychiatrist may improve their care. This study trained a type of artificial intelligence (AI) called natural language processing to read the consultation report an oncologist writes after they first see a patient to predict which patients will see a counsellor or psychiatrist. The AI predicted this with performance similar to other uses of AI in mental health, and used different words and phrases to predict who would see a psychiatrist compared to seeing a counsellor. We believe this is the first use of AI to predict mental health outcomes from medical documents written by clinicians outside of mental health. This study suggests this type of AI can predict the mental health needs of patients with cancer from this widely-available document.

Machine Learning to Predict the Need for Postmastectomy Radiotherapy after Immediate Breast Reconstruction.

Background: Post mastectomy radiotherapy (PMRT) is an independent predictor of reconstructive complications. PMRT may alter the timing and type of reconstruction recommended. This study aimed to create a machine learning model to predict the probability of requiring PMRT after immediate breast reconstruction (IBR). Methods: In this retrospective study, breast cancer patients who underwent IBR from January 2017 to December 2020 were reviewed and data were collected on 81 preoperative characteristics. Primary outcome was recommendation for PMRT. Four algorithms were compared to maximize performance and clinical utility: logistic regression, elastic net (EN), logistic lasso, and random forest (RF). The cohort was split into a development dataset (75% of cohort for training-validation) and 25% used for the test set. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), precision-recall curves, and calibration plots. Results: In a total of 800 patients, 325 (40.6%) patients were recommended to undergo PMRT. With the training-validation dataset (n = 600), model performance was logistic regression 0.73 AUC [95% confidence interval (CI) 0.65-0.80]; RF 0.77 AUC (95% CI, 0.74-0.81); EN 0.77 AUC (95% CI, 0.73-0.81); logistic lasso 0.76 AUC (95% CI, 0.72-0.80). Without significantly sacrificing performance, 81 predictive factors were reduced to 12 for prediction with the EN method. With the test dataset (n = 200), performance of the EN prediction model was confirmed [0.794 AUC (95% CI, 0.730-0.858)]. Conclusion: A parsimonious accurate machine learning model for predicting PMRT after IBR was developed, tested, and translated into a clinically applicable online calculator for providers and patients.

Aerosol delivery and spatiotemporal tissue distribution of hydroxychloroquine in rat lung.

Inhalation enables the delivery of drugs directly to the lung, increasing the retention for prolonged exposure and maximizing the therapeutic index. However, the differential regional lung exposure kinetics and systemic pharmacokinetics are not fully known, and their estimation is critical for pulmonary drug delivery. The study evaluates the pharmacokinetics of hydroxychloroquine in different regions of the respiratory tract for multiple routes of administration. We also evaluated the influence of different inhaled formulations on systemic and lung pharmacokinetics by identifying suitable nebulizers followed by early characterization of emitted aerosol physicochemical properties. The salt- and freebase-based formulations required different nebulizers and generated aerosol with different physicochemical properties. An administration of hydroxychloroquine by different routes resulted in varied systemic and lung pharmacokinetics, with oral administration resulting in low tissue concentrations in all regions of the respiratory tract. A nose-only inhalation exposure resulted in higher and sustained lung concentrations of hydroxychloroquine with a lung parenchyma-to-blood ratio of 386 after 1440 min post-exposure. The concentrations of hydroxychloroquine in different regions of the respiratory tract (i.e., nasal epithelium, larynx, trachea, bronchi, and lung parenchyma) varied over time, indicating different retention kinetics. The spatiotemporal distribution of hydroxychloroquine in the lung is different due to the heterogeneity of cell types, varying blood perfusion rate, clearance mechanisms, and deposition of inhaled aerosol along the respiratory tract. In addition to highlighting the varied lung physiology, these results demonstrate the ability of the lung to retain increased levels of inhaled lysosomotropic drugs. Such findings are critical for the development of future inhalation-based therapeutics, aiming to optimize target site exposure, enable precision medicine, and ultimately enhance clinical outcomes.

A platform for connecting social media data to domain-specific topics using large language models: an application to student mental health.

Objectives: To design a novel artificial intelligence-based software platform that allows users to analyze text data by identifying various coherent topics and parts of the data related to a specific research theme-of-interest (TOI). Materials and Methods: Our platform uses state-of-the-art unsupervised natural language processing methods, building on top of a large language model, to analyze social media text data. At the center of the platform's functionality is BERTopic, which clusters social media posts, forming collections of words representing distinct topics. A key feature of our platform is its ability to identify whole sentences corresponding to topic words, vastly improving the platform's ability to perform downstream similarity operations with respect to a user-defined TOI. Results: Two case studies on mental health among university students are performed to demonstrate the utility of the platform, focusing on signals within social media (Reddit) data related to depression and their connection to various emergent themes within the data. Discussion and Conclusion: Our platform provides researchers with a readily available and inexpensive tool to parse large quantities of unstructured, noisy data into coherent themes, as well as identifying portions of the data related to the research TOI. While the development process for the platform was focused on mental health themes, we believe it to be generalizable to other domains of research as well.

Public sentiments and the influence of information-seeking preferences on knowledge, attitudes, death conversation, and receptiveness toward palliative care: results from a nationwide survey in Singapore.

Background: Low awareness about palliative care among the global public and healthcare communities has been frequently cited as a persistent barrier to palliative care acceptance. Given that knowledge shapes attitudes and encourages receptiveness, it is critical to examine factors that influence the motivation to increase knowledge. Health information-seeking from individuals and media has been identified as a key factor, as the process of accessing and interpreting information to enhance knowledge has been shown to positively impact health behaviours. Objective: Our study aimed to uncover public sentiments toward palliative care in Singapore. A conceptual framework was additionally developed to investigate the relationship between information-seeking preferences and knowledge, attitudes, receptiveness of palliative care, and comfort in death discussion. Design and Methods: A nationwide survey was conducted in Singapore with 1226 respondents aged 21 years and above. The data were analysed through a series of hierarchical multiple regression to examine the hypothesised role of information-seeking sources as predictors. Results: Our findings revealed that 53% of our participants were aware of palliative care and about 48% were receptive to receiving the care for themselves. It further showed that while information-seeking from individuals and media increases knowledge, attitudes and receptiveness to palliative care, the comfort level in death conversations was found to be positively associated only with individuals, especially healthcare professionals. Conclusion: Our findings highlight the need for public health authorities to recognize people's deep-seated beliefs and superstitions surrounding the concept of mortality. As Asians view death as a taboo topic that is to be avoided at all costs, it is necessary to adopt multipronged communication programs to address those fears. It is only when the larger communicative environment is driven by the media to encourage public discourse, and concurrently supported by timely interventions to trigger crucial conversations on end-of-life issues between individuals, their loved ones, and the healthcare team, can we advance awareness and benefits of palliative care among the public in Singapore.

A nationwide survey to understand public sentiments and the extent that information-seeking preferences can increase knowledge, attitudes, receptiveness of palliative care, and comfort level in death discussion in Singapore Low awareness of palliative care is a barrier that persistently hinders palliative care acceptance among populations in developing and developed countries. As knowledge shapes attitudes and encourages receptiveness, it is vital that researchers uncover factors that influence the motivation to increase knowledge. Health information-seeking is a factor that deserves greater attention in palliative care research because the process of seeking out information on health concerns from other people or the media can greatly increase individuals' knowledge. As such, this nationwide survey involving 1226 participants was carried out in Singapore to understand the public sentiments toward palliative care. It further statistically analyzed if information-seeking (from individuals and the media) will increase knowledge, attitudes, receptiveness toward palliative care, and comfort level in death discussion. Our findings indicated that 53% of our participants were aware of palliative care and about 48% were receptive to receiving the care for themselves. Furthermore, while information-seeking from individuals and media increases knowledge, attitudes, and receptiveness to palliative care, people are only comfortable to engage in death discussion with individuals, especially healthcare professionals. Exposure to media alone is not enough to encourage individuals to want to talk about end-of-life issues including palliative care. As Asians view death as a taboo topic, it is important for public health authorities to recognize people's deep-seated beliefs and superstitions surrounding the concept of mortality. A multipronged communication program is therefore needed to address these fears. It is only when the larger communicative environment driven by the media to encourage public discourse, and concurrently supported by timely interventions to trigger crucial conversations on end-of-life issues between individuals, their loved ones, and the healthcare team, can we advance awareness and benefits of palliative care among the public in Singapore.

Advance Care Planning in Singapore: The genesis and evolution of a national programme.

Singapore is a developed country that is aging rapidly. In 2011, a national programme in Advance Care Planning (ACP) known as Living Matters was launched. Over the years, the programme has grown and evolved. While ACP has become routine in some hospital units, challenges remain in implementing ACP as a standard of care across all levels of the healthcare system. Opportunities abound in improving the quality of the ACP process and in bringing ACP upstream into outpatient clinics, primary care and into the community.

Regulatory T Cell Biomarkers Identify Patients at Risk of Developing Acute Cellular Rejection in the First Year Following Heart Transplantation.

BACKGROUND: Acute cellular rejection (ACR), an alloimmune response involving CD4+ and CD8+ T cells, occurs in up to 20% of patients within the first year following heart transplantation. The balance between a conventional versus regulatory CD4+ T cell alloimmune response is believed to contribute to developing ACR. Therefore, tracking these cells may elucidate whether changes in these cell populations could signal ACR risk. METHODS: We used a CD4+ T cell gene signature (TGS) panel that tracks CD4+ conventional T cells (Tconv) and regulatory T cells (Treg) on longitudinal samples from 94 adult heart transplant recipients. We evaluated combined diagnostic performance of the TGS panel with a previously developed biomarker panel for ACR diagnosis, HEARTBiT, while also investigating TGS' prognostic utility. RESULTS: Compared with nonrejection samples, rejection samples showed decreased Treg- and increased Tconv-gene expression. The TGS panel was able to discriminate between ACR and nonrejection samples and, when combined with HEARTBiT, showed improved specificity compared with either model alone. Furthermore, the increased risk of ACR in the TGS model was associated with lower expression of Treg genes in patients who later developed ACR. Reduced Treg gene expression was positively associated with younger recipient age and higher intrapatient tacrolimus variability. CONCLUSIONS: We demonstrated that expression of genes associated with CD4+ Tconv and Treg could identify patients at risk of ACR. In our post hoc analysis, complementing HEARTBiT with TGS resulted in an improved classification of ACR. Our study suggests that HEARTBiT and TGS may serve as useful tools for further research and test development.

An update on the discovery and development of reversible covalent inhibitors.

Small molecule drugs that covalently bind irreversibly to their target proteins have several advantages over conventional reversible inhibitors. They include increased duration of action, less-frequent drug dosing, reduced pharmacokinetic sensitivity, and the potential to target intractable shallow binding sites. Despite these advantages, the key challenges of irreversible covalent drugs are their potential for off-target toxicities and immunogenicity risks. Incorporating reversibility into covalent drugs would lead to less off-target toxicity by forming reversible adducts with off-target proteins and thus reducing the risk of idiosyncratic toxicities caused by the permanent modification of proteins, which leads to higher levels of potential haptens. Herein, we systematically review electrophilic warheads employed during the development of reversible covalent drugs. We hope the structural insights of electrophilic warheads would provide helpful information to medicinal chemists and aid in designing covalent drugs with better on-target selectivity and improved safety.

DeepComBat: A Statistically Motivated, Hyperparameter-Robust, Deep Learning Approach to Harmonization of Neuroimaging Data.

Neuroimaging data from multiple batches (i.e. acquisition sites, scanner manufacturer, datasets, etc.) are increasingly necessary to gain new insights into the human brain. However, multi-batch data, as well as extracted radiomic features, exhibit pronounced technical artifacts across batches. These batch effects introduce confounding into the data and can obscure biological effects of interest, decreasing the generalizability and reproducibility of findings. This is especially true when multi-batch data is used alongside complex downstream analysis models, such as machine learning methods. Image harmonization methods seeking to remove these batch effects are important for mitigating these issues; however, significant multivariate batch effects remain in the data following harmonization by current state-of-the-art statistical and deep learning methods. We present DeepCombat, a deep learning harmonization method based on a conditional variational autoencoder architecture and the ComBat harmonization model. DeepCombat learns and removes subject-level batch effects by accounting for the multivariate relationships between features. Additionally, DeepComBat relaxes a number of strong assumptions commonly made by previous deep learning harmonization methods and is empirically robust across a wide range of hyperparameter choices. We apply this method to neuroimaging data from a large cognitive-aging cohort and find that DeepCombat outperforms existing methods, as assessed by a battery of machine learning methods, in removing scanner effects from cortical thickness measurements while preserving biological heterogeneity. Additionally, DeepComBat provides a new perspective for statistically-motivated deep learning harmonization methods.

Microbial dysbiosis and the host airway epithelial response: insights into HIV-associated COPD using multi'omics profiling.

BACKGROUND: People living with HIV (PLWH) are at increased risk of developing Chronic Obstructive Pulmonary Disease (COPD) independent of cigarette smoking. We hypothesized that dysbiosis in PLWH is associated with epigenetic and transcriptomic disruptions in the airway epithelium. METHODS: Airway epithelial brushings were collected from 18 COPD + HIV + , 16 COPD - HIV + , 22 COPD + HIV - and 20 COPD - HIV - subjects. The microbiome, methylome, and transcriptome were profiled using 16S sequencing, Illumina Infinium Methylation EPIC chip, and RNA sequencing, respectively. Multi 'omic integration was performed using Data Integration Analysis for Biomarker discovery using Latent cOmponents. A correlation > 0.7 was used to identify key interactions between the 'omes. RESULTS: The COPD + HIV -, COPD -HIV + , and COPD + HIV + groups had reduced Shannon Diversity (p = 0.004, p = 0.023, and p = 5.5e-06, respectively) compared to individuals with neither COPD nor HIV, with the COPD + HIV + group demonstrating the most reduced diversity. Microbial communities were significantly different between the four groups (p = 0.001). Multi 'omic integration identified correlations between Bacteroidetes Prevotella, genes FUZ, FASTKD3, and ACVR1B, and epigenetic features CpG-FUZ and CpG-PHLDB3. CONCLUSION: PLWH with COPD manifest decreased diversity and altered microbial communities in their airway epithelial microbiome. The reduction in Prevotella in this group was linked with epigenetic and transcriptomic disruptions in host genes including FUZ, FASTKD3, and ACVR1B.

USP9X mediates an acute adaptive response to MAPK suppression in pancreatic cancer but creates multiple actionable therapeutic vulnerabilities.

Pancreatic ductal adenocarcinomas (PDACs) frequently harbor KRAS mutations. Although MEK inhibitors represent a plausible therapeutic option, most PDACs are innately resistant to these agents. Here, we identify a critical adaptive response that mediates resistance. Specifically, we show that MEK inhibitors upregulate the anti-apoptotic protein Mcl-1 by triggering an association with its deubiquitinase, USP9X, resulting in acute Mcl-1 stabilization and protection from apoptosis. Notably, these findings contrast the canonical positive regulation of Mcl-1 by RAS/ERK. We further show that Mcl-1 inhibitors and cyclin-dependent kinase (CDK) inhibitors, which suppress Mcl-1 transcription, prevent this protective response and induce tumor regression when combined with MEK inhibitors. Finally, we identify USP9X as an additional potential therapeutic target. Together, these studies (1) demonstrate that USP9X regulates a critical mechanism of resistance in PDAC, (2) reveal an unexpected mechanism of Mcl-1 regulation in response to RAS pathway suppression, and (3) provide multiple distinct promising therapeutic strategies for this deadly malignancy.

Engaging a Non-catalytic Cysteine Residue Drives Potent and Selective Inhibition of Caspase-6.

Caspases are a family of cysteine-dependent proteases with important cellular functions in inflammation and apoptosis, while also implicated in human diseases. Classical chemical tools to study caspase functions lack selectivity for specific caspase family members due to highly conserved active sites and catalytic machinery. To overcome this limitation, we targeted a non-catalytic cysteine residue (C264) unique to caspase-6 (C6), an enigmatic and understudied caspase isoform. Starting from disulfide ligands identified in a cysteine trapping screen, we used a structure-informed covalent ligand design to produce potent, irreversible inhibitors (3a) and chemoproteomic probes (13-t) of C6 that exhibit unprecedented selectivity over other caspase family members and high proteome selectivity. This approach and the new tools described will enable rigorous interrogation of the role of caspase-6 in developmental biology and in inflammatory and neurodegenerative diseases.

Predicting the Survival of Patients With Cancer From Their Initial Oncology Consultation Document Using Natural Language Processing.

Importance: Predicting short- and long-term survival of patients with cancer may improve their care. Prior predictive models either use data with limited availability or predict the outcome of only 1 type of cancer. Objective: To investigate whether natural language processing can predict survival of patients with general cancer from a patient's initial oncologist consultation document. Design, Setting, and Participants: This retrospective prognostic study used data from 47 625 of 59 800 patients who started cancer care at any of the 6 BC Cancer sites located in the province of British Columbia between April 1, 2011, and December 31, 2016. Mortality data were updated until April 6, 2022, and data were analyzed from update until September 30, 2022. All patients with a medical or radiation oncologist consultation document generated within 180 days of diagnosis were included; patients seen for multiple cancers were excluded. Exposures: Initial oncologist consultation documents were analyzed using traditional and neural language models. Main Outcomes and Measures: The primary outcome was the performance of the predictive models, including balanced accuracy and receiver operating characteristics area under the curve (AUC). The secondary outcome was investigating what words the models used. Results: Of the 47 625 patients in the sample, 25 428 (53.4%) were female and 22 197 (46.6%) were male, with a mean (SD) age of 64.9 (13.7) years. A total of 41 447 patients (87.0%) survived 6 months, 31 143 (65.4%) survived 36 months, and 27 880 (58.5%) survived 60 months, calculated from their initial oncologist consultation. The best models achieved a balanced accuracy of 0.856 (AUC, 0.928) for predicting 6-month survival, 0.842 (AUC, 0.918) for 36-month survival, and 0.837 (AUC, 0.918) for 60-month survival, on a holdout test set. Differences in what words were important for predicting 6- vs 60-month survival were found. Conclusions and Relevance: These findings suggest that models performed comparably with or better than previous models predicting cancer survival and that they may be able to predict survival using readily available data without focusing on 1 cancer type.

Cross-Ancestry Genome-Wide Association Study Defines the Extended CYP2D6 Locus as the Principal Genetic Determinant of Endoxifen Plasma Concentrations.

The therapeutic efficacy of tamoxifen is predominantly mediated by its active metabolites 4-hydroxy-tamoxifen and endoxifen, whose formation is catalyzed by the polymorphic cytochrome P450 2D6 (CYP2D6). Yet, known CYP2D6 polymorphisms only partially determine metabolite concentrations in vivo. We performed the first cross-ancestry genome-wide association study with well-characterized patients of European, Middle-Eastern, and Asian descent (n = 497) to identify genetic factors impacting active and parent metabolite formation. Genome-wide significant variants were functionally evaluated in an independent liver cohort (n = 149) and in silico. Metabolite prediction models were validated in two independent European breast cancer cohorts (n = 287, n = 189). Within a single 1-megabase (Mb) region of chromosome 22q13 encompassing the CYP2D6 gene, 589 variants were significantly associated with tamoxifen metabolite concentrations, particularly endoxifen and metabolic ratio (MR) endoxifen/N-desmethyltamoxifen (minimal P = 5.4E-35 and 2.5E-65, respectively). Previously suggested other loci were not confirmed. Functional analyses revealed 66% of associated, mostly intergenic variants to be significantly correlated with hepatic CYP2D6 activity or expression (ρ = 0.35 to -0.52), and six hotspot regions in the extended 22q13 locus impacting gene regulatory function. Machine learning models based on hotspot variants (n = 12) plus CYP2D6 activity score (AS) increased the explained variability (~ 9%) compared with AS alone, explaining up to 49% (median R2 ) and 72% of the variability in endoxifen and MR endoxifen/N-desmethyltamoxifen, respectively. Our findings suggest that the extended CYP2D6 locus at 22q13 is the principal genetic determinant of endoxifen plasma concentration. Long-distance haplotypes connecting CYP2D6 with adjacent regulatory sites and nongenetic factors may account for the unexplained portion of variability.

Synthetic data as an enabler for machine learning applications in medicine.

Synthetic data generation is the process of using machine learning methods to train a model that captures the patterns in a real dataset. Then new or synthetic data can be generated from that trained model. The synthetic data does not have a one-to-one mapping to the original data or to real patients, and therefore has the potential of privacy preserving properties. There is a growing interest in the application of synthetic data across health and life sciences, but to fully realize the benefits, further education, research, and policy innovation is required. This article summarizes the opportunities and challenges of SDG for health data, and provides directions for how this technology can be leveraged to accelerate data access for secondary purposes.

Recommendations on the post-acute management of the osteoporotic fracture - Patients with "very-high" Re-fracture risk.

Osteoporosis is a systemic skeletal disease where there is low bone mass and deterioration of bone microarchitecture, leading to an increased risk of a fragility fracture. The aim of this clinical guideline from Fragility Fracture Network Hong Kong SAR, is to provide evidence-based recommendations on the post-acute treatment of the osteoporotic fracture patient that presents for clinical care at the Fracture Liaison Service (FLS). It is now well established that the incidence of a second fracture is especially high after the first 2 years of the initial osteoporotic fracture. Therefore, the recent osteoporotic fracture should be categorized as "very-high" re-fracture risk. Due to the significant number of silent vertebral fractures in the elderly population, it is also recommended that vertebral fracture assessment (VFA) should be incorporated into FLS. This would have diagnostic and treatment implications for the osteoporotic fracture patient. The use of a potent anti-osteoporotic agent, and preferably an anabolic followed by an anti-resorptive agent should be considered, as larger improvements in BMD is strongly associated with a reduction in fractures. Managing other risk factors including falls and sarcopenia are imperative during rehabilitation and prevention of another fracture. Although of low incidence, one should remain vigilant of the atypical femoral fracture. The aging population is increasing worldwide, and it is expected that the treatment of osteoporotic fractures will be routine. The recommendations are anticipated to aid in the daily clinical practice for clinicians. The Translational potential of this article: Fragility fractures have become a common encounter in clinical practise in the hospital setting. This article provides recommendations on the post-acute management of fragility fracture patients at the FLS.