Exploring Molecular Mechanisms and Biomarkers in COPD: An Overview of Current Advancements and Perspectives.

Chronic obstructive pulmonary disease (COPD) plays a significant role in global morbidity and mortality rates, typified by progressive airflow restriction and lingering respiratory symptoms. Recent explorations in molecular biology have illuminated the complex mechanisms underpinning COPD pathogenesis, providing critical insights into disease progression, exacerbations, and potential therapeutic interventions. This review delivers a thorough examination of the latest progress in molecular research related to COPD, involving fundamental molecular pathways, biomarkers, therapeutic targets, and cutting-edge technologies. Key areas of focus include the roles of inflammation, oxidative stress, and protease-antiprotease imbalances, alongside genetic and epigenetic factors contributing to COPD susceptibility and heterogeneity. Additionally, advancements in omics technologies-such as genomics, transcriptomics, proteomics, and metabolomics-offer new avenues for comprehensive molecular profiling, aiding in the discovery of novel biomarkers and therapeutic targets. Comprehending the molecular foundation of COPD carries substantial potential for the creation of tailored treatment strategies and the enhancement of patient outcomes. By integrating molecular insights into clinical practice, there is a promising pathway towards personalized medicine approaches that can improve the diagnosis, treatment, and overall management of COPD, ultimately reducing its global burden.

Functional Genomics in Psoriasis.

Psoriasis is an autoimmune cutaneous condition that significantly impacts quality of life and represents a burden on society due to its prevalence. Genome-wide association studies (GWASs) have pinpointed several psoriasis-related risk loci, underlining the disease's complexity. Functional genomics is paramount to unveiling the role of such loci in psoriasis and disentangling its complex nature. In this review, we aim to elucidate the main findings in this field and integrate our discussion with gold-standard techniques in molecular biology-i.e., Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-and high-throughput technologies. These tools are vital to understanding how disease risk loci affect gene expression in psoriasis, which is crucial in identifying new targets for personalized treatments in advanced precision medicine.

A genome-wide CRISPR screen reveals that antagonism of glutamine metabolism sensitizes head and neck squamous cell carcinoma to ferroptotic cell death.

Glutamine is a conditionally essential amino acid for the growth and survival of rapidly proliferating cancer cells. Many cancers are addicted to glutamine, and as a result, targeting glutamine metabolism has been explored clinically as a therapeutic approach. Glutamine-catalyzing enzymes are highly expressed in primary and metastatic head and neck squamous cell carcinoma (HNSCC). However, the nature of the glutamine-associated pathways in this aggressive cancer type has not been elucidated. Here, we explored the therapeutic potential of a broad glutamine antagonist, DRP-104 (sirpiglenastat), in HNSCC tumors and aimed at shedding light on glutamine-dependent pathways in this disease. We observed a potent antitumoral effect of sirpiglenastat in HPV- and HPV + HNSCC xenografts. We conducted a whole-genome CRISPR screen and metabolomics analyses to identify mechanisms of sensitivity and resistance to glutamine metabolism blockade. These approaches revealed that glutamine metabolism blockade results in the rapid buildup of polyunsaturated fatty acids (PUFAs) via autophagy nutrient-sensing pathways. Finally, our analysis demonstrated that GPX4 mediates the protection of HNSCC cells from accumulating toxic lipid peroxides; hence, glutamine blockade sensitizes HNSCC cells to ferroptosis cell death upon GPX4 inhibition. These findings demonstrate the therapeutic potential of sirpiglenastat in HNSCC and establish a novel link between glutamine metabolism and ferroptosis, which may be uniquely translated into targeted glutamine-ferroptosis combination therapies.

Understanding homologous recombination repair deficiency in biliary tract cancers: clinical implications and correlation with platinum sensitivity.

BACKGROUND: Biliary tract cancers (BTCs) exhibit high mortality rates and significant heterogeneity in both clinical and molecular characteristics. This study aims to molecularly characterize a cohort of patients with BTC, with a specific focus on genomic alterations within homologous recombination repair (HRR) genes in a real-world setting. PATIENTS AND METHODS: We carried out a retrospective analysis on 256 patients with BTC treated at five Austrian centers and one German comprehensive cancer center between 2016 and 2023 utilizing comprehensive genomic profiling platforms to assess HRR status and its correlation with clinical outcomes after platinum-based chemotherapy. RESULTS: A total of 67 patients (27.5%) exhibited HRR gene mutations (HRRm), with the most common pathogenic alterations in BAP1 (9%), ARID1A (7.8%), and ATM (6.1%). Time to failure of the first-line strategy (TFS) between patients with HRRm and non-HRRm treated with platinum agents was 7.9 and 6.7 months, respectively [hazard ratio (HR) 0.89; P = 0.49]. The overall survival (OS) estimates at 6, 18, and 24 months were 82%, 45%, and 39% in the HRRm group (median 16.01 months) and 81%, 42%, and 22% in the HRR group (median 15.68 months), respectively (Fleming-Harrington test P = 0.0004; log-rank P = 0.022). Significance did not persist in the multivariate analysis (HR 0.72; 95% confidence interval 0.489-1.059; P = 0.095). An interaction between HRRm status and molecular-informed therapeutic strategies in later lines was noted. In the second-line treatment, OS following an irinotecan-based regimen was comparable to re-exposure to platinum-based agents (12.36 versus 10.13 months; HR 0.92; P = 0.85). No better outcome was noted for patients with HRRm versus patients with non-HRRm with second-line platinum agents (HR 1.45; P = 0.35). CONCLUSIONS: Patients with HRRm with BTC showed a potential advantage in OS following platinum-based first-line chemotherapy, presumably attributed to enhanced opportunities for targetable coalterations. Further investigation is needed to outline HRR within the scope of BTCs and detail a clinically meaningful sensitivity to platinum agents or targeted approaches with poly (ADP-ribose) polymerase (PARP) inhibitors.

Precision cancer medicine platform trials: Concepts and design of AcSé-ESMART.

Precision cancer medicine brought the promise of improving outcomes for patients with cancer. High-throughput molecular profiling of tumors at treatment failure aims to direct a patient to a treatment matched to the tumor profile. In this way, improved outcome has been achieved in a small number of patients whose tumors exhibit unique targetable oncogenic drivers. Most cancers, however, contain multiple genetic alterations belonging to and of various hallmarks of cancer; for most of these alterations, there is limited knowledge on the level of evidence, their hierarchical roles in oncogenicity, and utility as biomarkers for response to targeted treatment(s). We developed a proof-of-concept trial that explores new treatment strategies in a molecularly-enriched tumor-agnostic, pediatric population. The evaluation of novel agents, including first-in-child molecules, alone or in combination, is guided by the available understanding of or hypotheses for the mechanisms of action of the diverse cancer events. Main objectives are: to determine 1) recommended phase 2 doses, 2) activity signals to provide the basis for disease specific development, and 3) to define new predictive biomarkers. Here we outline concepts, rationales and designs applied in the European AcSé-ESMART trial and highlight the feasibility but also complexity and challenges of such innovative platform trials.

Clinical Impact of Telomere Length Testing for Interstitial Lung Disease.

BACKGROUND: Shortened telomere length (TL) is a genomic risk factor for fibrotic interstitial lung disease (ILD), but its role in clinical management is unknown. RESEARCH QUESTION: What is the clinical impact of TL testing on the management of ILD? STUDY DESIGN AND METHODS: Patients were evaluated in the Columbia University ILD clinic and underwent CLIA-certified TL testing by flow cytometry and fluorescence in-situ hybridization (FlowFISH) as part of clinical management. Short TL was defined as below the 10th age-adjusted percentile for either granulocytes or lymphocytes by FlowFISH. Patients were offered genetic counseling and testing if they had short TL or a family history of ILD. FlowFISH TL was compared against research qPCR TL measurement. RESULTS: A total of 108 patients underwent TL testing, including those with clinical features of short telomere syndrome such as familial pulmonary fibrosis (50%) or extrapulmonary manifestations in the patient (25%) or a relative (41%). The overall prevalence of short TL was 46% and was similar across clinical ILD diagnoses. The number of short telomere clinical features was independently associated with detecting short TL (OR 2.00, 95% CI [1.27, 3.32]). TL testing led to clinical management changes for 35 (32%) patients, most commonly resulting in reduction or avoidance of immunosuppression. Of the patients who underwent genetic testing (n=34), a positive or candidate diagnostic finding in telomere-related genes was identified in 10 (29%) patients. Inclusion of TL testing below the 1st percentile helped reclassify 8 of 9 variants of uncertain significance (VUS) into actionable findings. The qPCR test correlated with FlowFISH, but age-adjusted percentile cutoffs may not be equivalent between the two assays. INTERPRETATION: Incorporating TL testing in ILD impacted clinical management and led to the discovery of new actionable genetic variants.

Impact of CYP2D6*2, CYP2D6*35, rs5758550, and related haplotypes on risperidone clearance in vivo.

PURPOSE: The CYP2D6 gene exhibits significant polymorphism, contributing to variability in responses to drugs metabolized by CYP2D6. While CYP2D6*2 and CYP2D6*35 are presently designated as alleles encoding normal metabolism, this classification is based on moderate level evidence. Additionally, the role of the formerly called "enhancer" single nucleotide polymorphism (SNP) rs5758550 is unclear. In this study, the impacts of CYP2D6*2, CYP2D6*35 and rs5758550 on CYP2D6 activity were investigated using risperidone clearance as CYP2D6 activity marker. METHODS: A joint parent-metabolite population pharmacokinetic model was used to describe 1,565 serum concentration measurements of risperidone and 9-hydroxyrisperidone in 512 subjects. Risperidone population clearance was modeled as the sum of a CYP2D6-independent clearance term and the partial clearances contributed from each individually expressed CYP2D6 allele or haplotype. In addition to the well-characterized CYP2D6 alleles (*3-*6, *9, *10 and *41), *2, *35 and two haplotypes assigned as CYP2D6*2-rs5758550G and CYP2D6*2-rs5758550A were evaluated. RESULTS: Each evaluated CYP2D6 allele was associated with significantly lower risperidone clearance than the reference normal function allele CYP2D6*1 (p < 0.001). Further, rs5758550 differentiated the effect of CYP2D6*2 (p = 0.005). The haplotype-specific clearances for CYP2D6*2-rs5758550A, CYP2D6*2-rs5758550G and CYP2D6*35 were estimated to 30%, 66% and 57%, respectively, relative to the clearance for CYP2D6*1. Notably, rs5758550 is in high linkage disequilibrium (R2 > 0.85) with at least 24 other SNPs and cannot be assigned as a functional SNP. CONCLUSION: CYP2D6*2 and CYP2D6*35 encode reduced risperidone clearance, and the extent of reduction for CYP2D6*2 is differentiated by rs5758550. Genotyping of these haplotypes might improve the precision of genotype-guided prediction of CYP2D6-mediated clearance.

Double Imprinted Nanoparticles for Sequential Membrane-to-Nuclear Drug Delivery.

Efficient and site-specific delivery of therapeutics drugs remains a critical challenge in cancer treatment. Traditional drug nanocarriers such as antibody-drug conjugates are not generally accessible due to their high cost and can lead to serious side effects including life-threatening allergic reactions. Here, these problems are overcome via the engineering of supramolecular agents that are manufactured with an innovative double imprinting approach. The developed molecularly imprinted nanoparticles (nanoMIPs) are targeted toward a linear epitope of estrogen receptor alfa (ERα) and loaded with the chemotherapeutic drug doxorubicin. These nanoMIPs are cost-effective and rival the affinity of commercial antibodies for ERα. Upon specific binding of the materials to ERα, which is overexpressed in most breast cancers (BCs), nuclear drug delivery is achieved via receptor-mediated endocytosis. Consequentially, significantly enhanced cytotoxicity is elicited in BC cell lines overexpressing ERα, paving the way for precision treatment of BC. Proof-of-concept for the clinical use of the nanoMIPs is provided by evaluating their drug efficacy in sophisticated three-dimensional (3D) cancer models, which capture the complexity of the tumor microenvironment in vivo without requiring animal models. Thus, these findings highlight the potential of nanoMIPs as a promising class of novel drug compounds for use in cancer treatment.

Integrating Vision-Language Models for Accelerated High-Throughput Nutrition Screening.

Addressing the critical need for swift and precise nutritional profiling in healthcare and in food industry, this study pioneers the integration of vision-language models (VLMs) with chemical analysis techniques. A cutting-edge VLM is unveiled, utilizing the expansive UMDFood-90k database, to significantly improve the speed and accuracy of nutrient estimation processes. Demonstrating a macro-AUCROC of 0.921 for lipid quantification, the model exhibits less than 10% variance compared to traditional chemical analyses for over 82% of the analyzed food items. This innovative approach not only accelerates nutritional screening by 36.9% when tested amongst students but also sets a new benchmark in the precision of nutritional data compilation. This research marks a substantial leap forward in food science, employing a blend of advanced computational models and chemical validation to offer a rapid, high-throughput solution for nutritional analysis.

Lung Adenocarcinoma Systems Biomarker and Drug Candidates Identified by Machine Learning, Gene Expression Data, and Integrative Bioinformatics Pipeline.

Lung adenocarcinoma (LUAD) is a significant planetary health challenge with its high morbidity and mortality rate, not to mention the marked interindividual variability in treatment outcomes and side effects. There is an urgent need for robust systems biomarkers that can help with early cancer diagnosis, prediction of treatment outcomes, and design of precision/personalized medicines for LUAD. The present study aimed at systems biomarkers of LUAD and deployed integrative bioinformatics and machine learning tools to harness gene expression data. Predictive models were developed to stratify patients based on prognostic outcomes. Importantly, we report here several potential key genes, for example, PMEL and BRIP1, and pathways implicated in the progression and prognosis of LUAD that could potentially be targeted for precision/personalized medicine in the future. Our drug repurposing analysis and molecular docking simulations suggested eight drug candidates for LUAD such as heat shock protein 90 inhibitors, cardiac glycosides, an antipsychotic agent (trifluoperazine), and a calcium ionophore (ionomycin). In summary, this study identifies several promising leads on systems biomarkers and drug candidates for LUAD. The findings also attest to the importance of integrative bioinformatics, structural biology and machine learning techniques in biomarker discovery, and precision oncology research and development.

Congenital syndromic Chiari-like malformation (CSCM) in Holstein cattle: towards unravelling of possible genetic causes.

BACKGROUND: Chiari malformation type II (CMII) was originally reported in humans as a rare disorder characterized by the downward herniation of the hindbrain and towering cerebellum. The congenital brain malformation is usually accompanied by spina bifida, a congenital spinal anomaly resulting from incomplete closure of the dorsal aspect of the spinal neural tube, and occasionally by other lesions. A similar disorder has been reported in several animal species, including cattle, particularly as a congenital syndrome. A cause of congenital syndromic Chiari-like malformation (CSCM) in cattle has not been reported to date. We collected a series of 14 CSCM-affected Holstein calves (13 purebred, one Red Danish Dairy F1 cross) and performed whole-genome sequencing (WGS). WGS was performed on 33 cattle, including eight cases with parents (trio-based; group 1), three cases with one parent (group 2), and three single cases (solo-based; group 3). RESULTS: Sequencing-based genome-wide association study of the 13 Holstein calves with CSCM and 166 controls revealed no significantly associated genome region. Assuming a single Holstein breed-specific recessive allele, no region of shared homozygosity was detected suggesting heterogeneity. Subsequent filtering for protein-changing variants that were only homozygous in the genomes of the individual cases allowed the identification of two missense variants affecting different genes, SHC4 in case 4 in group 1 and WDR45B in case 13 in group 3. Furthermore, these two variants were only observed in Holstein cattle when querying WGS data of > 5,100 animals. Alternatively, potential de novo mutational events were assessed in each case. Filtering for heterozygous private protein-changing variants identified one DYNC1H1 frameshift variant as a candidate causal dominant acting allele in case 12 in group 3. Finally, the presence of larger structural DNA variants and chromosomal abnormalities was investigated in all cases. Depth of coverage analysis revealed two different partial monosomies of chromosome 2 segments in cases 1 and 7 in group 1 and a trisomy of chromosome 12 in the WDR45B homozygous case 13 in group 3. CONCLUSIONS: This study presents for the first time a detailed genomic evaluation of CSCM in Holstein cattle and suggests an unexpected genetic and allelic heterogeneity considering the mode of inheritance, as well as the type of variant. For the first time, we propose candidate causal variants that may explain bovine CSCM in a certain proportion of affected calves. We present cattle as a large animal model for human CMII and propose new genes and genomic variants as possible causes for related diseases in both animals and humans.

The receiver operating characteristic curve accurately assesses imbalanced datasets.

Many problems in biology require looking for a "needle in a haystack," corresponding to a binary classification where there are a few positives within a much larger set of negatives, which is referred to as a class imbalance. The receiver operating characteristic (ROC) curve and the associated area under the curve (AUC) have been reported as ill-suited to evaluate prediction performance on imbalanced problems where there is more interest in performance on the positive minority class, while the precision-recall (PR) curve is preferable. We show via simulation and a real case study that this is a misinterpretation of the difference between the ROC and PR spaces, showing that the ROC curve is robust to class imbalance, while the PR curve is highly sensitive to class imbalance. Furthermore, we show that class imbalance cannot be easily disentangled from classifier performance measured via PR-AUC.

Advancing healthcare: the role and impact of AI and foundation models.

BACKGROUND: The integration of artificial intelligence (AI) into the healthcare domain is a monumental shift with profound implications for diagnostics, medical interventions, and the overall structure of healthcare systems. PURPOSE: This study explores the transformative journey of foundation AI models in healthcare, shedding light on the challenges, ethical considerations, and vast potential they hold for improving patient outcome and system efficiency. Notably, in this investigation we observe a relatively slow adoption of AI within the public sector of healthcare. The evolution of AI in healthcare is un-paralleled, especially its prowess in revolutionizing diagnostic processes. RESULTS: This research showcases how these foundational models can unravel hidden patterns within complex medical datasets. The impact of AI reverberates through medical interventions, encompassing pathology, imaging, genomics, and personalized healthcare, positioning AI as a cornerstone in the quest for precision medicine. The paper delves into the applications of generative AI models in critical facets of healthcare, including decision support, medical imaging, and the prediction of protein structures. The study meticulously evaluates various AI models, such as transfer learning, RNN, autoencoders, and their roles in the healthcare landscape. A pioneering concept introduced in this exploration is that of General Medical AI (GMAI), advocating for the development of reusable and flexible AI models. CONCLUSION: The review article discusses how AI can revolutionize healthcare by stressing the significance of transparency, fairness and accountability, in AI applications regarding patient data privacy and biases. By tackling these issues and suggesting a governance structure the article adds to the conversation about AI integration in healthcare environments.

Precision nursing and intermittent pneumatic compression significantly reduce perioperative deep vein thrombosis in post-surgical ovarian cancer patients.

OBJECTIVE: This study investigated the efficacy of precision nursing combined with intermittent pneumatic compression (IPC) devices in preventing perioperative deep vein thrombosis (DVT) in patients with ovarian cancer. METHODS: A retrospective analysis was conducted on 136 ovarian cancer surgery patients at Xi'an People's Hospital from February 2019 to April 2023. The patients were divided into two groups: 71 patients received precision nursing with IPC intervention (study group), while the remaining received standard nursing care (control group). Key variables analyzed included operation duration, intraoperative blood loss, postoperative blood transfusion requirements, changes in limb circumference, and variations in coagulation parameters activated partial thromboplastin time (APTT), D-Dimer (D-D), Fibrinogen (FIB), and Prothrombin Time (PT) before and after surgery. The incidence of DVT was recorded in both groups to determine risk factors for deep vein thrombosis. RESULTS: No significant differences were observed between the groups regarding operation duration, intraoperative blood loss, and postoperative blood transfusion rates (P > 0.05). Post-intervention, significant improvements were noted in the study group, with reduced FIB and D-D levels and increased PT and APTT levels compared to the control group (P < 0.05). Furthermore, the study group exhibited a significantly smaller post-intervention difference in limb circumference and a lower incidence of DVT (P=0.003). Precision nursing combined with IPC, pre-intervention D-D < 498.5, and FIGO stages III+IV were identified as independent factors against DVT development. CONCLUSION: Precision nursing paired with an IPC device significantly reduces the risk of perioperative DVT in ovarian cancer patients compared to conventional care.

Development and validation of an LC-MS/MS method for ruxolitinib quantification: advancing personalized therapy in hematologic malignancies.

Background: Hematologic malignancies such as leukemia and lymphoma present treatment challenges due to their genetic and molecular heterogeneity. Ruxolitinib, a Janus kinase (JAK) inhibitor, has demonstrated efficacy in managing these cancers. However, optimal therapeutic outcomes are contingent upon maintaining drug levels within a therapeutic window, highlighting the necessity for precise drug monitoring. Methods: We developed a sensitive liquid chromatography-tandem mass spectrometry (LC-MS/MS) method to quantify ruxolitinib in human plasma, improving upon traditional methods in specificity, sensitivity, and efficiency. The process involved the use of advanced chromatographic techniques and robust mass spectrometric conditions to ensure high accuracy and minimal matrix effects. The study was conducted using samples from 20 patients undergoing treatment, with calibration standards ranging from 10 to 2000 ng/mL. Results: The method displayed linearity (R 2 > 0.99) across the studied range and proved highly selective with no significant interference observed. The method's precision and accuracy met FDA guidelines, with recovery rates consistently exceeding 85%. Clinical application demonstrated significant variability in ruxolitinib plasma levels among patients, reinforcing the need for individualized dosing schedules. Conclusion: The validated LC-MS/MS method offers a reliable and efficient tool for the therapeutic drug monitoring of ruxolitinib, facilitating personalized treatment approaches in hematologic malignancies. This approach promises to enhance patient outcomes by optimizing dosing to reduce toxicity and improve efficacy.

Radiogenomics as an Integrated Approach to Glioblastoma Precision Medicine.

PURPOSE OF REVIEW: Isocitrate dehydrogenase wild-type glioblastoma is the most aggressive primary brain tumour in adults. Its infiltrative nature and heterogeneity confer a dismal prognosis, despite multimodal treatment. Precision medicine is increasingly advocated to improve survival rates in glioblastoma management; however, conventional neuroimaging techniques are insufficient in providing the detail required for accurate diagnosis of this complex condition. RECENT FINDINGS: Advanced magnetic resonance imaging allows more comprehensive understanding of the tumour microenvironment. Combining diffusion and perfusion magnetic resonance imaging to create a multiparametric scan enhances diagnostic power and can overcome the unreliability of tumour characterisation by standard imaging. Recent progress in deep learning algorithms establishes their remarkable ability in image-recognition tasks. Integrating these with multiparametric scans could transform the diagnosis and monitoring of patients by ensuring that the entire tumour is captured. As a corollary, radiomics has emerged as a powerful approach to offer insights into diagnosis, prognosis, treatment, and tumour response through extraction of information from radiological scans, and transformation of these tumour characteristics into quantitative data. Radiogenomics, which links imaging features with genomic profiles, has exhibited its ability in characterising glioblastoma, and determining therapeutic response, with the potential to revolutionise management of glioblastoma. The integration of deep learning algorithms into radiogenomic models has established an automated, highly reproducible means to predict glioblastoma molecular signatures, further aiding prognosis and targeted therapy. However, challenges including lack of large cohorts, absence of standardised guidelines and the 'black-box' nature of deep learning algorithms, must first be overcome before this workflow can be applied in clinical practice.

A prediction method for the individual serum concentration and therapeutic effect for optimizing adalimumab therapy in inflammatory bowel disease.

OBJECTIVES: Adalimumab (ADM) therapy is effective for inflammatory bowel disease (IBD), but a significant number of IBD patients lose response to ADM. Thus, it is crucial to devise methods to enhance ADM's effectiveness. This study introduces a strategy to predict individual serum concentrations and therapeutic effects to optimize ADM therapy for IBD during the induction phase. METHODS: We predicted the individual serum concentration and therapeutic effect of ADM during the induction phase based on pharmacokinetic and pharmacodynamic (PK/PD) parameters calculated using the empirical Bayesian method. We then examined whether the predicted therapeutic effect, defined as clinical remission or treatment failure, matched the observed effect. RESULTS: Data were obtained from 11 IBD patients. The therapeutic effect during maintenance therapy was successfully predicted at 40 of 47 time points. Moreover, the predicted effects at each patient's final time point matched the observed effects in 9 of the 11 patients. CONCLUSION: This is the inaugural report predicting the individual serum concentration and therapeutic effect of ADM using the Bayesian method and PK/PD modelling during the induction phase. This strategy may aid in optimizing ADM therapy for IBD.

Machine Learning for Predicting Biologic Agent Efficacy in Ulcerative Colitis: An Analysis for Generalizability and Combination with Computational Models.

Machine learning (ML) has been applied to predict the efficacy of biologic agents in ulcerative colitis (UC). ML can offer precision, personalization, efficiency, and automation. Moreover, it can improve decision support in predicting clinical outcomes. However, it faces challenges related to data quality and quantity, overfitting, generalization, and interpretability. This paper comments on two recent ML models that predict the efficacy of vedolizumab and ustekinumab in UC. Models that consider multiple pathways, multiple ethnicities, and combinations of real-world and clinical trial data are required for optimal shared decision-making and precision medicine. This paper also highlights the potential of combining ML with computational models to enhance clinical outcomes and personalized healthcare. Key Insights: (1) ML offers precision, personalization, efficiency, and decision support for predicting the efficacy of biologic agents in UC. (2) Challenging aspects in ML prediction include data quality, overfitting, and interpretability. (3) Multiple pathways, multiple ethnicities, and combinations of real-world and clinical trial data should be considered in predictive models for optimal decision-making. (4) Combining ML with computational models may improve clinical outcomes and personalized healthcare.

Gene-Based Predictive Modelling for Enhanced Detection of Systemic Lupus Erythematosus Using CNN-Based DL Algorithm.

Systemic Lupus Erythematosus (SLE) is a multifaceted autoimmune disease that presents with a diverse array of clinical signs and unpredictable disease progression. Conventional diagnostic methods frequently fall short in terms of sensitivity and specificity, which can result in delayed diagnosis and less-than-optimal management. In this study, we introduce a novel approach for improving the identification of SLE through the use of gene-based predictive modelling and Stacked deep learning classifiers. The study proposes a new method for diagnosing SLE using Stacked Deep Learning Classifiers (SDLC) trained on Gene Expression Omnibus (GEO) database data. By combining transcriptomic data from GEO with clinical features and laboratory results, the SDLC model achieves a remarkable accuracy value of 0.996, outperforming traditional methods. Individual models within the SDLC, such as SBi-LSTM and ACNN, achieved accuracies of 92% and 95%, respectively. The SDLC's ensemble learning approach allows for identifying complex patterns in multi-modal data, enhancing accuracy in diagnosing SLE. This study emphasises the potential of deep learning methods, in conjunction with open repositories like GEO, to advance the diagnosis and management of SLE. Overall, this research shows strong performance and potential for improving precision medicine in managing SLE.

Challenges and Opportunities for Precision Surgery for Colorectal Liver Metastases.

The incidence of colorectal cancer and colorectal liver metastases (CRLM) is increasing globally due to an interaction of environmental and genetic factors. A minority of patients with CRLM have surgically resectable disease, but for those who have resection as part of multimodal therapy for their disease, long-term survival has been shown. Precision surgery-the idea of careful patient selection and targeting of surgical intervention, such that treatments shown to be proven to benefit on a population level are the optimal treatment for each individual patient-is the new paradigm of care. Key to this is the understanding of tumour molecular biology and clinically relevant mutations, such as KRAS, BRAF, and microsatellite instability (MSI), which can predict poorer overall outcomes and a poorer response to systemic therapy. The emergence of immunotherapy and hepatic artery infusion (HAI) pumps show potential to convert previously unresectable disease to resectable disease, in addition to established systemic and locoregional therapies, but the surgeon must be wary of poor-quality livers and the spectre of post-hepatectomy liver failure (PHLF). Volume modulation, a cornerstone of hepatic surgery for a generation, has been given a shot in the arm with the advent of liver venous depletion (LVD) ensuring significantly more hypertrophy of the future liver remnant (FLR). The optimal timing of liver resection for those patients with synchronous disease is yet to be truly established, but evidence would suggest that those patients requiring complex colorectal surgery and major liver resection are best served with a staged approach. In the operating room, parenchyma-preserving minimally invasive surgery (MIS) can dramatically reduce the surgical insult to the patient and lead to better perioperative outcomes, with quicker return to function.