Collective mitochondrial dynamics resolve conflicting cellular tensions: From plants to general principles.

Mitochondria play diverse and essential roles in eukaryotic cells, and plants are no exception. Plant mitochondria have several differences from their metazoan and fungal cousins: they often exist in a fragmented state, move rapidly on actin rather than microtubules, have many plant-specific metabolic features and roles, and usually contain only a subset of the complete mtDNA genome, which itself undergoes frequent recombination. This arrangement means that exchange and complementation is essential for plant mitochondria, and recent work has begun to reveal how their collective dynamics and resultant "social networks" of encounters support this exchange, connecting plant mitochondria in time rather than in space. This review will argue that this social network perspective can be extended to a "societal network", where mitochondrial dynamics are an essential part of the interacting cellular society of organelles and biomolecules. Evidence is emerging that mitochondrial dynamics allow optimal resolutions to competing cellular priorities; we will survey this evidence and review potential future research directions, highlighting that plant mitochondria can help reveal and test principles that apply across other kingdoms of life. In parallel with this fundamental cell biology, we also highlight the translational "One Health" importance of plant mitochondrial behaviour - which is exploited in the production of a vast amount of crops consumed worldwide - and the potential for multi-objective optimisation to understand and rationally re-engineer the evolved resolutions to these tensions.

Dimensionality reduction approach for many-objective epistasis analysis.

In epistasis analysis, single-nucleotide polymorphism-single-nucleotide polymorphism interactions (SSIs) among genes may, alongside other environmental factors, influence the risk of multifactorial diseases. To identify SSI between cases and controls (i.e. binary traits), the score for model quality is affected by different objective functions (i.e. measurements) because of potential disease model preferences and disease complexities. Our previous study proposed a multiobjective approach-based multifactor dimensionality reduction (MOMDR), with the results indicating that two objective functions could enhance SSI identification with weak marginal effects. However, SSI identification using MOMDR remains a challenge because the optimal measure combination of objective functions has yet to be investigated. This study extended MOMDR to the many-objective version (i.e. many-objective MDR, MaODR) by integrating various disease probability measures based on a two-way contingency table to improve the identification of SSI between cases and controls. We introduced an objective function selection approach to determine the optimal measure combination in MaODR among 10 well-known measures. In total, 6 disease models with and 40 disease models without marginal effects were used to evaluate the general algorithms, namely those based on multifactor dimensionality reduction, MOMDR and MaODR. Our results revealed that the MaODR-based three objective function model, correct classification rate, likelihood ratio and normalized mutual information (MaODR-CLN) exhibited the higher 6.47% detection success rates (Accuracy) than MOMDR and higher 17.23% detection success rates than MDR through the application of an objective function selection approach. In a Wellcome Trust Case Control Consortium, MaODR-CLN successfully identified the significant SSIs (P < 0.001) associated with coronary artery disease. We performed a systematic analysis to identify the optimal measure combination in MaODR among 10 objective functions. Our combination detected SSIs-based binary traits with weak marginal effects and thus reduced spurious variables in the score model. MOAI is freely available at https://sites.google.com/view/maodr/home.

C-RCPred: a multi-objective algorithm for interactive secondary structure prediction of RNA complexes integrating user knowledge and SHAPE data.

RNAs can interact with other molecules in their environment, such as ions, proteins or other RNAs, to form complexes with important biological roles. The prediction of the structure of these complexes is therefore an important issue and a difficult task. We are interested in RNA complexes composed of several (more than two) interacting RNAs. We show how available knowledge on the considered RNAs can help predict their secondary structure. We propose an interactive tool for the prediction of RNA complexes, called C-RCPRed, that considers user knowledge and probing data (which can be generated experimentally or artificially). C-RCPred is based on a multi-objective optimization algorithm. Through an extensive benchmarking procedure, which includes state-of-the-art methods, we show the efficiency of the multi-objective approach and the positive impact of considering user knowledge and probing data on the prediction results. C-RCPred is freely available as an open-source program and web server on the EvryRNA website (https://evryrna.ibisc.univ-evry.fr).

Distributed multi-objective optimization for SNP-SNP interaction detection.

The detection of complex interactions between single nucleotide polymorphisms (SNPs) plays a vital role in genome-wide association analysis (GWAS). The multi-objective evolutionary algorithm is a promising technique for SNP-SNP interaction detection. However, as the scale of SNP data further increases, the exponentially growing search space gradually becomes the dominant factor, causing evolutionary algorithm (EA)-based approaches to fall into local optima. In addition, multi-objective genetic operations consume significant amounts of time and computational resources. To this end, this study proposes a distributed multi-objective evolutionary framework (DM-EF) to identify SNP-SNP interactions on large-scale datasets. DM-EF first partitions the entire search space into several subspaces based on a space-partitioning strategy, which is nondestructive because it guarantees that each feasible solution is assigned to a specific subspace. Thereafter, each subspace is optimized using a multi-objective EA optimizer, and all subspaces are optimized in parallel. A decomposition-based multi-objective firework optimizer (DCFWA) with several problem-guided operators was designed. Finally, the final output is selected from the Pareto-optimal solutions in the historical search of each subspace. DM-EF avoids the preference for a single objective function, handles the heavy computational burden, and enhances the diversity of the population to avoid local optima. Notably, DM-EF is load-balanced and scalable because it can flexibly partition the space according to the number of available computational nodes and problem size. Experiments on both artificial and real-world datasets demonstrate that the proposed method significantly improves the search speed and accuracy.

Emulation of Quantitative Systems Pharmacology models to accelerate virtual population inference in immuno-oncology.

Quantitative Systems Pharmacology (QSP) models are increasingly being applied for target discovery and dose selection in immuno-oncology (IO). Typical application involves virtual trial, a simulation of a virtual population of hundreds of model instances with model inputs reflecting individual variability. While the structure of the model and initial parameterisation are based on literature describing the underlying biology, calibration of the virtual population by existing clinical data is frequently required to create tumour and patient population specific model instances. Since comparison of a virtual trial with clinical output requires hundreds of large-scale, non-linear model evaluations, the inference of a virtual population is computationally expensive, frequently becoming a bottleneck. Here, we present novel approach to virtual population inference in IO using emulation of the QSP model and an objective function based on Kolmogorov-Smirnov statistics to maximise congruence of simulated and observed clinical tumour size distributions. We sample the parameter space of a QSP IO model to collect a set of tumour growth time profiles. We evaluate performance of several machine learning approaches in interpolating these time profiles and create a surrogate model, which computes tumor growth profiles faster than the original model and allows examination of tens of millions of virtual patients. We use the surrogate model to infer a virtual population maximising congruence with the waterfall plot of a pembrolizumab clinical trial. We believe that our approach is applicable not only in QSP IO, but also in other applications where virtual populations need to be inferred for computationally expensive mechanistic models.

Integrating transformers and many-objective optimization for drug design.

BACKGROUND: Drug design is a challenging and important task that requires the generation of novel and effective molecules that can bind to specific protein targets. Artificial intelligence algorithms have recently showed promising potential to expedite the drug design process. However, existing methods adopt multi-objective approaches which limits the number of objectives. RESULTS: In this paper, we expand this thread of research from the many-objective perspective, by proposing a novel framework that integrates a latent Transformer-based model for molecular generation, with a drug design system that incorporates absorption, distribution, metabolism, excretion, and toxicity prediction, molecular docking, and many-objective metaheuristics. We compared the performance of two latent Transformer models (ReLSO and FragNet) on a molecular generation task and show that ReLSO outperforms FragNet in terms of reconstruction and latent space organization. We then explored six different many-objective metaheuristics based on evolutionary algorithms and particle swarm optimization on a drug design task involving potential drug candidates to human lysophosphatidic acid receptor 1, a cancer-related protein target. CONCLUSION: We show that multi-objective evolutionary algorithm based on dominance and decomposition performs the best in terms of finding molecules that satisfy many objectives, such as high binding affinity and low toxicity, and high drug-likeness. Our framework demonstrates the potential of combining Transformers and many-objective computational intelligence for drug design.

Cognitive impairment following breast cancer treatments: an umbrella review.

OBJECTIVES: Cancer-related cognitive impairment (CRCI) refers to a cognitive decline associated with cancer or its treatments. While research into CRCI is expanding, evidence remains scattered due to differences in study designs, methodologies, and definitions. The present umbrella review aims to provide a comprehensive overview of the current evidence regarding the impact of different breast cancer therapies on cognitive functioning, with a particular focus on the interplay among objective cognitive deficits (ie, measured with standardized tests), subjective cognitive concerns, (ie, self-reported), and other mediating psycho-physical factors. METHODS: The search was made in Pubmed, Embase, and Scopus for articles published until July 2023, following the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-analysis protocol. RESULTS: Chemotherapy and endocrine therapy appear consistently associated with CRCI in patients with breast cancer, primarily affecting memory, attention/concentration, executive functioning, and processing speed. Subjective cognitive concerns were often found weakly or not associated with neuropsychological test results, while overall CRCI seemed consistently associated with psychological distress, fatigue, sleep quality, and inflammatory and biological factors. CONCLUSION: Current evidence suggests that CRCI is common after chemotherapy and endocrine therapy for breast cancer. However, heterogeneity in study designs and the scarcity of studies on more recent treatments such as targeted therapies and immunotherapies, highlight the need for more systematic and harmonized studies, possibly taking into account the complex and multifactorial etiology of CRCI. This may provide valuable insights into CRCI's underlying mechanisms and potential new ways to treat it.

The cancer caregiving burden trajectory over time: varying experiences of perceived versus objectively measured burden.

INTRODUCTION: The objective of this study was to comprehensively understand the burden experienced by caregivers (CGs) providing home-based, end-of-life care to patients with cancer. We examined the relationship between objective and subjective burden including whether and how burden changes over time. METHODS: A case series of terminal cancer patient-caregiver dyads (n = 223) were recruited from oncology clinics and followed for 12 months or until patient death. Data were collected every other week and in-person from CGs in their homes using quantitative surveys, diaries, and monthly structured observations. RESULTS: Bivariate correlations revealed a significant association between subjective burden and activities of daily living (ADLs), instrumental activities of daily living (IADL), high-intensity tasks, and time spent on ADLs; these correlations varied over time. Models examining the slope of subjective burden revealed little systematic change; spouse caregiver and patient functional limitations were positively, and Black caregiver was negatively associated with subjective burden. Generally, the slopes for measures of objective burden were significant and positive. Models showed subjective burden was positively associated with most measures of objective burden both within caregiver (concurrent measures were positively associated) and between CGs (those with higher subjective also had higher objective). CONCLUSIONS: Cancer caregiving is dynamic; CGs must adjust to the progression of the patient's disease. We found an association between subjective and objective burden both within and between CGs. Black CGs were more likely to report lower subjective burden compared to their White counterparts. More detailed investigation of the sociocultural components that affect caregiver experience of burden is needed to better understand how and where to best intervene with targeted supportive care services.

Multi-Objective Optimization for Rapid Identification of Novel Compound Metals for Interconnect Applications.

Interconnect materials play the critical role of routing energy and information in integrated circuits. However, established bulk conductors, such as copper, perform poorly when scaled down beyond 10 nm, limiting the scalability of logic devices. Here, a multi-objective search is developed, combined with first-principles calculations, to rapidly screen over 15,000 materials and discover new interconnect candidates. This approach simultaneously optimizes the bulk electronic conductivity, surface scattering time, and chemical stability using physically motivated surrogate properties accessible from materials databases. Promising local interconnects are identified that have the potential to outperform ruthenium, the current state-of-the-art post-Cu material, and also semi-global interconnects with potentially large skin depths at the GHz operation frequency. The approach is validated on one of the identified candidates, CoPt, using both ab initio and experimental transport studies, showcasing its potential to supplant Ru and Cu for future local interconnects.

Optimization of synchrotron radiation parameters using swarm intelligence and evolutionary algorithms.

Alignment of each optical element at a synchrotron beamline takes days, even weeks, for each experiment costing valuable beam time. Evolutionary algorithms (EAs), efficient heuristic search methods based on Darwinian evolution, can be utilized for multi-objective optimization problems in different application areas. In this study, the flux and spot size of a synchrotron beam are optimized for two different experimental setups including optical elements such as lenses and mirrors. Calculations were carried out with the X-ray Tracer beamline simulator using swarm intelligence (SI) algorithms and for comparison the same setups were optimized with EAs. The EAs and SI algorithms used in this study for two different experimental setups are the Genetic Algorithm (GA), Non-dominated Sorting Genetic Algorithm II (NSGA-II), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC). While one of the algorithms optimizes the lens position, the other focuses on optimizing the focal distances of Kirkpatrick-Baez mirrors. First, mono-objective evolutionary algorithms were used and the spot size or flux values checked separately. After comparison of mono-objective algorithms, the multi-objective evolutionary algorithm NSGA-II was run for both objectives - minimum spot size and maximum flux. Every algorithm configuration was run several times for Monte Carlo simulations since these processes generate random solutions and the simulator also produces solutions that are stochastic. The results show that the PSO algorithm gives the best values over all setups.

Comparison of the Value of Four Objective Nutritional Indices in Assessing the Long-Term Prognosis of Elderly Patients with Heart Failure with Preserved Ejection Fraction.

Background: The long-term prognosis of heart failure with preserved ejection fraction (HFpEF) is influenced by malnutrition. Currently, there's a deficit in objective and comprehensive nutritional assessment methods to evaluate the nutritional status and predicting the long-term outcomes of HFpEF patients. Methods: Our retrospective study included two hundred and eighteen elderly HFpEF patients admitted to the cardiovascular ward at the Air Force Medical Centre from January 2016 to December 2021. Based on follow-up outcomes, patients were categorized into all-cause death (99 cases) and Survival (119 cases) groups. We compared general data, laboratory results, and nutritional indexes between groups. Differences in subgroups based on Triglyceride-Total Cholesterol-Body Weight Index (TCBI), Geriatric Nutritional Risk Index (GNRI), Prognostic Nutritional Index (PNI), and Controlled Nutrition Score (CONUT) were analyzed using Kaplan-Meier survival curves and log-rank test. COX regression was used to identify all-cause mortality risk factors, and the predictive accuracy of the four nutritional indices was assessed using receiver operating characteristic (ROC) curves and Delong test analysis. Results: A total of 101 (45.41%) HFpEF patients experienced all-cause mortality during 59.02 ± 1.79 months of follow-up. The all-cause mortality group exhibited lower GNRI and PNI levels, and higher CONUT levels than the Survival group (p < 0.05). Kaplan-Meier analysis revealed lower cumulative survival in the low GNRI ( ≤ 96.50) and low PNI ( ≤ 43.75) groups, but higher in the low CONUT ( ≤ 2) group, compared to their respective medium and high-value groups. Multifactorial COX regression identified low PNI ( ≤ 43.75) as an independent all-cause mortality risk factor in elderly HFpEF patients. ROC and Delong's test indicated PNI (area under the curve [AUC] = 0.698, 95% confidence interval [CI] 0.629-0.768) as a more effective predictor of all-cause mortality than TCBI (AUC = 0.533, 95% CI 0.456-0.610) and CONUT (AUC = 0.621, 95% CI 0.547-0.695; p < 0.05). However, there was no significant difference compared to GNRI (AUC = 0.663, 95% CI 0.590-0.735; p > 0.05). Conclusions: In elderly HFpEF patients a PNI ≤ 43.75 was identified as an independent risk factor for all-cause mortality. Moreover, PNI demonstrates superior prognostic performance in predicting all-cause mortality in elderly patients with HFpEF when compared to TCBI, GNRI, and COUNT.

The efficacy and safety of vincristine, irinotecan and anlotinib in Epithelioid Sarcoma.

BACKGROUND: Epithelioid sarcoma is a rare soft tissue sarcoma characterized by SMARCB1/INI1 deficiency. Much attention has been paid to the selective EZH2 inhibitor tazemetostat, where other systemic treatments are generally ignored. To explore alternative treatment options, we studied the effects of irinotecan-based chemotherapy in a series of epithelioid sarcoma patients. METHODS: We retrospectively reviewed data from patients with metastatic or unresectable epithelioid sarcoma at the Peking University People's Hospital treated with irinotecan (50 mg/m2/d d1-5 Q3W) in combination with Anlotinib (12 mg Qd, 2 weeks on and 1 week off) from July 2015 to November 2021. RESULTS: A total of 54 courses were administered. With a median follow up of 21.2 months (95% CI, 12.2, 68.1), the 5-year overall survival rate was 83.3%. Five of eight (62.5%) patients presented with unresectable localized lesions, including local tumor thrombosis and lymphatic metastasis. The other patients had unresectable pulmonary metastases. Six of eight (75%) patients had progressed following two lines of systemic therapy. The objective response rate reached 37.5% (three of eight patients) while stabilized disease was observed in 62.5% (five of eight) of patients. No patient had progressed at initial evaluation. At the last follow up, two patients were still using the combination and three patients had ceased the therapy due to toxicities such as diarrhea, nausea, and emesis. One patient changed to tazemetostat for maintenance and one patient stopped treatment due to coronavirus disease 2019 (COVID-19). Another patient stopped therapy as residual lesions had been radiated. CONCLUSIONS: The combination of irinotecan and Anlotinib as a salvage regimen may be considered another effective treatment option for refractory epithelioid sarcoma. TRIAL REGISTRATION: This study was approved in the Medical Ethics Committee of Peking University People's Hospital on October 28, 2022 (No.: 2022PHD015-002). The study was registered in Clinicaltrials.gov with identifier no. NCT05656222.

Predict progression free survival and overall survival using objective response rate for anti-PD1/PDL1 therapy development.

In oncology anti-PD1 / PDL1 therapy development for solid tumors, objective response rate (ORR) is commonly used clinical endpoint for early phase study decision making, while progression free survival (PFS) and overall survival (OS) are widely used for late phase study decision making. Developing predictive models to late phase outcomes such as median PFS (mPFS) and median OS (mOS) based on early phase clinical outcome ORR could inform late phase study design optimization and probability of success (POS) evaluation. In existing literature, there are ORR / mPFS / mOS association and surrogacy investigations with limited number of included clinical trials. In this paper, without establishing surrogacy, we attempt to predict late phase survival (mPFS and mOS) based on early efficacy ORR and optimize late phase trial design for anti-PD1 / PDL1 therapy development. In order to include adequate number of eligible clinical trials, we built a comprehensive quantitative clinical trial landscape database (QLD) by combining information from different sources such as clinicaltrial.gov, publications, company press releases for relevant indications and therapies. We developed a generalizable algorithm to systematically extract structured data for scientific accuracy and completeness. Finally, more than 150 late phase clinical trials were identified for ORR / mPFS (ORR / mOS) predictive model development while existing literature included at most 50 trials. A tree-based machine learning regression model has been derived to account for ORR / mPFS (ORR / mOS) relationship heterogeneity across tumor type, stage, line of therapy, treatment class and borrow strength simultaneously when homogeneity persists. The proposed method ensures that the predictive model is robust and have explicit structure for clinical interpretation. Through cross validation, the average predictive mean square error of the proposed model is competitive to random forest and extreme gradient boosting methods and outperforms commonly used additive or interaction linear regression models. An example application of the proposed ORR / mPFS (ORR / mOS) predictive model on late phase trial POS evaluation for anti-PD1 / PDL1 combination therapy was illustrated.

Discrepancy between objective and subjective cognition and its association with the trajectory of symptoms and functioning in depressive patients.

BACKGROUND: Discrepancy between objective and subjective cognitive deficit is common among patients with major depressive disorders (MDDs) and may play a key role in the mechanism linking cognition with recovery of symptom and psychosocial function. This study, therefore, explores the cognitive discrepancy, and its association with the trajectory of symptoms and functioning over a 6-month period. METHODS: We used data from the Prospective Research Observation to Assess Cognition in Treated patients with MDD (PROACT) study, from which 598 patients were included. Cognitive discrepancy scores were computed using a novel methodology, with positive values indicating more subjective than objective deficit (i.e. 'underestimation') and negative values indicating more objective than subjective difficulties (i.e. 'overestimation'). Linear growth curve models were employed to examine the association of the cognitive discrepancy with the trajectory of depressive symptoms, psychosocial function, and quality of life. RESULTS: About 68% of patients displayed disproportionately more objective than subjective cognitive deficit at baseline, and the mean cognitive discrepancy score was -1.4 (2.7). Overestimation was associated with a faster decrease of HDRS-17 (ß = -0.46, p = 0.002) and a faster decrease of psychosocial function in social life (ß = -0.13, p = 0.013) and family life (ß = -0.12, p = 0.026), and a greater improvement of EQ-5D utility score (ß = 0.01, p < 0.001). CONCLUSION: We found a lower sensitivity of cognitive deficit at baseline and its decrease was associated with better health outcomes. Our findings have clinical implications of the necessity to assess both subjective and objective cognition for identification and categorization and to incorporate cognitive and psychological therapies for optimized treatment outcomes.

Chemotherapy-induced gut microbiome disruption, inflammation, and cognitive decline in female patients with breast cancer.

Chemotherapy is notorious for causing behavioral side effects (e.g., cognitive decline). Notably, the gut microbiome has recently been reported to communicate with the brain to affect behavior, including cognition. Thus, the aim of this clinical longitudinal observational study was to determine whether chemotherapy-induced disruption of the gut microbial community structure relates to cognitive decline and circulating inflammatory signals. Fecal samples, blood, and cognitive measures were collected from 77 patients with breast cancer before, during, and after chemotherapy. Chemotherapy altered the gut microbiome community structure and increased circulating TNF-α. Both the chemotherapy-induced changes in microbial relative abundance and decreased microbial diversity were related to elevated circulating pro-inflammatory cytokines TNF-α and IL-6. Participants reported subjective cognitive decline during chemotherapy, which was not related to changes in the gut microbiome or inflammatory markers. In contrast, a decrease in overall objective cognition was related to a decrease in microbial diversity, independent of circulating cytokines. Stratification of subjects, via a reliable change index based on 4 objective cognitive tests, identified objective cognitive decline in 35% of the subjects. Based on a differential microbial abundance analysis, those characterized by cognitive decline had unique taxonomic shifts (Faecalibacterium, Bacteroides, Fusicatenibacter, Erysipelotrichaceae UCG-003, and Subdoligranulum) over chemotherapy treatment compared to those without cognitive decline. Taken together, gut microbiome change was associated with cognitive decline during chemotherapy, independent of chemotherapy-induced inflammation. These results suggest that microbiome-related strategies may be useful for predicting and preventing behavioral side effects of chemotherapy.

Body mass index moderates the association between diabetes distress and objective self-management behaviours in adolescents with type 1 diabetes and elevated A1Cs.

OBJECTIVE: To examine the cross-sectional associations between diabetes distress, BMI (zBMI; BMI z-score), objectively measured mean daily blood glucose readings and insulin boluses administered, and A1C in adolescents with type 1 diabetes (T1D) using insulin pumps. METHODS: T1D self-management behaviour data were downloaded from adolescents' (N = 79) devices and mean daily frequency of blood glucose readings and insulin boluses were calculated. Diabetes distress was measured (Problem Areas in Diabetes-Teen questionnaire [PAID-T]), A1C collected, and zBMI calculated from height and weight. Three multiple linear regressions were performed with blood glucose readings, insulin boluses, and A1C as the three dependent variables and covariates (age, T1D duration), zBMI, diabetes distress, and the diabetes distress x zBMI interaction as independent variables. RESULTS: Participants (55.7% female) were 14.9 ± 1.9 years old with T1D for 6.6 ± 3.4 years. zBMI moderated the relationship between diabetes distress and mean daily insulin boluses administered (b = -0.02, p = 0.02); those with higher zBMI and higher diabetes distress administered fewer daily insulin boluses. zBMI was not a moderator of the association between diabetes distress and blood glucose readings (b = -0.01, p = 0.29) or A1C (b = 0.002, p = 0.81). CONCLUSIONS: Using objective behavioural data is useful for identifying how adolescent diabetes distress and zBMI affect daily bolusing behaviour amongst adolescent insulin pump users. Although distinct interventions exist to improve T1D self-management or diabetes distress, none addresses them together while considering zBMI. Decreasing diabetes distress could be especially important for youth with high zBMI.

Beyond lectures and practical courses: Teaching pharmacology using imaginative pedagogical tools.

Pharmacology has broadened its scope considerably in recent decades. Initially, it was of interest to chemists, doctors and pharmacists. In recent years, however, it has been incorporated into the teaching of biologists, molecular biologists, biotechnologists, chemical engineers and many health professionals, among others. Traditional teaching methods, such as lectures or laboratory work, have been superseded by the use of new pedagogical approaches to enable a better conceptualization and understanding of the discipline. In this article, we present several new methods that have been used in Spanish universities. Firstly, we describe a teaching network that has allowed the sharing of pedagogical innovations in Spanish universities. A European experience to improve prescribing safety is described in detail. The use of popular films and medical TV series in biomedical students shows how these audiovisual resources can be helpful in teaching pharmacology. The use of virtual worlds is detailed to introduce this new approach to teaching. The increasingly important area of the social aspects of pharmacology is also considered in two sections, one devoted to social pharmacology and the other to the use of learning based on social services to improve understanding of this important area. Finally, the use of Objective Structured Clinical Evaluation in pharmacology allows to know how this approach can help to better evaluate clinical pharmacology students. In conclusion, this article allows to know new pedagogical methods resources used in some Spanish universities that may help to improve the teaching of pharmacology.

Overreporting of adherence to hepatitis C direct-acting antiviral therapy and sustained virologic response among people who inject drugs in the HERO study.

BACKGROUND: Self-reported adherence to direct-acting antivirals (DAAs) to treat hepatitis C virus (HCV) among persons who inject drugs (PWID) is often an overreport of objectively measured adherence. The association of such overreporting with sustained virologic response (SVR) is understudied. This study among PWID aimed to determine a threshold of overreporting adherence that optimally predicts lower SVR rates, and to explore correlates of the optimal overreporting threshold. METHODS: This study analyzed per-protocol data of participants with adherence data (N = 493) from the HERO (Hepatitis C Real Options) study. Self-reported and objective adherence to a 12-week DAA regimen were measured using visual analogue scales and electronic blister packs, respectively. The difference (Δ) between self-reported and objectively measured adherence was calculated. We used the Youden index based on receiver operating characteristic (ROC) curve analysis to identify an optimal threshold of overreporting for predicting lower SVR rates. Factors associated with the optimal threshold of overreporting were identified by comparing baseline characteristics between participants at/above versus those below the threshold. RESULTS: The self-reported, objective, and Δ adherence averages were 95.1% (SD = 8.9), 75.9% (SD = 16.3), and 19.2% (SD = 15.2), respectively. The ≥ 25% overreporting threshold was determined to be optimal. The SVR rate was lower for ≥ 25% vs. < 25% overreporting (86.7% vs. 95.8%, p <.001). The factors associated with ≥ 25% Δ adherence were unemployment; higher number of days and times/day of injecting drugs; higher proportion of positive urine drug screening for amphetamine, methamphetamine, and oxycodone, and negative urine screening for THC (tetrahydrocannabinol)/cannabis. CONCLUSIONS: Self-reported DAA adherence was significantly greater than objectively measured adherence among PWID by 19.2%. Having ≥ 25% overreported adherence was associated with optimal prediction of lower SVR rates. PWID with risk factors for high overreporting may need to be more intensively managed to promote actual adherence.

Exploring the Role of Target Expression in Treatment Efficacy of Antibody-Drug Conjugates (ADCs) in Solid Cancers: A Comprehensive Review.

PURPOSE OF REVIEW: Antibody-drug conjugates (ADCs) offer a promising path for cancer therapy, leveraging the specificity of monoclonal antibodies and the cytotoxicity of linked drugs. The success of ADCs hinges on precise targeting of cancer cells based on protein expression levels. This review explores the relationship between target protein expression and ADC efficacy in solid tumours, focusing on results of clinical trials conducted between January 2019 and May 2023. RECENT FINDINGS: We hereby highlight approved ADCs, revealing their effectiveness even in low-expressing target populations. Assessing target expression poses challenges, owing to variations in scoring systems and biopsy types. Emerging methods, like digital image analysis, aim to standardize assessment. The complexity of ADC pharmacokinetics, tumour dynamics, and off-target effects emphasises the need for a balanced approach. This review underscores the importance of understanding target protein dynamics and promoting standardized evaluation methods in shaping the future of ADC-based cancer therapies.

Disease biomarker identification based on sample network optimization.

A large amount of evidence shows that biomarkers are discriminant features related to disease development. Thus, the identification of disease biomarkers has become a basic problem in the analysis of complex diseases in the medical fields, such as disease stage judgment, disease diagnosis and treatment. Research based on networks have become one of the most popular methods. Several algorithms based on networks have been proposed to identify biomarkers, however the networks of genes or molecules ignored the similarities and associations among the samples. It is essential to further understand how to construct and optimize the networks to make the identified biomarkers more accurate. On this basis, more effective strategies can be developed to improve the performance of biomarkers identification. In this study, a multi-objective evolution algorithm based on sample similarity networks has been proposed for disease biomarker identification. Specifically, we design the sample similarity networks to extract the structural characteristic information among samples, which used to calculate the influence of the sample to each class. Besides, based on the networks and the group of biomarkers we choose in every iteration, we can divide samples into different classes by the importance for each class. Then, in the process of evolution algorithm population iteration, we develop the elite guidance strategy and fusion selection strategy to select the biomarkers which make the sample classification more accurate. The experiment results on the five gene expression datasets suggests that the algorithm we proposed is superior over some state-of-the-art disease biomarker identification methods.