Ilne Ai Purana Adel, Hanggara Budi Utomo, Ninik Setiyowati
Patients with diabetes mellitus frequently experience anxiety that negatively affects their quality of life and treatment adherence. Psychological resources such as resilience and self-efficacy are known to play protective roles, yet the psychological mechanisms explaining their influence on anxiety remain not fully understood. This study examined the mediating role of growth mindset in the relationship between resilience, self-efficacy, and anxiety among patients with diabetes mellitus. A cross-sectional correlational quantitative design using Structural Equation Modeling involved 160 patients with diabetes mellitus at hospitals in Kediri, selected through cluster sampling. Instruments included the resilience scale, self-efficacy scale, Growth Mindset Scale, and anxiety scale. Data analysis was conducted using SmartPLS 4.0. Resilience showed a significant negative effect on anxiety, while self-efficacy demonstrated a non-significant negative effect. Resilience and self-efficacy exhibited significant positive effects on growth mindset, and growth mindset demonstrated the strongest negative effect on anxiety. Growth mindset partially mediated the role of resilience on anxiety and fully mediated the role of self-efficacy on anxiety. Growth mindset functions as an active psychological mechanism in reducing anxiety related to disease burden. These findings provide an empirical basis for developing growth mindset-based psychoeducational interventions to strengthen self-efficacy and psychological resilience, thereby reducing anxiety, and improving treatment adherence among patients with diabetes mellitus.
Biomolecular networks underpin emerging technologies in synthetic biology-from robust biomanufacturing and metabolic engineering to smart therapeutics and cell-based diagnostics-and also provide a mechanistic language for understanding complex dynamics in natural and ecological systems. Yet designing chemical reaction networks (CRNs) that implement a desired dynamical function remains largely manual: while a proposed network can be checked by simulation, the reverse problem of discovering a network from a behavioral specification is difficult, requiring substantial human insight to navigate a vast space of topologies and kinetic parameters with nonlinear and possibly stochastic dynamics. Here we introduce GenAI-Net, a generative AI framework that automates CRN design by coupling an agent that proposes reactions to simulation-based evaluation defined by a user-specified objective. GenAI-Net efficiently produces novel, topologically diverse solutions across multiple design tasks, including dose responses, complex logic gates, classifiers, oscillators, and robust perfect adaptation in deterministic and stochastic settings (including noise reduction). By turning specifications into families of circuit candidates and reusable motifs, GenAI-Net provides a general route to programmable biomolecular circuit design and accelerates the translation from desired function to implementable mechanisms.
Malignant tumors exhibit complex pathogenesis, yet classical oncological theories remain fragmented, failing to provide a unifying framework to address this complexity. This gap limits the utility and translational potential of the prevailing "confront-and-eradicate" therapeutic paradigm, constraining transformative therapeutic breakthroughs and driving the emergence of acquired and recurrent drug resistance. Here, we propose the Tumor Existential Crisis-Driven Survival (ECDS) theory, anchored in the core proposition that impairment of Existential Stability drives the compensatory hyperactivation of Survival Capacity. This framework defines three foundational constructs (Existential Stability, Survival Capacity, and Existence Threshold) and three guiding principles, unifying and integrating canonical core theories of tumorigenesis. It delineates the dynamic coupling between declining Existential Stability and escalating Survival Capacity during tumor evolution, reinterprets the hierarchical activation of the well-established 14 cancer hallmarks, elucidates the redundancy of survival signaling pathways that underpins intratumoral and intertumoral heterogeneity, and unravels the "hierarchical leap" in therapeutic resistance. By reframing tumors as "Existential Stability erosion-driven passive survival systems" rather than "intrinsically aggressive cellular aggregates", ECDS challenges prevailing dogma, uncovers tumors' intrinsic vulnerability, and establishes a robust meta-theoretical foundation for both basic cancer research and translational clinical management.
Understanding disease-gene associations is essential for unravelling disease mechanisms and advancing diagnostics and therapeutics. Traditional approaches based on manual curation and literature review are labour-intensive and not scalable, prompting the use of machine learning on large biomedical data. In particular, graph neural networks (GNNs) have shown promise for modelling complex biological relationships. To address limitations in existing models, we propose GLaDiGAtor (Graph Learning-bAsed DIsease-Gene AssociaTiOn pRediction), a novel GNN framework with an encoder-decoder architecture for disease-gene association prediction. GLaDiGAtor constructs a heterogeneous biological graph integrating gene-gene, disease-disease, and gene-disease interactions from curated databases, and enriches each node with contextual features from well-known language models (ProtT5 for protein sequences and BioBERT for disease text). In evaluations, our model achieves superior predictive accuracy and generalisation, outperforming 14 existing methods. Literature-supported case studies confirm the biological relevance of high-confidence novel predictions, highlighting GLaDiGAtor's potential to discover candidate disease genes. These results underscore the power of graph convolutional networks in biomedical informatics and may ultimately facilitate drug discovery by revealing new gene-disease links. The source code and processed datasets are publicly available at https://github.com/HUBioDataLab/GLaDiGAtor.
Traditional cellular force-sensing techniques, such as traction force microscopy (TFM), are predominantly limited to measuring linear tractions, overlooking and technically unable to capture the nanoscale torsional forces that are critical in cell-matrix interactions. Here, we introduce a nanodiamond-enabled torsion microscopy (DTM) that integrates nitrogen-vacancy (NV) centers as orientation markers with micropillar arrays to decouple and quantify nanoscale rotational and translational motions induced by cells. This approach achieves high precision (~1.47 degree rotational accuracy and ~3.13*10-15 Nm torque sensitivity), enabling reconstruction of cellular torsional force fields and twisting energy distributions previously underestimated. Our findings reveal the widespread presence of torsional forces in cell-matrix interactions, introducing "cellular mechanical modes" where different adhesion patterns dictate the balance between traction- and torque- mediated mechanical energy transferred to the substrate. Notably, in immune cells like macrophages that generally exert low linear tractions, torque overwhelmingly dominates traction, highlighting a unique mechanical output for specific cellular functions. By uncovering these differential modes, DTM provides a versatile tool to advance biomechanical investigations, with potential applications in disease diagnostics and therapeutics.
William Hedley Thompson, Emelie Thern, Filip Gedin
et al.
Abstract This study applies network theory to registry data to identify prospective differences between individuals who develop long-term pain later in life and those who do not. The research is based on assessments of biological, psychological, and social variables in late adolescence during military conscription in Sweden. The analysis reveals significant differences in the network profiles of adolescent men who later developed long-term pain. These differences are reflected in several network-based outputs, including global, nodal, and edge levels, revealing a consistent picture of the pain-associated network profile. This profile demonstrates how those vulnerable to long-term pain have a specific configuration of variables that skew away from the rest of the population, mainly relating to psychosocial aspects.
Current clinical agent built on small LLMs, such as TxAgent suffer from a \textit{Context Utilization Failure}, where models successfully retrieve biomedical evidence due to supervised finetuning but fail to ground their diagnosis in that information. In this work, we propose the Executor-Analyst Framework, a modular architecture that decouples the syntactic precision of tool execution from the semantic robustness of clinical reasoning. By orchestrating specialized TxAgents (Executors) with long-context foundation models (Analysts), we mitigate the reasoning deficits observed in monolithic models. Beyond simple modularity, we demonstrate that a Stratified Ensemble strategy significantly outperforms global pooling by preserving evidentiary diversity, effectively addressing the information bottleneck. Furthermore, our stress tests reveal critical scaling insights: (1) a \textit{Context-Performance Paradox}, where extending reasoning contexts beyond 12k tokens introduces noise that degrades accuracy; and (2) the \textit{Curse of Dimensionality} in action spaces, where expanding toolsets necessitates hierarchical retrieval strategies. Crucially, our approach underscores the potential of training-free architectural engineering, achieving state-of-the-art performance on CURE-Bench without the need for expensive end-to-end finetuning. This provides a scalable, agile foundation for the next generation of trustworthy AI-driven therapeutics. Code has been released on https://github.com/June01/CureAgent.
Sharjeel Tahir, Judith Johnson, Jumana Abu-Khalaf
et al.
A prevalent shortfall among current empathic AI systems is their inability to recognize when verbal expressions may not fully reflect underlying emotional states. This is because the existing datasets, used for the training of these systems, focus on surface-level emotion recognition without addressing the complex verbal-visual incongruence (mismatch) patterns useful for empathic understanding. In this paper, we present E-THER, the first Person-Centered Therapy-grounded multimodal dataset with multidimensional annotations for verbal-visual incongruence detection, enabling training of AI systems that develop genuine rather than performative empathic capabilities. The annotations included in the dataset are drawn from humanistic approach, i.e., identifying verbal-visual emotional misalignment in client-counsellor interactions - forming a framework for training and evaluating AI on empathy tasks. Additional engagement scores provide behavioral annotations for research applications. Notable gains in empathic and therapeutic conversational qualities are observed in state-of-the-art vision-language models (VLMs), such as IDEFICS and VideoLLAVA, using evaluation metrics grounded in empathic and therapeutic principles. Empirical findings indicate that our incongruence-trained models outperform general-purpose models in critical traits, such as sustaining therapeutic engagement, minimizing artificial or exaggerated linguistic patterns, and maintaining fidelity to PCT theoretical framework.
Francesca Ballatore, Lorenzo Scolaris, Chiara Giverso
Glioblastoma Multiforme (GBM) is a highly aggressive brain tumour with limited therapeutic options and poor prognosis. This study presents a mathematical framework to investigate the efficacy of immunotherapy strategies based on cytotoxic T-lymphocyte (CTL) infusion. The model couples tumour and immune dynamics through a system of partial differential equations (PDEs), incorporating cell proliferation, diffusion, and chemotactic migration in response to TGF-$β$, a tumour-secreted signalling molecule. A reduced ordinary differential equation (ODE) model is first analysed to derive threshold conditions for tumour eradication, identifying critical infusion levels consistent with clinical data. Numerical bifurcation analysis explores the impact of parameter variations. The full PDE model is solved using the finite element method on simplified 2D domains, followed by sensitivity analyses to quantify parameter influence on tumour mass and volume. The model is then applied to a realistic 3D brain geometry reconstructed from patient-specific MRI and DTI data, accounting for anatomical anisotropy and tissue heterogeneity. Therapeutic scenarios are simulated with spatially localised lymphocyte infusion. Results highlight spatial variations in tumour growth and treatment response, with infusion intensity and tumour location critically influencing therapeutic outcomes. These findings emphasise the importance of personalised, spatially informed modelling in optimising immunotherapy protocols for GBM.
Biomedical research increasingly relies on heterogeneous, high-dimensional datasets, yet effective visualization remains hindered by fragmented code resources, steep programming barriers, and limited domain-specific guidance. Bizard is an open-source visualization code repository engineered to streamline data analysis in biomedical research. It aggregates a diverse array of executable visualization scripts, empowering researchers to select and tailor optimal graphical methods for their specific investigative demands. The platform features an intuitive interface equipped with sophisticated browsing and filtering capabilities, exhaustive tutorials, and interactive discussion forums that foster knowledge dissemination. Through its community-driven paradigm, Bizard promotes continual refinement and functional expansion, establishing itself as an essential resource for elevating biomedical data visualization and analytical standards. By harnessing Bizard's infrastructure, researchers can augment their visualization proficiency, propel methodological progress, and enhance interpretive rigor, ultimately accelerating precision medicine and personalized therapeutics. Bizard is freely accessible at https://openbiox.github.io/Bizard/.
Hafsa Wajih, Umul Baneen, Syeda Laiba Fatima
et al.
OBJECTIVES:
The objectives of the study were to determine balance and flexibility in young adults between the age group of 18 to 25 years.
METHODOLOGY:
It was a comparative cross-sectional study design. Ethical Review Committee approved the study after reviewing. The data was collected from a total of n = 370 healthy young adults after taking informed consent from the participants. Participants were divided into 2 groups through WHO activeness criteria. Y – Balance test was used to measure balance while sit and reach test and static flexibility tests were used to measure flexibility.
RESULTS:
The data was analyzed on SPSS version 21.0 and significance value was selected to be α = 0.05. Participants taken were from the age category of 18 to 25 years. Normality test was applied which showed majority of the data in non-normally distributed (p-value of Leg Length Composite Score, Right Leg Composite Score and Static Flexibility Score is less than 0.05). So, Mann Whitney U test was applied and results were computed. The results showed insignificant difference in Y – balance scores (Left Leg Composite Score p = 0.464, Right Leg Composite Score p = 0.780) and sit and reach scores (p = 0.093) of inactive individuals as compared to active individuals. Moreover, there was statistically insignificant difference in static flexibility (p = 0.879) of both groups with active participants against inactive participants.
CONCLUSION:
It was concluded that physical activity has no significant effect on balance and flexibility in young adults. However, taking participants of equal weight and height in active against inactive group may improve the results or by using quantitative tools to measure balance and flexibility and by using another method to screen out participants to be included in active and inactive groups the results could be improved further.
KEYWORDS:
Balance, Flexibility, Physical Activity, Young Adults
Vocational rehabilitation. Employment of people with disabilities, Therapeutics. Psychotherapy
Hassan Sarwar Hassan Sarwar, Anna Zaheer, Sahar Fatima
Background: A new air-borne pandemic COVID-19 had resulted in a large number of morbidity and deaths. Post-traumatic stress disorder used to begin three months after its origin and probably lasts for 6 months.
Objective: To determine the association of factors with post-traumatic stress disorder in COVID-19 survivors after getting normal confirmed by COVID19 negative test done through RTPCR diagnostic testing.
Methodology: According to Epitool a total of 165 COVID-19 survivors participated in this cross-sectional study. The non-probability convenient sampling approach was utilized. Demographic data was recorded using a self-made proforma while evaluation of post-traumatic stress disorder was done through IES-R scale.
Results: A total of 165 Covid-19 survivors took part in study, comprised upon 81 (49.1%) of men and 84 (50.9%) of females. The majority of COVID-19 survivors socioeconomically were from middle class in number of 148 (89.7%). About 66 (40.0%) encountered high impact post-traumatic stress disorder while surviving corona virus. COVID-19 survivors had trouble sleeping, being woken without cause, or over-slept, about 114 (69.1%) favored it whereas 51 (30.9%) opposed it. While 85 (51.5%) of COVID-19 survivors felt chest pain, tightness in chest or shortness of breath like symptoms after battling disease whereas 80 (48.5%) survivors had no impact.
Conclusion: The study determined that majority of COVID-19 survivors suffered from post-traumatic stress disorder had stronger association with the factors like nervousness, anxiousness and panic like stuff after surviving pandemic along with trouble in sleeping, staying asleep, awakened without reason or had overslept. While in comparison moderate association was configured between respiratory like symptoms including chest discomfort, heart beating and post-traumatic stress disorder in survivors who had battled out COVID-19.
Keywords: COVID-19, Post-Traumatic Stress Disorder, Sleep discomfort, Reverse Transcriptase Polymerase Chain Reaction, SARS-CoV-2.
Vocational rehabilitation. Employment of people with disabilities, Therapeutics. Psychotherapy
One of the most important perspectives explaining the effect of marital conflict on children is the Cognitive-Contextual Theory. Within the framework of this theory, the aims of the present study were to examine preschool children's perceptions of marital conflict; to investigate the effect of children's perceptions of marital conflict on their problem-solving skills; and to find an answer to the question of whether the interaction effect of children's perceived conflict frequency and parental conflict resolution type will make a difference in interpersonal problem-solving skills. Participants were 106 kindergarten children aged 5-6 years and their mothers. “Perception of Marital Conflict Cards” and “Preschool Interpersonal Problem-Solving Test” were administered to children and “O’Leary Porter Marital Conflict Scale” was applied to mothers. To examine children's perception of marital conflict, the answers to the Perception of Marital Conflict Cards were analyzed by content analysis. The findings supported the Cognitive-Contextual Theory for the 5-6 aged. Regression analysis results showed that children's perceived frequency of conflict, feelings of sadness, and perceived type of parental conflict resolution significantly predicted interpersonal problem-solving skills. Two-way ANOVA was used to examine whether the interaction effect of children's perceived frequency and parental conflict resolution type differentiated interpersonal problem-solving skills. No significant difference was found in the interaction effect. However, the main effect of children’s understanding of parental conflict resolution type is found to make a significant difference in children’s problem-solving skills. The findings revealed that marital conflict is more than a problem within the family and its importance in children's peer relationships.
Colorectal cancer (CRC) continues to be a significant global health burden, prompting the need for more effective and targeted therapeutic strategies. Nanoparticle-based drug delivery systems have emerged as a promising approach to address the limitations of conventional chemotherapy, offering enhanced specificity, reduced systemic toxicity, and improved therapeutic outcomes. This paper provides an in-depth review of the current advancements in the application of nanoparticles as vehicles for targeted drug delivery in CRC therapy. It covers a variety of nanoparticle types, including liposomes, polymeric nanoparticles, dendrimers, and mesoporous silica nanoparticles (MSNs), with a focus on their design, functionalization, and mechanisms of action. This review also examines the challenges associated with the clinical translation of these technologies and explores future directions, emphasizing the potential of nanoparticle-based systems to revolutionize CRC treatment.
Generating ligand molecules for specific protein targets, known as structure-based drug design, is a fundamental problem in therapeutics development and biological discovery. Recently, target-aware generative models, especially diffusion models, have shown great promise in modeling protein-ligand interactions and generating candidate drugs. However, existing models primarily focus on learning the chemical distribution of all drug candidates, which lacks effective steerability on the chemical quality of model generations. In this paper, we propose a novel and general alignment framework to align pretrained target diffusion models with preferred functional properties, named AliDiff. AliDiff shifts the target-conditioned chemical distribution towards regions with higher binding affinity and structural rationality, specified by user-defined reward functions, via the preference optimization approach. To avoid the overfitting problem in common preference optimization objectives, we further develop an improved Exact Energy Preference Optimization method to yield an exact and efficient alignment of the diffusion models, and provide the closed-form expression for the converged distribution. Empirical studies on the CrossDocked2020 benchmark show that AliDiff can generate molecules with state-of-the-art binding energies with up to -7.07 Avg. Vina Score, while maintaining strong molecular properties. Code is available at https://github.com/MinkaiXu/AliDiff.
Sunawan Sunawan, Anwar Sutoyo, Imam Setyo Nugroho
et al.
Honesty is a significant issue being investigated in the academic world due to the prevalence of dishonesty such as cheating and plagiarism among students. This research aims to examine the relationship between students' honesty and their moral disengagement and incivility perspectives. A correlational study was conducted with 636 students from two junior high schools in Central Java using cluster sampling. Participants completed the academic integrity scale, moral disengagement scale, and incivility scale. The results indicated that moral disengagement and incivility significantly predict students' honesty, as confirmed by the significant correlation (R = .41, F (13,622) = 9.57, p < .01). The study's results suggest that factors such as euphemistic labeling, dehumanization, unintentional incivility, and intentional incivility contribute to students' honesty. The findings of this study highlight the importance of addressing moral disengagement and incivility in educational settings. To promote honesty and positive behavior among students, educational institutions may consider implementing programs that address these factors and encourage positive moral reasoning and respectful behavior. Further discussion of these results is provided in the study.
Sonodynamic therapy (SDT) has received a lot of interest due to its deep tissue penetration and lack of invasiveness. However, SDT still prioritizes the creation of highly effective, multifunctional, and biocompatible sonosensitizers to improve the therapeutic efficiency. In this study, sodium niobate (NaNbO3) nanosonosensitizers are rationed synthesized for SDT for the first time. NaNbO3 nanosonosensitizers with semiconductor characteristics are proved to generate large amounts of reactive oxygen species and induce cell apoptosis under ultrasound irradiation. In vitro anti-tumor theranostic results confirm the mitochondrial dysfunction-dependent death pathway. In vivo tumor xenograft evaluation demonstrates that NaNbO3 will massively induce cytotoxicity and tumor eradication under ultrasound irradiation. These results provide the paradigm of the utilization of novel nanosonosensitizers as a therapeutic nanoplatform in treating breast cancer cells.
Tendon and ligament injuries are debilitating conditions across species. Poor regenerative capacities of these tissues limit restoration of original functions. The first study of this dissertation evaluated the effect of cellular administration on tendon/ligament injuries in horses using meta-analysis. The findings led to the second study that engineered implantable de novo tendon neotissue using equine adipose-derived multipotent stromal cells and collagen type I. The neotendon was evaluated for its biocompatibility and therapeutic potential in the third study using immunocompetent and immunocompromised rat bilateral calcaneal tendon elongation model. The fourth study investigated the therapeutic effects of neotendon in surgically-induced non-terminal equine accessory ligament of deep digital flexor tendon injury model.
We explored how five individuals with borderline personality disorder (BPD) perceived their self-concept over the 12 months after attending a psychoeducational intervention at a community mental health care centre. In this mixed-methods process–outcome study, subjective experiences of meaningful development gathered via an in-depth interview were explored using content analysis. Symptom change was assessed by the Borderline Personality Disorder Severity Index interview. A total of 221 utterances related to the processing of self-concept and identity were identified. Content analysis yielded five core categories pertaining to self-concept and identity: 1) from extremely negative and fluctuating self-concept to improved self-worth and stability; 2) self as actor: sense of agency; 3) decreased disconnection from and integration into self of emotions and emotional needs; 4) the importance of understanding the origins of the negative self-concept; and 5) challenges to the processing of self-concept and identity. Identity development was hampered by insufficient self-compassion and perception of the diagnosis as an additional stigma. The data highlight the importance in treatment of achieving change in punitive internalizations and judgmental self-talk. The findings also suggest the value of facilitating a sense of agency and contact with emotional experiences.