Haydn Thomas Jones, Natalie Maus, Josh Magnus Ludan
et al.
AI-driven discovery can greatly reduce design time and enhance new therapeutics' effectiveness. Models using simulators explore broad design spaces but risk violating implicit constraints due to a lack of experimental priors. For example, in a new analysis we performed on a diverse set of models on the GuacaMol benchmark using supervised classifiers, over 60\% of molecules proposed had high probability of being mutagenic. In this work, we introduce Medex, a dataset of priors for design problems extracted from literature describing compounds used in lab settings. It is constructed with LLM pipelines for discovering therapeutic entities in relevant paragraphs and summarizing information in concise fair-use facts. Medex consists of 32.3 million pairs of natural language facts, and appropriate entity representations (i.e. SMILES or refseq IDs). To demonstrate the potential of the data, we train LLM, CLIP, and LLava architectures to reason jointly about text and design targets and evaluate on tasks from the Therapeutic Data Commons (TDC). Medex is highly effective for creating models with strong priors: in supervised prediction problems that use our data as pretraining, our best models with 15M learnable parameters outperform larger 2B TxGemma on both regression and classification TDC tasks, and perform comparably to 9B models on average. Models built with Medex can be used as constraints while optimizing for novel molecules in GuacaMol, resulting in proposals that are safer and nearly as effective. We release our dataset at https://huggingface.co/datasets/medexanon/Medex, and will provide expanded versions as available literature grows.
Niels Bassler, Giuseppe Schettino, Hugo Palmans
et al.
Spatially Fractionated Radiation Therapy (SFRT) produces highly heterogeneous dose distributions, for which conventional metrics such as peak, valley, or average dose can yield ambiguous or inconsistent estimates of therapeutic window changes. These dose descriptors are not uniquely linked to biological outcome, complicating comparisons between modalities. We introduce the Biologically Effective Particle Number Ratio (BPNR), a model-independent and outcome-based framework that quantifies therapeutic window changes using directly measurable delivery quantities rather than spatial dose metrics. BPNR is defined as the ratio of total particle numbers (proportional to monitor units, MU) required to reach specified levels of tumor control probability (TCP) and normal tissue complication probability (NTCP). The relative BPNR (RBPNR) compares therapeutic windows across modalities and avoids ambiguities of spatial dose averaging by relying solely on biological endpoints. As an illustration, we apply the framework to preclinical proton minibeam radiotherapy (pMBRT) versus conventional proton therapy. The absence of normal tissue complications at the highest tested MU in the pMBRT arm yields an RBPNR > 1.4, indicating a widened therapeutic window. The BPNR/RBPNR methodology provides a concise, experimentally grounded, and modality-agnostic way to quantify therapeutic window changes, with potential applications across SFRT, FLASH, high-LET radiation, and combination therapies.
Diffusion models have emerged as a leading framework in generative modeling, poised to transform the traditionally slow and costly process of drug discovery. This review provides a systematic comparison of their application in designing two principal therapeutic modalities: small molecules and therapeutic peptides. We dissect how the unified framework of iterative denoising is adapted to the distinct molecular representations, chemical spaces, and design objectives of each modality. For small molecules, these models excel at structure-based design, generating novel, pocket-fitting ligands with desired physicochemical properties, yet face the critical hurdle of ensuring chemical synthesizability. Conversely, for therapeutic peptides, the focus shifts to generating functional sequences and designing de novo structures, where the primary challenges are achieving biological stability against proteolysis, ensuring proper folding, and minimizing immunogenicity. Despite these distinct challenges, both domains face shared hurdles: the scarcity of high-quality experimental data, the reliance on inaccurate scoring functions for validation, and the crucial need for experimental validation. We conclude that the full potential of diffusion models will be unlocked by bridging these modality-specific gaps and integrating them into automated, closed-loop Design-Build-Test-Learn (DBTL) platforms, thereby shifting the paradigm from mere chemical exploration to the on-demand engineering of novel~therapeutics.
Large language models (LLMs) hold significant potential for mental health support, capable of generating empathetic responses and simulating therapeutic conversations. However, existing LLM-based approaches often lack the clinical grounding necessary for real-world psychological counseling, particularly in explicit diagnostic reasoning aligned with standards like the DSM/ICD and incorporating diverse therapeutic modalities beyond basic empathy or single strategies. To address these critical limitations, we propose PsyLLM, the first large language model designed to systematically integrate both diagnostic and therapeutic reasoning for mental health counseling. To develop PsyLLM, we design a novel automated data synthesis pipeline that processes real-world mental health posts collected from Reddit, where users frequently share psychological distress and seek community support. This pipeline processes real-world mental health posts, generates multi-turn dialogue structures, and leverages LLMs guided by international diagnostic standards (e.g., DSM/ICD) and multiple therapeutic frameworks (e.g., CBT, ACT, psychodynamic) to simulate detailed clinical reasoning processes. Rigorous multi-dimensional filtering ensures the generation of high-quality, clinically aligned dialogue data. In addition, we introduce a new benchmark and evaluation protocol, assessing counseling quality across four key dimensions. Our experiments demonstrate that PsyLLM significantly outperforms state-of-the-art baseline models on this benchmark. The model weights and dataset have been publicly released at https://github.com/Emo-gml/PsyLLM.
Liza Fidiawati, Safriatullah Safriatullah, Cut Fazlil Hanum
et al.
Child sexual abuse is a serious problem that has a significant impact on child development and protection, making teachers' attitudes as protectors in the school environment crucial. However, research examining the psychological factors that shape teachers’ attitudes toward child sexual abuse, particularly in the context of developing countries, remains limited and fragmented. This study aims to analyse the influence of teachers' attitudes toward child sexual abuse, specifically examining the role of anxiety, trust, and commitment in shaping these attitudes. The method used was a quantitative survey design. The research sample consisted of 307 kindergarten teachers in Banda Aceh City, Indonesia, randomly selected and stratified by length of work experience. The instrument used was the Teachers' Attitudes Toward Reporting Child Sexual Abuse, which consists of 21 items with a five-point Likert scale, divided into three main factors: anxiety, trust, and commitment. Data were analysed using multiple regression to measure the influence of independent variables on teachers' attitudes as the dependent variable. The results showed that teachers' commitment and trust significantly strengthened proactive attitudes in preventing and addressing child sexual abuse, while anxiety had a negative effect.
Laura Álvarez-Cienfuegos Cercas, María Villasante Holgado
El fenómeno de negación del embarazo genera mucha incomprensión en las mujeres que lo sufren y en los sanitarios que se encuentran estos casos en la práctica clínica. Presentamos un caso clínico que no presenta factores clásicos como antecedentes traumáticos o problemas de salud mental, lo que respalda los estudios más recientes sobre este tema. Reflexionamos acerca de dos circunstancias especiales presentes en el caso: la particular relación con su percepción del cuerpo y los antecedentes de negación del embarazo a nivel familiar. Los estudios han demostrado cómo el descubrimiento del embarazo tardío, junto con el encuentro con un niño hasta entonces ignorado, produce un fuerte shock en las madres. Por ello, es importante que los sanitarios detecten este fenómeno , así como la intervención psicoterapéutica especializada con las mujeres y su familia, para garantizar la adecuada vinculación madre - bebé así como su adaptación al nuevo rol de madre.
Robust therapeutic relationships between counselors and clients are fundamental to counseling effectiveness. The assessment of therapeutic alliance is well-established in traditional face-to-face therapy but may not directly translate to text-based settings. With millions of individuals seeking support through online text-based counseling, understanding the relationship in such contexts is crucial. In this paper, we present an automatic approach using large language models (LLMs) to understand the development of therapeutic alliance in text-based counseling. We adapt a theoretically grounded framework specifically to the context of online text-based counseling and develop comprehensive guidelines for characterizing the alliance. We collect a comprehensive counseling dataset and conduct multiple expert evaluations on a subset based on this framework. Our LLM-based approach, combined with guidelines and simultaneous extraction of supportive evidence underlying its predictions, demonstrates effectiveness in identifying the therapeutic alliance. Through further LLM-based evaluations on additional conversations, our findings underscore the challenges counselors face in cultivating strong online relationships with clients. Furthermore, we demonstrate the potential of LLM-based feedback mechanisms to enhance counselors' ability to build relationships, supported by a small-scale proof-of-concept.
Pavan K. Inguva, Saikat Mukherjee, Pierre J. Walker
et al.
Nucleic acids such as mRNA have emerged as a promising therapeutic modality with the capability of addressing a wide range of diseases. Lipid nanoparticles (LNPs) as a delivery platform for nucleic acids were used in the COVID-19 vaccines and have received much attention. While modern manufacturing processes which involve rapidly mixing an organic stream containing the lipids with an aqueous stream containing the nucleic acids are conceptually straightforward, detailed understanding of LNP formation and structure is still limited and scale-up can be challenging. Mathematical and computational methods are a promising avenue for deepening scientific understanding of the LNP formation process and facilitating improved process development and control. This article describes strategies for the mechanistic modeling of LNP formation, starting with strategies to estimate and predict important physicochemical properties of the various species such as diffusivities and solubilities. Subsequently, a framework is outlined for constructing mechanistic models of reactor- and particle-scale processes. Insights gained from the various models are mapped back to product quality attributes and process insights. Lastly, the use of the models to guide development of advanced process control and optimization strategies is discussed.
The International Affective Picture System (IAPS), widely utilized in emotion research, is based on a dimensional approach. It includes 1196 colored static images depicting different semantic categories, with valence, arousal, and dominance ratings determined for each. However, the specific discrete emotions evoked by these images within the IAPS has remained unclear. There-fore, numerous investigations have been conducted in different cultural contexts to address this matter. This study aimed to determine a subset of images from the IAPS that elicit discrete emo-tions. To achieve this goal, an image was selected for each semantic category within the IAPS, employing specific criteria, and the elicited discrete emotions were subsequently examined in a Turkish sample. Additionally, valence ratings for these images were obtained within the Turkish culture, facilitating cross-cultural comparisons. Sixty Turkish students (43 female) aged between 19-25 participated in this study. The participants rated the valence, discrete emotion category, and the intensity of the determined discrete emotion (1 = none; 9 = very strongly) for 231 select-ed images from the IAPS, respectively. Valence ratings were obtained using the paper-pencil version of the Self-Assessment Manikin. Accordingly, 76 images, of which intensity ratings above six were classified into a single discrete emotion with the agreement of 70% and above among the participants. Furthermore, the obtained data were compared with the results of studies conducted in different cultural settings to explore potential cultural differences. Overall, the results highlighted the importance of selecting culture-specific stimuli in emotion studies.
RUKHSAR SARDAR, SHAKIR ULLAH, SYED ZAIN UL ABIDIN
et al.
BACKGROUND:
Balance issues, which are more prevalent in older persons than in younger people, are one of the obstacles that come with ageing. Falls are a major public health concern because they are a primary cause of death and functional disability in older people. Dual-task training (DTT) have been showed that have satisfying effects on older adults.
OBJECTIVES:
To find the effect of dual task training on balance limitations in geriatric population.
METHODOLOGY:
A total of 66 patients were selected based on the inclusion criteria of both male and female participants, age more than 65 years, past one year fall history and no neurological problem. 61 out of 66 completed the research, with 5 people dropping out of the study. Out of these 61 patients (28 were males and 33 were females with a mean age of 69 years±4.073). The Timed Up and Go Test (TUGT) and the Berg Balance Scale (BBS) scores were checked pre and post exercise application for any improvement in all the 61 patients. Data of 61 participants after a period of 4 weeks was obtained. The data was analysed using SPSS version 22. Wilcoxon signed ranks test was applied to compare baseline TUGT and BBS with follow up score.
RESULTS:
The mean pre TUGT was 16.98 seconds and mean post TUGT was 15.77 seconds and the mean pre BBS was 41 and post BBS was 50 at 95% confidence interval. The results were clinically not significant because the effect size was very small to be noticed, but were statistically significant as was shown p < 0.05.
CONCLUSION:
After comparison of the results of this study with other research studies, it was concluded that though the results were statistically significant, they lacked clinical significance.
KEYWORDS:
Core Stability, Geriatrics, Postural balance, Treatment Effectiveness
Vocational rehabilitation. Employment of people with disabilities, Therapeutics. Psychotherapy
The use of nanoparticles for targeted drug delivery, especially in diseased arteries, is a novel procedure. We are incorporating nanoparticles into blood vessels using a catheter, which could potentially deliver drugs precisely to affected areas, reducing side effects and increasing treatment efficiency. Considering non-Newtonian fluid modeling because blood is a complex fluid with non-linear behavior. In this paper, we are using mathematical modeling to understand blood flow dynamics, temperature, and concentration dispersion, which can provide valuable insights into the behavior of therapeutic nanoparticles in the bloodstream. The perturbation method is used to solve the complex mathematical model with permeable flow boundary conditions. We are investigating flow field characteristics including wall shear stress, pressure, and impedance to understand how nanoparticles disperse and interact with different physiological aspects. In conclusion, the proposed study focuses on the use of nanotechnology and mathematical modeling to understand the effects of therapeutic nanoparticles in diseased arteries, which is an important and valuable contribution to the medical field.
Laura Montalbano, Agnese Augello, Giovanni Pilato
et al.
In chronic diseases, obtaining a correct diagnosis and providing the most appropriate treatments often is not enough to guarantee an improvement of the clinical condition of a patient. Poor adherence to medical prescriptions constitutes one of the main causes preventing achievement of therapeutic goals. This is generally true especially for certain diseases and specific target patients, such as children. An engaging and entertaining technology can be exploited in support of clinical practices to achieve better health outcomes. Our assumption is that a gamified session with a humanoid robot, compared to the usual methodologies for therapeutic education, can be more incisive in learning the correct inhalation procedure in children affected by asthma. In this perspective, we describe an interactive module implemented on the Pepper robotic platform and the setting of a study that was planned in 2020 to be held at the Pneumoallergology Pediatric clinic of CNR in Palermo. The study was canceled due to the COVID-19 pandemic. Our long-term goal is to assess, by means of a qualitative-quantitative survey plan, the impact of such an educational action, evaluating possible improvement in the adherence to the treatment.
Menggunakan dzikir sebagai mediasi atau terapi merupakan suatu hal yang sangat berpengaruh pada ibu hamil. Dzikir dianggap dapat membantu individu dalam membentuk keyakinan kepada Allah SWT. Dzikir dapat disamakan dengan terapi relaksasi atau mediasi dimana menekankan usaha untuk beristirahat dan bersantai dengan mengurangi ketegangan atau tekanan psikologis. Faktor yang berperan dalam kecemasan ibu hamil salah satunya adalah kemampuan mengendalikan emosi. Pada penelitian kali ini peneliti menggunakan metode kualitatif deskriptif melalui observasi dan wawancara. Hasil penelitian dari wawancara dan observasi menunjukkan bahwa dengan terapi dzikir dapat membantu mengurangi kecemasan yang dialami ibu hamil.
Pakistan is ranked 5th in terms of population with 232 million people, while 338 million people are predicted by 20501. As one of the country’s major challenges, health care is managed on two levels: the provincial level and the federal level. Health care budgets are allocated 19.5 billion in fiscal year 2022-23, about 3% of the Gross Domestic Product (GDP), but less than the amount recommended by the World Health Organization (WHO)2.
As a key component of healthcare, physical rehabilitation includes habilitating children born with different abilities, restoring normal physical function after stroke, regaining normal physical function after musculoskeletal injuries, cardiorespiratory, sports, geriatric, speech, gynecological, and achieving physical independence through prosthetic and orthotic devices 3. The benefits of regular physical activity and physical fitness are also associated with decreased chronic disease risk, preventing tenderness, promoting longevity and enhancing the quality of life 4.
According to statistics, there were 27-34 million people with different abilities in Pakistan in 2014, while globally 15% of the total population had disability 5. In terms of stroke prevalence per capita, Pakistan has the highest rate. The number of stroke cases reported in Pakistan each day is approximately 1000, resulting in 400 deaths daily, while the remaining 600 cases are managed through medical management and rehabilitation 6. The prevalence of cerebral palsy in Pakistan is approximately double (i.e., 4 children per 1000) than globally (2-3 children per 1000).
In physical rehabilitation, research is vital to reporting key statistics about prevalence and incidence, identifying risk factors, adapting and developing diagnostic tools, testing for reliability and validity, and assessing whether existing treatment techniques are effective, as well as developing new protocols, guidelines, and treatment techniques.
Research mentorship and supervision are key challenges in rehabilitation research in Pakistan, even though the first rehabilitation institution was established in the 1950s 7. Research in rehabilitation has expanded in the last two decades with the launch of MS/M.Phil and Ph.D. programs in universities. In various universities, faculty and MS/M.Phil and Ph.D. level students started doing research and publishing papers, but there was a lack in research mentorship/supervision. In the beginning, foreign faculty were engaged in some universities. However, today some supervisors with Ph.D. qualifications are available and supervise projects of Ph.D. level students.
Researchers in the rehabilitation sciences face another challenge when it comes to accessing literature. There are a majority of quality research journals that are not open access to students and institutions, and they require paid subscriptions. Since these journals charge subscription fees and publication charges, they restrict access to quality literature, publication of reports, and publication of research 8.
Providing funding for research projects in rehabilitation sciences professions has always been a challenge. The total budget for the financial year 2022-23 was 9,000 billion PKR, and there was only 44 billion PKR allocated to higher education. In total, only 04% of the budget is allocated for higher education 2. The higher education commission of Pakistan chartered 247 universities and degree-awarding institutions. There are approximately 58,000 university teachers and 2 million university students. HEC funds research projects through the National Research Program for Universities (NRPU), Technology Development Fund (TDF), Local Challenge Fund (LCF), Grand Challenge Fund (GCF), Rapid Technology Transfer Grant (RTTG), Technology Transfer Support Fund (TTSF), and Innovative and Collaborative Research Grant (ICRG) 9.
Lack of latest equipment, research laboratories, and specialized research centers in the country especially in the universities and degree awarding or higher education affiliated institutions. There is a lack of the latest and modern diagnostic equipment, tools, and therapeutic equipment, as technology evolving very fast in the last two decades, and very sophisticated equipment is now been available in the portal form.
The way forward is to focus on research-related specialized faculty training and support to enroll faculty members in research-based MS/M. Phil and Ph.D. level degrees in physical rehabilitation, especially in top-ranked universities in technologically developed countries. HEC and universities must facilitate the faculty member, MS/M.Phil and Ph.D. level students for access to research databases and high-impact factor research journals. The state must increase the annual budget for higher education as per the recommendation of the WHO and plan to develop well-equipped research laboratories and research centers in the universities and affiliated teaching hospitals.
REFERENCES
• Info W. Population of Pakistan (2020 and historical). https://www.worldometers.info/world-population/pakistan-population/ (accessed 3/10/2023 2023).
• Finance Division GoP. Federal Budget Publications 2022-23. 2022. https://www.finance.gov.pk/fb_2022_23.html (accessed 3/10/2023 2023).
• Jesus TS, Papadimitriou C, Bright FA, Kayes NM, Pinho CS, Cott CA. Person-centered rehabilitation model: framing the concept and practice of person-centered adult physical rehabilitation based on a scoping review and thematic analysis of the literature. Archives of Physical Medicine and Rehabilitation 2022; 103(1): 106-20.
• Anderson E, Durstine JL. Physical activity, exercise, and chronic diseases: A brief review. Sports Medicine and Health Science 2019; 1(1): 3-10.
• JAMIL K. World Health Organization Global Disability Action Plan 2014–2021: challenges and perspectives for physical medicine and rehabilitation in Pakistan. J Rehabil Med 2017; 49: 10-21.
• Hashmi M, Khan M, Wasay M. Growing burden of stroke in Pakistan: a review of progress and limitations. International journal of stroke 2013; 8(7): 575-81.
• Malik AN, Anwar S, Siddiqi FA. Research Barriers & facilitators of Physical Therapists of Pakistan. Rawal Medical Journal 2016; 41(3): 369-72.
• Batool U. REHABILITATION RESEARCH BARRIERS IN PAKISTAN: soi: 21-2017/re-trjvol02iss02p58. The Rehabilitation Journal 2018; 2(02): 58-9.
• Higher Education Comission P. RESEARCH GRANT PROGRAMS. 2022. https://www.hec.gov.pk/english/services/RnD/Pages/Research-Grants.aspx (accessed 3/10/2023 2023).
Vocational rehabilitation. Employment of people with disabilities, Therapeutics. Psychotherapy
Elena Righetti, Alice Antonello, Luca Marchetti
et al.
Parkinson's disease (PD) is the second most common neurodegenerative disorder worldwide, yet there is no disease-modifying therapy up to this date. The biological complexity underlying PD hampers the investigation of the principal contributors to its pathogenesis. In this context, mechanistic models grounded in molecular-level knowledge provide virtual labs to uncover the primary events triggering PD onset and progression and suggest promising therapeutic targets. Multiple modeling efforts in PD research have focused on the pathological role of $α$-synuclein ($α$syn), a presynaptic protein that emerges from the intricate molecular network as a crucial driver of neurodegeneration. Here, we collect the advances in mathematical modeling of $α$syn homeostasis, focusing on aggregation and degradation pathways, and discussing potential modeling improvements and possible implications in PD therapeutic strategy design.
Di Indonesia kita, sikap intoleransi sudah menyusup ke masyarakat yang dibuktikan oleh tindakan negatif antara satu dengan yang lainnya mulai dari caci maki, permusuhan, kekerasan hingga pengerusakan tempat ibadah dengan menggunakan agama sebagai kambing hitam atas tindakannya. Maka dari itu kekuatan ikhlas berperan penting dalam menghadapi problematika intoleransi tersebut. Secara deskriptif penulis mendalami kekuatan ikhlas yang dikemukakan Komarudin Hidayat dalam membangun sikap yang toleransi, sehingga diharapkan nantinya Ikhlas menjadi salah satu jalan spiritualitas manusia yang dapat meredam, mencegah dan merubah konflik intoleransi di dalam masyarakat Indonesia khususnya dan dunia pada umumnya menjadi penuh cinta dan kasih sayang.
Domain-aware machine learning (ML) models have been increasingly adopted for accelerating small molecule therapeutic design in the recent years. These models have been enabled by significant advancement in state-of-the-art artificial intelligence (AI) and computing infrastructures. Several ML architectures are pre-dominantly and independently used either for predicting the properties of small molecules, or for generating lead therapeutic candidates. Synergetically using these individual components along with robust representation and data generation techniques autonomously in closed loops holds enormous promise for accelerated drug design which is a time consuming and expensive task otherwise. In this perspective, we present the most recent breakthrough achieved by each of the components, and how such autonomous AI and ML workflow can be realized to radically accelerate the hit identification and lead optimization. Taken together, this could significantly shorten the timeline for end-to-end antiviral discovery and optimization times to weeks upon the arrival of a novel zoonotic transmission event. Our perspective serves as a guide for researchers to practice autonomous molecular design in therapeutic discovery.