Sci Rep. 2025 Jan 9;15(1):1455. doi: 10.1038/s41598-025-85681-0.

ABSTRACT

Drug-induced eosinophilic pneumonia (EP) is an uncommon adverse drug reaction. Many drugs have been reported to cause EP, the evidence mainly being in the form of case reports/case series. This study aims to conduct an exploratory analysis of the United States Food and Drug Administration adverse event reporting system (FAERS) database to identify previously unknown drugs that can cause EP and supplement the available evidence for known culprit drugs. A retrospective case-noncase study was conducted using individual case safety reports (ICSRs) reported to the US FAERS from the first quarter of 2004 to the second quarter of 2024. Cases of potentially drug-induced EP were identified using OpenVigil application by conducting a narrow and broad scope search using the Medical Dictionary of Regulatory Activities preferred terms. A base list of drugs described in select literature to have caused EP was used to categorize known and unknown drugs. A disproportionality analysis was performed, with a reporting odds ratio > 2, lower end of the 95% confidence interval > 1, and a minimum of 3 reported cases considered a signal of disproportionate reporting (SDR). During the study period, 8,702,548 individual case safety reports (ICSRs) were submitted to the FAERS. Of these, 855 ICSRs using the narrow scope search and 1411 ICSRs using the broad scope search reported EP. The three most commonly reported drugs with an SDR for EP using the narrow scope search were daptomycin, naltrexone, and prednisone. The most common indications for the use of the drugs were infections, immunological conditions, asthma, and central nervous system disorders. In total, there were 45 drugs with an SDR but no supporting literature evidence available. The number of drugs implicated in causing EP has increased over the years. Several antimicrobial agents, followed by drugs affecting the central nervous system and anticancer drugs, including monoclonal antibodies, can produce EP. The list of suspected drugs identified in this study, especially those with SDR and literature evidence, should be strongly considered as a possible cause in patients presenting with pneumonia not explained otherwise.

PMID:39789288 | DOI:10.1038/s41598-025-85681-0

Eur J Hosp Pharm. 2025 Jan 9:ejhpharm-2024-004353. doi: 10.1136/ejhpharm-2024-004353. Online ahead of print.

ABSTRACT

BACKGROUND: Tachyphylaxis is the rapid development of drug tolerance following repeated administration.

OBJECTIVES: To evaluate the United States Food and Drug Administration Adverse Event Reporting System (USFDA AERS) data for drugs significantly associated with tachyphylaxis using disproportionality analysis.

METHODS: Disproportionality analysis was used for detecting safety signals for identifying drugs associated with tachyphylaxis. Frequentist and Bayesian statistical methods were employed to detect signals, identifying anesthetics, immunosuppressants, antineoplastics, and psychoactive drugs with positive associations.

RESULTS: Data from 29,153,222 reports between 2004 and 2024 were examined, and 242 reports of tachyphylaxis included. Tachyphylaxis was observed with corticosteroids, opioids, antihistamines, psycholeptics, nitroglycerin, antineoplastics, immunosuppressants, sympathomimetics, psychoanaleptics and psycholeptics that are well documented. Tachyphylaxis was also observed with propofol, cisatracurium, oxcarbazepine, and cabergoline emphasizing the need for further investigation. Hospitalization was reported in 16.9% of cases, with 5% leading to disability and 2.5% resulting in death.

CONCLUSION: While this study provides valuable insights into drug-related tachyphylaxis, limitations such as underreporting and lack of detailed clinical context exist. Future research should focus on understanding underlying mechanisms and developing strategies to mitigate tachyphylaxis in long-term treatments.

PMID:39788698 | DOI:10.1136/ejhpharm-2024-004353

Lancet Oncol. 2025 Jan 6:S1470-2045(24)00636-3. doi: 10.1016/S1470-2045(24)00636-3. Online ahead of print.

ABSTRACT

BACKGROUND: CMG901 is a novel first-in-class antibody-drug conjugate with a humanised anticlaudin 18.2 antibody linked to microtubule-disrupting agent monomethyl auristatin E. We aimed to assess the antitumour activity and safety of CMG901 in patients with advanced gastric or gastro-oesophageal junction cancer and other solid tumours.

METHODS: KYM901 is a multicentre, open-label, single-arm, phase 1 trial consisting of dose-escalation and dose-expansion stages. Patients with advanced solid tumours, including gastric or gastro-oesophageal junction and pancreatic cancers, were recruited from 31 hospital sites in China. Eligible patients were aged 18 years or older, were refractory to standard therapy or had no available standard-of-care regimen, and had an Eastern Cooperative Oncology Group performance status score of 0-1, a life expectancy of at least 3 months, and at least one measurable lesion. Patients received intravenous CMG901 every 3 weeks (0·3-3·4 mg/kg in dose escalation and 2·2-3·0 mg/kg in dose expansion) until disease progression, unacceptable toxic effects, initiation of new antitumour therapy, study withdrawal, or death. Primary endpoints were adverse events and dose-limiting toxic effects in the dose-escalation phase, and objective response rate and recommended phase 2 dose in the dose-expansion phase. Confirmed objective response was defined as a partial or complete response that was verified by follow-up imaging at least 4 weeks after the initial assessment. Safety was assessed in all patients who received at least one dose of CMG901 with at least one post-dose safety evaluation. Antitumour activity was assessed in all patients who received at least one dose of CMG901 (full analysis set) and in all CMG901-treated patients with at least one post-dose imaging evaluation and no major protocol deviations (efficacy analysis set). Dose-expansion data for patients with pancreatic cancer will be published separately. Due to small sample sizes, results in patients with other solid tumours (n=2) are not planned for publication. This ongoing trial is registered with ClinicalTrials.gov, NCT04805307.

FINDINGS: Between Dec 24, 2020, and Feb 23, 2023, 27 patients were enrolled in the dose-escalation phase (median age 57·0 years [IQR 48·0-63·0]; 14 [52%] male, 13 [48%] female) and 107 patients with gastric or gastro-oesophageal junction cancer in the dose-expansion phase (median age 56·0 years [44·0-64·0]; 57 [53%] male, 50 [47%] female). As of Feb 24, 2024, one dose-limiting toxic effect (grade 3 pancreatitis) occurred at 2·2 mg/kg, and the maximum tolerated dose was not reached in the dose-escalation phase. All 27 patients reported at least one treatment-emergent adverse event, most frequently vomiting (19 [70%]), decreased appetite (16 [59%]), proteinuria (16 [59%]), and anaemia (15 [56%]), and five (19%) had drug-related grade 3 or worse treatment-emergent adverse events. In 107 patients, grade 3 or worse treatment-emergent adverse events occurred in 73 (68%) patients and serious adverse events occurred in 54 (50%) patients in dose expansion. The most common grade 3-4 adverse events were neutrophil count decreased (22 [21%]), anaemia (15 [14%]), and vomiting (11 [10%]). One treatment-related death was reported. At median follow-up of 9·0 months (IQR 4·4-12·9), among 113 patients with gastric or gastro-oesophageal junction cancer in the 2·2-3·0 mg/kg cohort full analysis set across both the dose-escalation and dose-expansion phases, the confirmed objective response rate was 28% (95% CI 20-38; 32 of 113 patients). In the 109 patients included in the efficacy analysis set, the confirmed objective response rate was 29% (95% CI 21-39; 32 of 109 patients). Based on overall safety, activity, and pharmacokinetics of CMG901, 2·2 mg/kg was the proposed recommended phase 2 dose.

INTERPRETATION: CMG901 showed a manageable safety profile and had promising antitumour activity in patients with advanced gastric or gastro-oesophageal junction cancer.

FUNDING: KYM Biosciences.

PMID:39788133 | DOI:10.1016/S1470-2045(24)00636-3

Virology. 2025 Jan 3;603:110391. doi: 10.1016/j.virol.2025.110391. Online ahead of print.

ABSTRACT

The integration of nanotechnology into antiretroviral drug delivery systems presents a promising avenue to address challenges posed by long-term antiretroviral therapies (ARTs), including poor bioavailability, drug-induced toxicity, and resistance. These limitations impact the therapeutic effectiveness and quality of life for individuals living with HIV. Nanodrug delivery systems, particularly nanoemulsions, have demonstrated potential in improving drug solubility, enhancing bioavailability, and minimizing systemic toxicity. Moreover, nanodrug platforms can target viral reservoirs, potentially reducing the emergence of drug-resistant strains-a significant challenge in anti-HIV treatment. This study evaluates the biological efficacy of a rosemary oil-based nanoemulsion loaded with Nelfinavir (NFV) and Epigallocatechin Gallate (EGCG), which demonstrated HIV-1 suppression at sub-CC₅₀ concentrations across two distinct cellular systems. The synergistic interaction between NFV and EGCG was confirmed through cellular assays, enzymatic studies, and molecular interaction analysis. In vitro experiments revealed that the NE-NFV-EGCG nanoemulsion exhibited enhanced HIV-1 inhibitory activity compared to pure NFV, highlighting a promising therapeutic synergy. The findings suggest that EGCG could be a valuable adjunct in NFV-based regimens for HIV management. Molecular interaction studies further confirmed the nanoemulsion's inhibitory potential against the HIV-1 protease enzyme. This study marks a significant advancement in HIV-1 treatment by documenting, for the first time, the synergistic inhibitory activity of NFV and EGCG. The novel nanoformulation offers improved oral bioavailability, minimal side effects, and enhanced therapeutic outcomes. Future studies are needed to optimize the formulation for clinical applications, including sustained drug release and drug transport mechanisms.

PMID:39787774 | DOI:10.1016/j.virol.2025.110391

Acta Pharm. 2025 Jan 9;74(4):693-708. doi: 10.2478/acph-2024-0038. Print 2024 Dec 1.

ABSTRACT

Cardiovascular diseases (CVDs) are the leading cause of mortality and morbidity globally. It is estimated that 17.9 million people died from CVDs in 2019, which represents 32 % of all deaths worldwide. Cardiovascular drugs are the most common medical intervention for the prevention of cardiovascular events. CV medications have many benefits however their application is often complicated by multimorbidity and polypharmacy. Drug-drug interactions (DDIs) can lead to adverse drug events, hospitalizations, prolonged hospital stays, increased healthcare costs, and increased risk of mortality. Hospital admission provides an opportunity for pharmacotherapy analysis and for identifying DDIs which can jeopardize medication safety. The aim of this study is to determine the type and prevalence of potential clinically significant DDIs in patients with CVD and to examine factors associated with exposure to DDIs. A prospective study was conducted at the Dubrava University Hospital at the Clinic of Cardiology during a 6-month period (September 2023 - February 2024). Demographic, clinical and pharmacotherapy data were collected for each patient. The first prescribed pharmacotherapy was analyzed. The research was approved by the Hospital's Ethics Committee and each patient involved in the study signed an informed consent. Lexicomp® Lexi-InteractTM Online (Lexi-Comp, Inc., USA) was used for DDI analysis. Poisson regression was used for regression analysis for determining risk factors associated with exposure to DDIs. Total of 151 patients admitted to Cardiology ward were included in the research, and the average age was 67 years. Patients had an average of 9 medications in their therapy and 8 diagnoses. Overall, 1268 potential clinically significant DDIs were determined, of which the most frequently determined interactions were grade C (90.9 %), then grade D (8.6 %) and grade X (0.6 %). CV medications were involved in 88 % DDIs. The most common interventions regarding identified DDIs included exclusion one of the drugs, dose adjustment, increased monitoring of signs of bleeding, cardiac disorders, hypoglycemia, CNS depression and rhabdomyolysis, blood pressure, markers of renal function and electrolyte status. Factors associated with the prevalence of potential clinically significant DDIs were decreased renal function, recent hospitalization, total number of comorbidities and polypharmacy. Specific comorbidities associated with DDIs were arrhythmia, heart failure, diabetes mellitus and disease of the respiratory system. A high prevalence of DDIs of CV medications in all categories of clinical significance was determined. Managing medication safety in specific patient groups with CVDs can represent a greater challenge regarding DDIs. Certain medical conditions, such as arrhythmia, heart failure, diabetes, and diseases of the respiratory system, multimorbidity, polypharmacy, impaired renal function and recent hospitalization are identified in this research as additional factors associated with DDIs occurrence in patients with CVDs at hospital admission. Hospital admission is one of the crucial points for managing medication safety. Clinical pharmacists should regularly analyze DDIs in prescribed pharmaco-therapy which enhances medication safety and also contributes to the quality of provided health care.

PMID:39787625 | DOI:10.2478/acph-2024-0038

PLoS One. 2025 Jan 9;20(1):e0314151. doi: 10.1371/journal.pone.0314151. eCollection 2025.

ABSTRACT

Aquaporin 1 (AQP1) is a key channel for water transport in peritoneal dialysis. Inhibition of AQP1 could therefore impair water transport during peritoneal dialysis. It is not known whether inhibition of AQP1 occurs unintentionally due to off-target interactions of administered medications. A high-throughput virtual screening study has been performed to investigate the possible binding of licensed medications to the water pore of human AQP1. A complete model of human AQP1 based on its canonical sequence was assembled using I-TASSER and MODELLER. The model was refined via the incorporation of pore water molecules from a high-resolution yeast aquaporin structure. Docking studies were conducted for the cytoplasmic domain of the AQP1 monomer against a library of all compounds listed in the British National Formulary (BNF), using the PLANTS software with the ChemPLP scoring function. The stability of the best docked conformations within the intrinsic water pore was assessed via short 15 nanosecond molecular dynamics (MD) simulations using the GROMACS-on-Colab utility. Of the 1512 compounds tested, 1002 docking results were obtained, and 198 of these conformations occupied a position within the intrinsic water pore. 30 compounds with promising docking scores were assessed by MD. The docked conformations for dopamine, gabapentin, pregabalin, and methyldopa were stable in these short MD studies. For furosemide and pravastatin, the MD trajectory suggested a binding mode different to the docking result. A small set of compounds which could impede water transport through human AQP1 have been identified in this computational screening study.

PMID:39787482 | DOI:10.1371/journal.pone.0314151

J Craniofac Surg. 2024 Oct 29. doi: 10.1097/SCS.0000000000010663. Online ahead of print.

ABSTRACT

BACKGROUND: With the use of machine learning algorithms, artificial intelligence (AI) has become a viable diagnostic and treatment tool for oral cancer. AI can assess a variety of information, including histopathology slides and intraoral pictures.

AIM: The purpose of this systematic review is to evaluate the efficacy and accuracy of AI technology in the detection and diagnosis of oral cancer between 2020 and 2024.

METHODOLOGY: With an emphasis on AI applications in oral cancer diagnostics, a thorough search approach was used to find pertinent publications published between 2020 and 2024. Using particular keywords associated with AI, oral cancer, and diagnostic imaging, databases such as PubMed, Scopus, and Web of Science were searched. Among the selection criteria were actual English-language research papers that assessed the effectiveness of AI models in diagnosing oral cancer. Three impartial reviewers extracted data, evaluated quality, and compiled the findings using a narrative synthesis technique.

RESULTS: Twelve papers that demonstrated a range of AI applications in the diagnosis of oral cancer satisfied the inclusion criteria. This study showed encouraging results in lesion identification and prognostic prediction using machine learning and deep learning algorithms to evaluate oral pictures and histopathology slides. The results demonstrated how AI-driven technologies might enhance diagnostic precision and enable early intervention in cases of oral cancer.

CONCLUSION: Unprecedented prospects to transform oral cancer diagnosis and detection are provided by artificial intelligence. More resilient AI systems in oral oncology can be achieved by joint research and innovation efforts, even in the face of constraints like data set variability and regulatory concerns.

PMID:39787481 | DOI:10.1097/SCS.0000000000010663

Neuroradiol J. 2025 Jan 9:19714009251313511. doi: 10.1177/19714009251313511. Online ahead of print.

ABSTRACT

INTRODUCTION: The prevalence of neurodegenerative diseases has significantly increased, necessitating a deeper understanding of their symptoms, diagnostic processes, and prevention strategies. Frontotemporal dementia (FTD) and Alzheimer's disease (AD) are two prominent neurodegenerative conditions that present diagnostic challenges due to overlapping symptoms. To address these challenges, experts utilize a range of imaging techniques, including magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), functional MRI (fMRI), positron emission tomography (PET), and single-photon emission computed tomography (SPECT). These techniques facilitate a detailed examination of the manifestations of these diseases. Recent research has demonstrated the potential of artificial intelligence (AI) in automating the diagnostic process, generating significant interest in this field.

MATERIALS AND METHODS: This narrative review aims to compile and analyze articles related to the AI-assisted diagnosis of FTD and AD. We reviewed 31 articles published between 2012 and 2024, with 23 focusing on machine learning techniques and 8 on deep learning techniques. The studies utilized features extracted from both single imaging modalities and multi-modal approaches, and evaluated the performance of various classification models.

RESULTS: Among the machine learning studies, Support Vector Machines (SVM) exhibited the most favorable performance in classifying FTD and AD. In deep learning studies, the ResNet convolutional neural network outperformed other networks.

CONCLUSION: This review highlights the utility of different imaging modalities as diagnostic aids in distinguishing between FTD and AD. However, it emphasizes the importance of incorporating clinical examinations and patient symptom evaluations to ensure comprehensive and accurate diagnoses.

PMID:39787363 | DOI:10.1177/19714009251313511

J Med Chem. 2025 Jan 9. doi: 10.1021/acs.jmedchem.4c02740. Online ahead of print.

ABSTRACT

Applying artificial intelligence techniques to flexibly model the binding between the ligand and protein has attracted extensive interest in recent years, but their applicability remains improved. In this study, we have developed CarsiDock-Flex, a novel two-step flexible docking paradigm that generates binding poses directly from predicted structures. CarsiDock-Flex consists of an equivariant deep learning-based model termed CarsiInduce to refine ESMFold-predicted protein pockets with the induction of specific ligands and our existing CarsiDock algorithm to redock the ligand into the induced binding pockets. Extensive evaluations demonstrate the effectiveness of CarsiInduce, which can successfully guide the transition of ESMFold-predicted pockets into their holo-like conformations for numerous cases, thus leading to the superior docking accuracy of CarsiDock-Flex even on unseen sequences. Overall, our approach offers a novel design for flexible modeling of protein-ligand binding poses, paving the way for a deeper understanding of protein-ligand interactions that account for protein flexibility.

PMID:39787296 | DOI:10.1021/acs.jmedchem.4c02740

PLoS Comput Biol. 2025 Jan 9;21(1):e1012738. doi: 10.1371/journal.pcbi.1012738. eCollection 2025 Jan.

ABSTRACT

Human mobility between different regions is a major factor in large-scale outbreaks of infectious diseases. Deep learning models incorporating infectious disease transmission dynamics for predicting the spread of multi-regional outbreaks due to human mobility have become a hot research topic. In this study, we incorporate the Graph Transformer Neural Network and graph learning mechanisms into a metapopulation SIR model to build a hybrid framework, Metapopulation Graph Transformer Neural Network (M-Graphormer), for high-dimensional parameter estimation and multi-regional epidemic prediction. The framework effectively solves the problem that existing models may lose some hidden spatial dependencies in the data when dealing with the dynamic graph structure of the network due to human mobility. We performed multi-wave infectious disease prediction in multiple regions based on real epidemic data. The results show that the framework is capable of performing high-dimensional parameter estimation and accurately predicting epidemic transmission dynamics in multiple regions even with low data quality. In addition, we retrospectively extrapolate the temporal evolution patterns of contact rate under different interventions implemented in different regions, reflecting the dynamics of intervention intensity and the need for flexibility in adjusting interventions in different regions. To provide early warning of infectious disease transmission, we retrospectively predicted the arrival time of infectious diseases using data from the early stages of outbreaks.

PMID:39787070 | DOI:10.1371/journal.pcbi.1012738

PLoS One. 2025 Jan 9;20(1):e0313776. doi: 10.1371/journal.pone.0313776. eCollection 2025.

ABSTRACT

BACKGROUND: Chat Generative Pre-trained Transformer (ChatGPT) is a 175-billion-parameter natural language processing model that uses deep learning algorithms trained on vast amounts of data to generate human-like texts such as essays. Consequently, it has introduced new challenges and threats to medical education. We assessed the use of ChatGPT and other AI tools among medical students in Uganda.

METHODS: We conducted a descriptive cross-sectional study among medical students at four public universities in Uganda from 1st November 2023 to 20th December 2023. Participants were recruited by stratified random sampling. We used a semi-structured questionnaire to collect data on participants' socio-demographics and use of AI tools such as ChatGPT. Our outcome variable was use of AI tools. Data were analyzed descriptively in Stata version 17.0. We conducted a modified Poisson regression to explore the association between use of AI tools and various exposures.

RESULTS: A total of 564 students participated. Almost all (93%) had heard about AI tools and more than two-thirds (75.7%) had ever used AI tools. Regarding the AI tools used, majority (72.2%) had ever used ChatGPT, followed by SnapChat AI (14.9%), Bing AI (11.5%), and Bard AI (6.9%). Most students use AI tools to complete assignments (55.5%), preparing for tutorials (39.9%), preparing for exams (34.8%) and research writing (24.8%). Students also reported the use of AI tools for nonacademic purposes including emotional support, recreation, and spiritual growth. Older students were 31% less likely to use AI tools compared to younger ones (Adjusted Prevalence Ratio (aPR):0.69; 95% CI: [0.62, 0.76]). Students at Makerere University were 66% more likely to use AI tools compared to students in Gulu University (aPR:1.66; 95% CI:[1.64, 1.69]).

CONCLUSION: The use of ChatGPT and other AI tools was widespread among medical students in Uganda. AI tools were used for both academic and non-academic purposes. Younger students were more likely to use AI tools compared to older students. There is a need to promote AI literacy in institutions to empower older students with essential skills for the digital age. Further, educators should assume students are using AI and adjust their way of teaching and setting exams to suit this new reality. Our research adds further evidence to existing voices calling for regulatory frameworks for AI in medical education.

PMID:39787055 | DOI:10.1371/journal.pone.0313776

Eur Heart J Acute Cardiovasc Care. 2025 Jan 9:zuaf001. doi: 10.1093/ehjacc/zuaf001. Online ahead of print.

ABSTRACT

BACKGROUND: Acute heart failure (AHF) poses significant diagnostic challenges in the emergency room (ER) because of its varied clinical presentation and limitations of traditional diagnostic methods. This study aimed to develop and evaluate a deep-learning model using electrocardiogram (ECG) data to enhance AHF identification in the ER.

METHODS: In this retrospective cohort study, we analyzed the ECG data of 19,285 patients who visited ERs of three hospitals between 2016 and 2020; 9,119 with available left ventricular ejection fraction and N-terminal prohormone of brain natriuretic peptide level data and who were diagnosed with AHF were included in the study. We extracted morphological and clinical parameters from ECG data to train and validate four machine learning models: baseline linear regression and more advanced models including XGBoost, Light GBM, and CatBoost.

RESULTS: The CatBoost algorithm outperformed other models, showing superior area under the receiver operating characteristic and area under the precision-recall curve diagnostic accuracy across both internal (0.89 ± 0.01 and 0.89 ± 0.01) and external (0.90 and 0.89) validation datasets, respectively. The model demonstrated high accuracy, precision, recall, and f1 score, indicating robust performance in AHF identification.

CONCLUSION: The developed machine learning model significantly enhanced AHF detection in the ER using conventional 12-lead ECGs combined with clinical data. These findings suggest that ECGs, a common tool in the ER, can effectively help screen for AHF.

PMID:39787045 | DOI:10.1093/ehjacc/zuaf001

Noise Health. 2024 Oct-Dec 01;26(123):474-482. doi: 10.4103/nah.nah_98_24. Epub 2024 Dec 30.

ABSTRACT

BACKGROUND: The effect of background noise on auscultation accuracy for different lung sound classes under standardised conditions, especially at lower to medium levels, remains largely unexplored. This article aims to evaluate the impact of three levels of Gaussian white noise (GWN) on the ability to identify three classes of lung sounds.

METHODS AND MATERIALS: A pre-post pilot study assessing the impact of GWN on a group of students' ability to identify lung sounds was conducted. The three intensities were applied to the three classes of lung sounds: no GWN, signal-to-noise ratio (SNR), SNR-40 (medium level) and SNR-20 (high). This resulted with three exams, each containing nine questions. Fifty-two participants underwent a 4-day training programme and were tested on their identification of lung sound classes under the three levels of GWN, but seven subjects were excluded for not completing all three assessments. Statistical analysis was performed on 45 subjects, using non-parametric tests to analyse the data. A P-value of 0.05 was considered statistically significant.

RESULTS: The GWN did not impact the overall lung sound identification capacity of medical students, with consistent scores of 66.7% across the three noise levels for all three lung sound classes combined. However, when considering sound classes separately, GWN affected the identification of normal (NAS) and discontinuous (DAS), but not continuous (CAS) types. Exam scores for NAS varied significantly across the three noise levels, with respective scores of 66.7%, 100% and 66.7%. Scores for DAS also varied, revealing 66.7%, 33.3% and 66.7%.

CONCLUSION: This study introduces a standardised simulation-based approach to investigate the effect of GWN on the accuracy of auscultation amongst medical students. Findings indicate that whilst CAS sounds are robust to background noise, the identification of NAS and DAS sounds can be compromised. The medium noise levels (SNR-40) of noise pollution had the greatest effect on the DAS lung sounds.

PMID:39787547 | DOI:10.4103/nah.nah_98_24

Chin Med J (Engl). 2024 Dec 31. doi: 10.1097/CM9.0000000000003401. Online ahead of print.

NO ABSTRACT

PMID:39787375 | DOI:10.1097/CM9.0000000000003401

J Chem Inf Model. 2025 Jan 9. doi: 10.1021/acs.jcim.4c02079. Online ahead of print.

ABSTRACT

Drug-induced liver injury (DILI) is a major challenge in drug development, often leading to clinical trial failures and market withdrawals due to liver toxicity. This study presents StackDILI, a computational framework designed to accelerate toxicity assessment by predicting DILI risk. StackDILI integrates multiple molecular descriptors to extract structural and physicochemical features, including the constitution, pharmacophore, MACCS, and E-state descriptors. Additionally, a genetic algorithm is employed for feature selection and optimization, ensuring that the most relevant features are used. These optimized features are processed through a stacking ensemble model comprising multiple tree-based machine learning models, improving prediction accuracy and interpretability. Notably, StackDILI demonstrates a strong performance on the DILIrank test set and maintains robustness across cross-validation. Moreover, interpretability analysis reveals key molecular features associated with DILI risks, providing valuable insights into toxicity prediction. To further improve accessibility, a user-friendly web interface is developed, allowing users to input SMILES strings and receive rapid predictions with ease. The StackDILI model provides a powerful tool for efficient DILI assessment, supporting safer drug development. The web interface is accessible at https://awi.cuhk.edu.cn/biosequence/StackDILI/.

PMID:39786982 | DOI:10.1021/acs.jcim.4c02079

Alzheimers Dement. 2024 Dec;20 Suppl 2:e086490. doi: 10.1002/alz.086490.

ABSTRACT

BACKGROUND: Cerebral small vessel disease (cSVD) is a leading cause of stroke and dementia. Its underlying mechanisms remain elusive and specific mechanism-based drugs are lacking.

METHOD: We integrated more than 2,800 CSF and 4,600 plasma pQTL, derived from the largest proteomic studies so far (SOMAscan 7k and 4k; in up to 35,559 individuals), and the two most prevalent MRI-markers of cSVD (MRI-cSVD, white matter hyperintensities and perivascular spaces burden; in up to 48,454 individuals) in a Mendelian Randomization (MR) framework to identify causal and druggable targets for cSVD. Identified association were followed-up using a multipronged approach: across fluids, proteomics platforms (Olink 3072, N=8,590) and lifespan (N=1,748), using both MR and individual-level data.

RESULT: We found 51 proteins associated with MRI-cSVD of which 46 in CSF and 9 in plasma. Among available significant CSF- and plasma-proteins, 32% and 31% replicated in cross-fluid and cross-platform follow-up, and 47% were associated with stroke and/or dementia at least at nominal significance. We found converging evidence that protein-cSVD associations are enriched in extracellular matrix and immune response pathways. Immunity-related proteins already showed association with MRI-cSVD already in young adults in their twenties. Furthermore, we provide genetic support for drug repositioning opportunities for cSVD, including compounds crossing the blood brain barrier.

CONCLUSION: Together, these findings provide a novel proteogenomic signature of cSVD and pave the way for novel therapeutic developments.

PMID:39785542 | DOI:10.1002/alz.086490

Alzheimers Dement. 2024 Dec;20 Suppl 7:e083393. doi: 10.1002/alz.083393.

ABSTRACT

Psychosis is a common and distressing disorder in people with Alzheimer disease, associated with a poor clinical prognosis, an increased risk of institutionalization and for which there are no approved treatments. New approaches to diagnosis and symptom assessment and treatment are beginning to move the field forward, including the emergence of psychosis at the pre-clinical or even pre-cognitive impairment stages of disease in some individuals. The Alzheimer's Association International Society to Advance Alzheimer's Research and Treatment (ISTAART) research criteria for psychosis in neurodegenerative disease, and the ISTAART criteria for mild behavioural impairment are examples of recent developments. New genomic, neuroimaging, post-mortem and neurobiology studies are beginning to refine our mechanistic understanding and providing novel opportunities for drug discovery, drug repurposing and potentially for better approaches to precision medicine. Emerging potential therapies include the 5HT2A inverse agonist pimavanserin, which is already licensed in the US for the treatment of Parkinson's disease, escitalopram, muscarinic agonists and cannabidiol. Emerging data also highlight opportunities to optimize and develop more targeted psychological therapies for people with Alzheimer's disease psychosis. The treatment of psychosis also remains a major challenge in synuclein dementias where psychosis is more frequent and more persistent, and where many patients experience severe sensitivity reactions to antipsychotic medications. There is very little work examining the mechanisms or treatment of psychosis in people with vascular dementia, which remains a major unmet need. Although the assessment and management of psychosis in people with dementia remain challenging, improved diagnosis, evolving mechanistic understanding and an increased focus on new treatment studies are providing direction and new opportunities to the field and to people with Alzheimer's disease.

PMID:39784914 | DOI:10.1002/alz.083393

J Alzheimers Dis. 2025 Jan 9:13872877241305740. doi: 10.1177/13872877241305740. Online ahead of print.

ABSTRACT

Seizures in people with Alzheimer's disease are increasingly recognized to worsen disease burden and accelerate functional decline. Harnessing established antiseizure medicine discovery strategies in rodents with Alzheimer's disease associated risk genes represents a novel way to uncover disease modifying treatments that may benefit these Alzheimer's disease patients. This commentary discusses the recent evaluation by Dejakaisaya and colleagues to assess the antiseizure and disease-modifying potential of the repurposed cephalosporin antibiotic, ceftriaxone, in the Tg2576 mouse model. The use of established epilepsy models in Alzheimer's disease research carries the potential to advance novel disease-modifying treatments.

PMID:39784685 | DOI:10.1177/13872877241305740

Alzheimers Dement. 2024 Dec;20 Suppl 8:e095063. doi: 10.1002/alz.095063.

ABSTRACT

BACKGROUND: Alzheimer's disease (AD) is a devastating form of dementia, and its prevalence is rising as human lifespan increases. Our lab created the AD-BXD mouse model, which expresses AD mutations across a genetically diverse reference panel (BXD), to identify factors that confer resilience to cognitive decline in AD. This model mimics key characteristics of human AD including variation in age of onset and severity of cognitive decline.

METHOD: To facilitate discovery of conserved mechanisms of resilience to AD, we generated a cross-species single-nuclei transcriptomic dataset from normal and AD human (ROSMAP) and AD-BXD mouse frontal cortex tissue. We interrogated resilience-associated gene expression signatures, validated resilience candidate genes with human reference data, and used a druggability ranking and drug repositioning pipeline to nominate drugs to promote resilience to AD. To learn more about the context of resilience gene expression, we used a hierarchical mapping algorithm to predict anatomical locations of cells expressing resilience gene signatures.

RESULT: We found the strongest gene expression signature associated with cognitive resilience to AD arises from excitatory layer 4/5 (eL4/5) cortical intratelencephalic neurons. This resilience signature includes genes involved in synaptic plasticity, vesicle transport, and axonal and dendritic development. We found that 27 of the 61 genes in the resilience signature are druggable and identified several candidate drugs for further investigation (Telpoukhovskaia et al., 2022). We also identified genes expressed across a continuum of cognitive performance. Our hierarchical mapping approach showed that the eL4/5 neurons expressing resilience signature genes are distributed throughout the frontal cortex, mainly in the somatomotor area.

CONCLUSION: We identified 61 candidate resilience genes to target with new or existing drugs. We also determined that expression of resilience candidate genes occurs in eL4/5 neurons in the somatomotor region of the cortex. Ongoing projects in the lab aim to evaluate efficacy of nominated drugs and profile learning-specific proteomes of eL4/5 neurons in resilient and susceptible AD-BXD strains. When integrated with existing genetic, behavioral, and pathological data, our work will elucidate the cellular, molecular, and genetic mechanisms that contribute to cognitive resilience in face of neurodegenerative disease pathology.

PMID:39783534 | DOI:10.1002/alz.095063

Alzheimers Dement. 2024 Dec;20 Suppl 8:e095709. doi: 10.1002/alz.095709.

ABSTRACT

BACKGROUND: Cerebral amyloid angiopathy (CAA), the accumulation of amyloid proteins in the cerebral vasculature, increases the risk of stroke and vascular cognitive impairment and dementia (VCID). Not only is there no treatment for CAA, but the condition is also highly comorbid with Alzheimer's disease (AD), and its presence may serve as a contraindication to treating patients with anti-amyloid therapies due to an increased risk of hemorrhage and edema. Therefore, it is crucial to identify novel treatments for individuals with CAA. Epidemiological studies suggest that certain antihypertensive medications, including those that target the renin-angiotensin system (RAS), are associated with a decreased risk of dementia. This study assesses whether two FDA-approved RAS-targeting drugs: telmisartan [a moderately brain-penetrant angiotensin receptor blocker (ARB)], and lisinopril [a brain-penetrant angiotensin-converting enzyme (ACE) inhibitor]; can be repurposed for the treatment of CAA.

METHODS: At either ∼3 months (early intervention) or ∼8 months (later intervention) of age, male and female Tg-SwDI mice began treatment with either telmisartan (1 mg/kg/day) or lisinopril (15 mg/kg/day) dissolved in drinking water or received plain drinking water only. Age- and sex-matched C57BL/6J mice receiving plain drinking water served as wild-type controls. Following 4 months of treatment, mice underwent blood pressure measurement followed by behavioral testing prior to euthanasia.

RESULTS: Voluntary oral consumption delivered doses similar to the target dose for both drugs. At the doses used, telmisartan and lisinopril treatment did not significantly reduce blood pressure in Tg-SwDI mice. Our findings thus far suggest that these drug treatments, particularly lisinopril, may mitigate cognitive-behavioral deficits observed in Tg-SwDI mice.

CONCLUSIONS: Ongoing experiments are being completed to increase sample sizes and investigate the potential benefits of telmisartan and lisinopril to mitigate neuropathological and cognitive impairment in Tg-SwDI mice. If findings support our hypothesis, this will demonstrate that these drugs could be repurposed to prevent and/or treat CAA, reducing the worldwide burden of stroke and dementia.

PMID:39783163 | DOI:10.1002/alz.095709