The Amyloid precursor protein (APP) is a transmembrane glycoprotein from which amyloid-β (Aβ) peptides are generated after proteolytic cleavage. Aβ peptides are the main constituent of amyloid plaques in Alzheimer\'s Disease (AD). The physiological functions of APP in the human adult brain are very diverse including intracellular signaling, synaptic and neuronal plasticity, and cell adhesion, among others. There is growing evidence that APP becomes dysfunctional in AD and that this dyshomeostasis may impact several APP functions beyond Aβ generation. The vast majority of current anti-amyloid approaches in AD have focused on reducing the synthesis of Aβ or increasing the clearance of brain Aβ aggregates following a paradigm in which Aβ plays a solo in APP dyshomeostasis. A wider view places APP at the center stage in which Aβ is an important, but not the only, factor involved in APP dyshomeostasis. Under this paradigm, APP dysfunction is universal in AD, but with some differences across different subtypes. Little is known about how to approach APP dysfunction therapeutically beyond anti-Aβ strategies. In this review, we will describe the role of APP dyshomeostasis in AD beyond Aβ and the potential therapeutic strategies targeting APP.

Microbes constitute the most prevalent life form on Earth, yet their remarkable diversity remains mostly unrecognized. Microbial diversity in vertebrate models presents a significant challenge for investigating host-microbiome interactions. The model organism Caenorhabditis elegans has many advantages for delineating the effects of host genetics on microbial composition. In the wild, the C. elegans gut contains various microbial species, while in the laboratory it is usually a host for a single bacterial species. There is a potential host-microbe interaction between microbial metabolites, drugs, and C. elegans phenotypes. This mini-review aims to summarize the current understanding regarding the microbiome in C. elegans. Examples using C. elegans to study host-microbe-metabolite interactions are discussed.

Collagen, a versatile family of proteins with 28 members and 44 genes, is pivotal in maintaining tissue integrity and function. It plays a crucial role in physiological processes like wound healing, hemostasis, and pathological conditions such as fibrosis and cancer. Collagen is a target in these processes. Direct methods for collagen modulation include enzymatic breakdown and molecular binding approaches. For instance, Clostridium histolyticum collagenase is effective in treating localized fibrosis. Polypeptides like collagen-binding domains offer promising avenues for tumor-specific immunotherapy and drug delivery. Indirect targeting of collagen involves regulating cellular processes essential for its synthesis and maturation, such as translation regulation and microRNA activity. Enzymes involved in collagen modification, such as prolyl-hydroxylases or lysyl-oxidases, are also indirect therapeutic targets. From another perspective, collagen is also a natural source of drugs. Enzymatic degradation of collagen generates bioactive fragments known as matrikines and matricryptins, which exhibit diverse pharmacological activities. Overall, collagen-derived peptides present significant therapeutic potential beyond tissue repair, offering various strategies for treating fibrosis, cancer, and genetic disorders. Continued research into specific collagen targeting and the application of collagen and its derivatives may lead to the development of novel treatments for a range of pathological conditions.

The diagnosis of anaphylaxis is based on the clinical history. The utility of tryptase measurements in clinical setting is limited. Mas-related G protein-coupled receptor-X2 (MRGPRX2) is expressed in mast cells and is involved in the degranulation of these cells. We evaluated the potential of MRGPRX2 as a diagnostic biomarker in patients with iodinated contrast media (ICM)-induced immediate hypersensitivity reactions (IHRs). A total of 173 patients with documented ICM-induced IHR within 4 months from registration were enrolled and skin tests for the culprit ICM were performed. The time interval was evaluated as the duration between the onset of ICM-induced IHR and the measurement of serum MRGPRX2 levels. Serum MRGPRX2 concentration was determined using an enzyme-linked immunosorbent assay kit. Of the 173 patients, 33 and 140 were included in the anaphylaxis and non-anaphylaxis groups, respectively. Serum MRGPRX2 levels were significantly higher in the anaphylaxis than in the non-anaphylaxis group (29.9 ± 24.1 vs. 20.7±17.5, P = 0.044). Serum MRGPRX2 showed a moderate predictive ability for anaphylaxis, with an area under the curve of 0.61 (P = 0.058). When groups were classified based on the time interval, T1(0-2months) and T2 (2-4months), patients with anaphylaxis had higher MRGPRX2 levels compared to the non-anaphylaxis group in the T2 group (36.5±19.2 vs. 20.5±19.0, P = 0.035). This pilot study shows that serum MRGPRX2 is a potential long-term biomarker for predicting anaphylaxis, particularly ICM-induced anaphylaxis. Further studies are needed to determine the role of MRGPRX2 in anaphylaxis in a larger population of patients with various drug-induced IHRs.

OBJECTIVE: In Canada, substance-related accidental acute toxicity deaths (AATDs) continue to rise at the national and sub-national levels. However, it is unknown if, where, when, and to what degree AATDs cluster in space, time, and space-time across the country. The objectives of this study were to 1) assess for clusters of AATDs that occurred in Canada during 2016 and 2017 at the national and provincial/territorial (P/T) levels, and 2) examine the substance types detected in AATD cases within each cluster.
METHODS: Two years of person-level data on AATDs were abstracted from coroner and medical examiner files using a standardized data collection tool, including the decedent\'s postal code and municipality information on the places of residence, acute toxicity (AT) event, and death, and the substances detected in the death. Data were combined with Canadian census information to create choropleth maps depicting AATD rates by census division. Spatial scan statistics were used to build Poisson models to identify clusters of high rates (p < 0.05) of AATDs at the national and P/T levels in space, time, and space-time over the study period. AATD cases within clusters were further examined for substance types most present in each cluster.
RESULTS: Eight clusters in five regions of Canada at the national level and 24 clusters in 15 regions at the P/T level were identified, highlighting where AATDs occurred at far higher rates than the rest of the country. The risk ratios of identified clusters ranged from 1.28 to 9.62. Substances detected in clusters varied by region and time, however, opioids, stimulants, and alcohol were typically the most commonly detected substances within clusters.
CONCLUSIONS: Our findings are the first in Canada to reveal the geographic disparities in AATDs at national and P/T levels using spatial scan statistics. Rates associated with substance types within each cluster highlight which substance types were most detected in the identified regions. Findings may be used to guide intervention/program planning and provide a picture of the 2016 and 2017 context that can be used for comparisons of the geographic distribution of AATDs and substances with different time periods.

BACKGROUND: In health care, diagnosis codes in claims data and electronic health records (EHRs) play an important role in data-driven decision making. Any analysis that uses a patient\'s diagnosis codes to predict future outcomes or describe morbidity requires a numerical representation of this diagnosis profile made up of string-based diagnosis codes. These numerical representations are especially important for machine learning models. Most commonly, binary-encoded representations have been used, usually for a subset of diagnoses. In real-world health care applications, several issues arise: patient profiles show high variability even when the underlying diseases are the same, they may have gaps and not contain all available information, and a large number of appropriate diagnoses must be considered.
OBJECTIVE: We herein present Pat2Vec, a self-supervised machine learning framework inspired by neural network-based natural language processing that embeds complete diagnosis profiles into a small real-valued numerical vector.
METHODS: Based on German outpatient claims data with diagnosis codes according to the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10), we discovered an optimal vectorization embedding model for patient diagnosis profiles with Bayesian optimization for the hyperparameters. The calibration process ensured a robust embedding model for health care-relevant tasks by aggregating the metrics of different regression and classification tasks using different machine learning algorithms (linear and logistic regression as well as gradient-boosted trees). The models were tested against a baseline model that binary encodes the most common diagnoses. The study used diagnosis profiles and supplementary data from more than 10 million patients from 2016 to 2019 and was based on the largest German ambulatory claims data set. To describe subpopulations in health care, we identified clusters (via density-based clustering) and visualized patient vectors in 2D (via dimensionality reduction with uniform manifold approximation). Furthermore, we applied our vectorization model to predict prospective drug prescription costs based on patients\' diagnoses.
RESULTS: Our final models outperform the baseline model (binary encoding) with equal dimensions. They are more robust to missing data and show large performance gains, particularly in lower dimensions, demonstrating the embedding model\'s compression of nonlinear information. In the future, other sources of health care data can be integrated into the current diagnosis-based framework. Other researchers can apply our publicly shared embedding model to their own diagnosis data.
CONCLUSIONS: We envision a wide range of applications for Pat2Vec that will improve health care quality, including personalized prevention and signal detection in patient surveillance as well as health care resource planning based on subcohorts identified by our data-driven machine learning framework.

UNASSIGNED: Stigma among healthcare professionals toward people who use drugs (PWUDs) must be addressed for recovery. However, research on this topic is limited in Japan, therefore we developed a brand-new scale through coproduction with PWUDs to measure stigma and conducted a survey using the developed scale to examine what influences stigma towards PWUDs in Japanese healthcare settings.
UNASSIGNED: Based on interviews with PWUDs and their families, we developed a survey containing 24 questions on stigma toward PWUDs. The survey was sent to healthcare professionals working in the public sector. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were conducted to determine the factor construct. Generalized linear mixed model (GLMM) analyses with each factor of the stigma questions set as a dependent variable were conducted to discover the specific contribution of each variable to professionals\' stigma.
UNASSIGNED: The six factors suggested by the EFA showed a good fit, as confirmed by the CFA of the stigma questions. GLMM discovered that \"currently providing treatment services to PWUDs,\" \"having PWUDs close to themselves,\" and \"experiencing violence by the client when providing treatment services\" were significantly associated with higher stigma scale scores. \"Experience in receiving support,\" \"attending self-help groups,\" and \"using peer-based recovery support with PWUDs\" were significantly associated with lower stigma scale scores.
UNASSIGNED: The scale coproduced with local PWUDs can be a reliable tool to measure the stigma PWUDs face in Japan. Further results indicate that interaction with recovered PWUDs should be promoted.

We aimed to assess salivary and seroprevalence of Toxoplasma immunoglobulins in risky populations and evaluate drug docking targeting TgERP. A cross-sectional study was conducted in Alexandria University hospitals\' outpatient clinics. 192 participants were enrolled from September 2022 to November 2023. Anti-Toxoplasma IgG and IgM were determined in serum and saliva by ELISA. An in-Silico study examined TgERP\'s protein-protein interactions (PPIs) with pro-inflammatory cytokine receptors, anti-inflammatory cytokine, cell cycle progression regulatory proteins, a proliferation marker, and nuclear envelope integrity-related protein Lamin B1. Our findings revealed that anti-T. gondii IgG were detected in serum (66.1%) and saliva (54.7%), with 2.1% of both samples were positive for IgM. Salivary IgG had 75.59% sensitivity, 86.15% specificity, 91.40% PPV, 64.40% NPP, 79.17% accuracy and fair agreement with serum IgG. On the other hand, the sensitivity, specificity, PPV, NPV, and accuracy in detecting salivary IgM were 75.0%, 99.47%, 75.0%, 99.47%, and 98.96%. AUC 0.859 indicates good discriminatory power. Examined synthetic drugs and natural products can target specific amino acids residues of TgERP that lie at the same binding interface with LB1 and Ki67, subsequently, hindering their interaction. Hence, salivary samples can be a promising diagnostic approach. The studied drugs can counteract the pro-inflammatory action of TgERP.

UNASSIGNED: Acanthamoeba infection is a serious public health concern, necessitating the development of effective and safe anti-Acanthamoeba chemotherapies. Poly (ADP-ribose) polymerases (PARPs) govern a colossal amount of biological processes, such as DNA damage repair, protein degradation and apoptosis. Multiple PARP-targeted compounds have been approved for cancer treatment. However, repurposing of PARP inhibitors to treat Acanthamoeba is poorly understood.
UNASSIGNED: In the present study, we attempted to fill these knowledge gaps by performing anti-Acanthamoeba efficacy assays, cell biology experiments, bioinformatics, and transcriptomic analyses.
UNASSIGNED: Using a homology model of Acanthamoeba poly (ADP-ribose) polymerases (PARPs), molecular docking of approved drugs revealed three potential inhibitory compounds: olaparib, venadaparib and AZ9482. In particular, venadaparib exhibited superior docking scores (-13.71) and favorable predicted binding free energy (-89.28 kcal/mol), followed by AZ9482, which showed a docking score of -13.20 and a binding free energy of -92.13 kcal/mol. Notably, the positively charged cyclopropylamine in venadaparib established a salt bridge (through E535) and a hydrogen bond (via N531) within the binding pocket. For comparison, AZ9482 was well stacked by the surrounding aromatic residues including H625, Y652, Y659 and Y670. In an assessment of trophozoites viability, AZ9482 exhibited a dose-and time-dependent anti-trophozoite effect by suppressing Acanthamoeba PARP activity, unlike olaparib and venadaparib. An Annexin V-fluorescein isothiocyanate/propidium iodide apoptosis assay revealed AZ9482 induced trophozoite necrotic cell death rather than apoptosis. Transcriptomics analyses conducted on Acanthamoeba trophozoites treated with AZ9482 demonstrated an atlas of differentially regulated proteins and genes, and found that AZ9482 rapidly upregulates a multitude of DNA damage repair pathways in trophozoites, and intriguingly downregulates several virulent genes. Analyzing gene expression related to DNA damage repair pathway and the rate of apurinic/apyrimidinic (AP) sites indicated DNA damage efficacy and repair modulation in Acanthamoeba trophozoites following AZ9482 treatment.
UNASSIGNED: Collectively, these findings highlight AZ9482, as a structurally unique PARP inhibitor, provides a promising prototype for advancing anti-Acanthamoeba drug research.

Functionalized graphene oxide nanoparticles (NPs) have emerged as promising nanocarriers for drug delivery in lung cancer therapy. Quercetin and lurbinectedin encapsulated in graphene oxide (GO) NPs are tested for treating A549 lung cancer cells. Spectroscopic analyses show that graphene oxide functionalization creates a transparent, smooth surface for drug loading. Treatment with quercetin/lurbinectedin-loaded GO NPs induces notable cytotoxic effects in lung cancer cells, as evidenced by distinct morphological alterations and confirmed apoptotic cellular death observed through fluorescence microscopy. Additionally, our study highlights the impact of this approach on lung cancer metastasis, supported by qRT-PCR analysis of relative gene expression levels, including p53, Bax, Caspase-3, and Bcl 2, revealing robust molecular mechanisms underlying therapeutic efficacy against A549 and PC9 cell lines. Flow cytometric analyses further confirm the induction of cellular death in lung cancer cells following administration of the nanoformulation. Our findings show that quercetin/lurbinectedin-loaded GO NPs may be a promising lung cancer treatment, opening new avenues for targeted and effective therapies.