DSN-DDI: an accurate and generalized framework for drug-drug interaction prediction by dual-view representation learning.

Drug-drug interaction (DDI) prediction identifies interactions of drug combinations in which the adverse side effects caused by the physicochemical incompatibility have attracted much attention. Previous studies usually model drug information from single or dual views of the whole drug molecules but ignore the detailed interactions among atoms, which leads to incomplete and noisy information and limits the accuracy of DDI prediction. In this work, we propose a novel dual-view drug representation learning network for DDI prediction ('DSN-DDI'), which employs local and global representation learning modules iteratively and learns drug substructures from the single drug ('intra-view') and the drug pair ('inter-view') simultaneously. Comprehensive evaluations demonstrate that DSN-DDI significantly improved performance on DDI prediction for the existing drugs by achieving a relatively improved accuracy of 13.01% and an over 99% accuracy under the transductive setting. More importantly, DSN-DDI achieves a relatively improved accuracy of 7.07% to unseen drugs and shows the usefulness for real-world DDI applications. Finally, DSN-DDI exhibits good transferability on synergistic drug combination prediction and thus can serve as a generalized framework in the drug discovery field.

How side effects can improve treatment efficacy: a randomized trial.

While treatment side effects may adversely impact patients, they could also potentially function as indicators for effective treatment. In this study, we investigated whether and how side effects can trigger positive treatment expectations and enhance treatment outcomes. In this pre-registered trial (DRKS00026648), 77 healthy participants were made to believe that they will receive fentanyl nasal sprays before receiving thermal pain in a controlled experimental setting. However, nasal sprays did not contain fentanyl, rather they either contained capsaicin to induce a side effect (mild burning sensation) or saline (inert). After the first session, participants were randomized to two groups and underwent functional MRI. One group continued to believe that the nasal sprays could contain fentanyl while the other group was explicitly informed that no fentanyl was included. This allowed for the independent manipulation of the side effects and the expectation of pain relief. Our results revealed that nasal sprays with a side effect lead to lower pain than inert nasal sprays without side effects. The influence of side effects on pain was dependent on individual beliefs about how side effects are related to treatment outcome, as well as on expectations about received treatment. Functional MRI data indicated an involvement of the descending pain modulatory system including the anterior cingulate cortex and the periaqueductal gray during pain after experiencing a nasal spray with side effects. In summary, our data show that mild side effects can serve as a signal for effective treatment thereby influencing treatment expectations and outcomes, which is mediated by the descending pain modulatory system. Using these mechanisms in clinical practice could provide an efficient way to optimize treatment outcome. In addition, our results indicate an important confound in clinical trials, where a treatment (with potential side effects) is compared to placebo.

HMMF: a hybrid multi-modal fusion framework for predicting drug side effect frequencies.

BACKGROUND: The identification of drug side effects plays a critical role in drug repositioning and drug screening. While clinical experiments yield accurate and reliable information about drug-related side effects, they are costly and time-consuming. Computational models have emerged as a promising alternative to predict the frequency of drug-side effects. However, earlier research has primarily centered on extracting and utilizing representations of drugs, like molecular structure or interaction graphs, often neglecting the inherent biomedical semantics of drugs and side effects. RESULTS: To address the previously mentioned issue, we introduce a hybrid multi-modal fusion framework (HMMF) for predicting drug side effect frequencies. Considering the wealth of biological and chemical semantic information related to drugs and side effects, incorporating multi-modal information offers additional, complementary semantics. HMMF utilizes various encoders to understand molecular structures, biomedical textual representations, and attribute similarities of both drugs and side effects. It then models drug-side effect interactions using both coarse and fine-grained fusion strategies, effectively integrating these multi-modal features. CONCLUSIONS: HMMF exhibits the ability to successfully detect previously unrecognized potential side effects, demonstrating superior performance over existing state-of-the-art methods across various evaluation metrics, including root mean squared error and area under receiver operating characteristic curve, and shows remarkable performance in cold-start scenarios.

Crossfeat: a transformer-based cross-feature learning model for predicting drug side effect frequency.

BACKGROUND: Safe drug treatment requires an understanding of the potential side effects. Identifying the frequency of drug side effects can reduce the risks associated with drug use. However, existing computational methods for predicting drug side effect frequencies heavily depend on known drug side effect frequency information. Consequently, these methods face challenges when predicting the side effect frequencies of new drugs. Although a few methods can predict the side effect frequencies of new drugs, they exhibit unreliable performance owing to the exclusion of drug-side effect relationships. RESULTS: This study proposed CrossFeat, a model based on convolutional neural network-transformer architecture with cross-feature learning that can predict the occurrence and frequency of drug side effects for new drugs, even in the absence of information regarding drug-side effect relationships. CrossFeat facilitates the concurrent learning of drugs and side effect information within its transformer architecture. This simultaneous exchange of information enables drugs to learn about their associated side effects, while side effects concurrently acquire information about the respective drugs. Such bidirectional learning allows for the comprehensive integration of drug and side effect knowledge. Our five-fold cross-validation experiments demonstrated that CrossFeat outperforms existing studies in predicting side effect frequencies for new drugs without prior knowledge. CONCLUSIONS: Our model offers a promising approach for predicting the drug side effect frequencies, particularly for new drugs where prior information is limited. CrossFeat's superior performance in cross-validation experiments, along with evidence from case studies and ablation experiments, highlights its effectiveness.

Microbial bile acid metabolite ameliorates mycophenolate mofetil-induced gastrointestinal toxicity through vitamin D3 receptor.

Mycophenolate mofetil (MMF) is one of the most used immunosuppressive drugs in organ transplantation, but frequent gastrointestinal (GI) side effects through unknown mechanisms limit its clinical use. Gut microbiota and its metabolites were recently reported to play a vital role in MMF-induced GI toxicity, but the specific mechanism of how they interact with the human body is still unclear. Here, we found that secondary bile acids (BAs), as bacterial metabolites, were significantly reduced by MMF administration in the gut of mice. Microbiome data and fecal microbiota transfer model supported a microbiota-dependent effect on the reduction of secondary BAs. Supplementation of the secondary BA lithocholic acid alleviated MMF-induced weight loss, colonic inflammation, and oxidative phosphorylation damage. Genetic deletion of the vitamin D3 receptor (VDR), which serves as a primary colonic BA receptor, in colonic epithelial cells (VDRΔIEC) abolished the therapeutic effect of lithocholic acid on MMF-induced GI toxicity. Impressively, we discovered that paricalcitol, a Food and Drug Administration-approved VDR agonist that has been used in clinics for years, could effectively alleviate MMF-induced GI toxicity. Our study reveals a previously unrecognized mechanism of gut microbiota, BAs, and VDR signaling in MMF-induced GI side effects, offering potential therapeutic strategies for clinics.

Toxicity due to steroid treatment in ITP remains a concern.

Corticosteroids are the preferred first-line treatment in ITP in guidelines. The analyses by Wang et al. shows that hospital-registered steroid-related toxicity occurs frequently and emphasizes that exposure should be for a limited duration of time. Commentary on: Wang et al. Longitudinal evaluation of adverse events due to steroid use in primary immune thrombocytopenia: a population-based study. Br J Haematol 2024;204:1986-1993.

Diurnal variation of cisplatin-induced renal toxicity in ICR mice.

Cisplatin (CDDP) is a platinum-based anticancer drug widely prescribed for its effectiveness in treating various forms of cancer. However, its major side effect is nephrotoxicity. Although several methods have been developed to mitigate CDDP-induced nephrotoxicity, an optimal approach has yet to be established. This study aimed to investigate the "chronotoxicity" of CDDP as a potential strategy to reduce its side effects. Male ICR mice were treated with CDDP (20 mg/kg, intraperitoneal injection, one shot) at zeitgeber time (ZT) 2 or ZT14 (light or dark phase). After 72 h, we collected plasma and kidney and evaluated several markers. We found that body weight change between ZT2 and ZT14 by CDDP was comparable. In contrast, many toxicological factors, such as plasma blood urine nitrogen, plasma creatinine, renal oxidative stress (malondialdehyde), DNA damage (γH2AX), acute kidney injury biomarker (KIM-1), and inflammation (Tnfα), were significantly induced at ZT14 compared to than that of ZT2. Our present data suggested that chronotoxicology might provide beneficial information on the importance of administration timings for toxic evaluations and unacceptable side effects.

A similarity-based deep learning approach for determining the frequencies of drug side effects.

The side effects of drugs present growing concern attention in the healthcare system. Accurately identifying the side effects of drugs is very important for drug development and risk assessment. Some computational models have been developed to predict the potential side effects of drugs and provided satisfactory performance. However, most existing methods can only predict whether side effects will occur and cannot determine the frequency of side effects. Although a few existing methods can predict the frequency of drug side effects, they strongly depend on the known drug-side effect relationships. Therefore, they cannot be applied to new drugs without known side effect frequency information. In this paper, we develop a novel similarity-based deep learning method, named SDPred, for determining the frequencies of drug side effects. Compared with the existing state-of-the-art models, SDPred integrates rich features and can be applied to predict the side effect frequencies of new drugs without any known drug-side effect association or frequency information. To our knowledge, this is the first work that can predict the side effect frequencies of new drugs in the population. The comparison results indicate that SDPred is much superior to all previously reported models. In addition, some case studies also demonstrate the effectiveness of our proposed method in practical applications. The SDPred software and data are freely available at https://github.com/zhc940702/SDPred, https://zenodo.org/record/5112573 and https://hub.docker.com/r/zhc940702/sdpred.

Identification of drug-side effect association via restricted Boltzmann machines with penalized term.

In the entire life cycle of drug development, the side effect is one of the major failure factors. Severe side effects of drugs that go undetected until the post-marketing stage leads to around two million patient morbidities every year in the United States. Therefore, there is an urgent need for a method to predict side effects of approved drugs and new drugs. Following this need, we present a new predictor for finding side effects of drugs. Firstly, multiple similarity matrices are constructed based on the association profile feature and drug chemical structure information. Secondly, these similarity matrices are integrated by Centered Kernel Alignment-based Multiple Kernel Learning algorithm. Then, Weighted K nearest known neighbors is utilized to complement the adjacency matrix. Next, we construct Restricted Boltzmann machines (RBM) in drug space and side effect space, respectively, and apply a penalized maximum likelihood approach to train model. At last, the average decision rule was adopted to integrate predictions from RBMs. Comparison results and case studies demonstrate, with four benchmark datasets, that our method can give a more accurate and reliable prediction result.

DSGAT: predicting frequencies of drug side effects by graph attention networks.

A critical issue of drug risk-benefit evaluation is to determine the frequencies of drug side effects. Randomized controlled trail is the conventional method for obtaining the frequencies of side effects, while it is laborious and slow. Therefore, it is necessary to guide the trail by computational methods. Existing methods for predicting the frequencies of drug side effects focus on modeling drug-side effect interaction graph. The inherent disadvantage of these approaches is that their performance is closely linked to the density of interactions but which is highly sparse. More importantly, for a cold start drug that does not appear in the training data, such methods cannot learn the preference embedding of the drug because there is no link to the drug in the interaction graph. In this work, we propose a new method for predicting the frequencies of drug side effects, DSGAT, by using the drug molecular graph instead of the commonly used interaction graph. This leads to the ability to learn embeddings for cold start drugs with graph attention networks. The proposed novel loss function, i.e. weighted $\varepsilon$-insensitive loss function, could alleviate the sparsity problem. Experimental results on one benchmark dataset demonstrate that DSGAT yields significant improvement for cold start drugs and outperforms the state-of-the-art performance in the warm start scenario. Source code and datasets are available at https://github.com/xxy45/DSGAT.

Integrating specific and common topologies of heterogeneous graphs and pairwise attributes for drug-related side effect prediction.

MOTIVATION: Computerized methods for drug-related side effect identification can help reduce costs and speed up drug development. Multisource data about drug and side effects are widely used to predict potential drug-related side effects. Heterogeneous graphs are commonly used to associate multisourced data of drugs and side effects which can reflect similarities of the drugs from different perspectives. Effective integration and formulation of diverse similarities, however, are challenging. In addition, the specific topology of each heterogeneous graph and the common topology of multiple graphs are neglected. RESULTS: We propose a drug-side effect association prediction model, GCRS, to encode and integrate specific topologies, common topologies and pairwise attributes of drugs and side effects. First, multiple drug-side effect heterogeneous graphs are constructed using various kinds of similarities and associations related to drugs and side effects. As each heterogeneous graph has its specific topology, we establish separate module based on graph convolutional autoencoder (GCA) to learn the particular topology representation of each drug node and each side effect node, respectively. Since multiple graphs reflect the complex relationships among the drug and side effect nodes and contain common topologies, we construct a module based on GCA with sharing parameters to learn the common topology representations of each node. Afterwards, we design an attention mechanism to obtain more informative topology representations at the representation level. Finally, multi-layer convolutional neural networks with attribute-level attention are constructed to deeply integrate the similarity and association attributes of a pair of drug-side effect nodes. Comprehensive experiments show that GCRS's prediction performance is superior to other comparing state-of-the-art methods for predicting drug-side effect associations. The recall rates in top-ranked candidates and case studies on five drugs further demonstrate GCRS's ability in discovering potential drug-related side effects. CONTACT: zhang@hlju.edu.cn.

Mechanisms of adverse mammary effect induced by olanzapine and therapeutic interventions in rat model.

BACKGROUND: Olanzapine antagonizes dopamine receptors and is prescribed to treat multiple psychiatric conditions. The main side effect of concern for olanzapine is weight gain and metabolic syndrome. Olanzapine induces hyperprolactinemia, however its effect on the mammary gland is poorly documented. METHODS: Rats received olanzapine by gavage or in drinking water at 1, 3, and 6 mg/kg/day for 5-40 days or 100 days, with and without coadministration of bromocriptine or aripiprazole and using once daily or continuous administration strategies. Histomorphology of the mammary gland, concentrations of prolactin, estradiol, progesterone, and olanzapine in serum, mammary gland and adipose tissue, and mRNA and protein expressions of prolactin receptors were analyzed. RESULTS: In adult and prepubescent female rats and male rats, olanzapine induced significant development of mammary glands in dose- and time-dependent manners, with histopathological hyperplasia of mammary ducts and alveoli with lumen dilation and secretion, marked increase of mammary prolactin receptor expression, a marker of breast tissue, and with mild increase of circulating prolactin. This side effect can be reversed after medication withdrawal, but long-term olanzapine treatment for 100 days implicated tumorigenic potentials indicated by usual ductal epithelial hyperplasia. Olanzapine induced mammary development was prevented with the coaddition of the dopamine agonist bromocriptine or partial agonist aripiprazole, or by continuous administration of medication instead of a once daily regimen. CONCLUSIONS: These results shed light on the previously overlooked effect of olanzapine on mammary development and present experimental evidence to support current clinical management strategies of antipsychotic induced side effects in the breast.

Myelinolysis cases presenting with manic attack after rapid correction of hyponatremia: Two cases.

OBJECTIVE: Myelinolysis is a neurological condition that can display diverse psychiatric symptoms, with electrolyte imbalance, alcoholism and malnutrition being the frequent causes. Rapid correction of hyponatremia may trigger pontine and extra-pontine myelinolysis. CASES: This paper examines two cases: one of hyponatremia after antihypertensive use and the other of myelinolysis due to rapid correction of hyponatremia. Since myelinolysis appeared as a manic episode, the patients sought treatment at the psychiatry outpatient clinic. Further tests were conducted to rule out organic causes and the diagnosis was confirmed prior to referring the patients to the neurology clinic. CONCLUSION: Psychiatrists should be meticulous in excluding organic causes in first-episode mania and consider these possibilities in the differential diagnosis for the pertinent patient group.

Case reports: Could sexual dysfunction in women with migraine be a side effect of CGRP inhibition?

BACKGROUND: The development and approval of antibodies targeting calcitonin gene-related peptide or its receptor mark a revolutionary era for preventive migraine treatment. Real-world evidence sheds light on rare, stigmatized or overlooked side effects of these drugs. One of these potential side effects is sexual dysfunction. CASE REPORTS: We present two cases of one 42-year-old and one 45-year-old female patient with chronic migraine who both reported sexual dysfunction as a possible side effect of treatment with galcanezumab, a monoclonal antibody targeting calcitonin gene-related peptide. DISCUSSION: As calcitonin gene-related peptide is involved in vaginal lubrication as well as genital sensation and swelling, inhibiting the calcitonin gene-related peptide pathway may lead to sexual dysfunction as a potential side effect. CONCLUSION: Sexual dysfunction in female migraine patients might be a rare and overlooked side effect of monoclonal antibodies targeting the calcitonin gene-related peptide pathway. Considering the discomfort and stigma surrounding both migraine and sexual dysfunction, we advocate for an open attitude and awareness among clinicians toward such side effects.

Not all types of depressed patients who persist with their antidepressant treatment improve in side effect complaints: A comparison of treatment completers and dropouts in the STAR*D trial.

INTRODUCTION: There is a "traditional belief" that antidepressant side effect complaints improve with medication persistence; however, support for this theory has remained inconclusive. We aimed to examine if side effect complaints improved over time by modeling the relationship between side effect complaints and time at dropout for patients receiving citalopram during the first level of acute treatment in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial. METHODS: We categorized the 2833 patients into five patterns by week of dropout. We used pattern-mixture modeling to model change in side effect complaints (frequency, intensity, and burden) over the 12-week course of treatment, while accounting for attrition and depressive severity. Using post-hoc linear contrasts, we compared the attrition patterns with the completers' pattern for severity of side effect complaints at each respective last visit prior to dropout as well as averaged side effect complaints across the duration of treatment. We also reported frequencies and tolerability of side effects for nine organ/function systems over the course of treatment. RESULTS: Patients who dropped out early exhibited worsening side effect burden and patients who dropped out later showed improvements in side effect frequency and intensity. Treatment completers improved in all side effect complaints over the course of treatment. Early attrition patterns had more severe side effect complaints for both tests of post-hoc linear contrasts than later attrition patterns and completers. CONCLUSIONS: Side effect complaints from antidepressant treatment improve over time, but only for some types of patients. As a precaution for early dropout, clinicians should monitor patients who exhibit worsening and more severe side effect complaints-especially in the first 6 weeks of antidepressant treatment. In addition, clinicians may want to consider changing the type of treatment early on for these patients, rather than encouraging them to persist with their current medication.

Safety analysis of Oseltamivir and Baloxavir Marboxil after market approval: a pharmacovigilance study based on the FDA adverse event reporting system.

BACKGROUND AND OBJECTIVES: Amidst limited influenza treatment options, evaluating the safety of Oseltamivir and Baloxavir Marboxil is crucial, particularly given their comparable efficacy. This study investigates post-market safety profiles, exploring adverse events (AEs) and their drug associations to provide essential clinical references. METHODS: A meticulous analysis of FDA Adverse Event Reporting System (FAERS) data spanning the first quarter of 2004 to the fourth quarter of 2022 was conducted. Using data mining techniques like reporting odds ratio (ROR), proportional reporting ratio, Bayesian Confidence Propagation Neural Network, and Multiple Gamma Poisson Shrinkage, AEs related to Oseltamivir and Baloxavir Marboxil were examined. Venn analysis compared and selected specific AEs associated with each drug. RESULTS: Incorporating 15,104 Oseltamivir cases and 1,594 Baloxavir Marboxil cases, Wain analysis unveiled 21 common AEs across neurological, psychiatric, gastrointestinal, dermatological, respiratory, and infectious domains. Oseltamivir exhibited 221 significantly specific AEs, including appendicolith [ROR (95% CI), 459.53 (340.88 ∼ 619.47)], acne infantile [ROR (95% CI, 368.65 (118.89 ∼ 1143.09)], acute macular neuroretinopathy [ROR (95% CI), 294.92 (97.88 ∼ 888.64)], proctitis [ROR (95% CI), 245.74 (101.47 ∼ 595.31)], and Purpura senile [ROR (95% CI), 154.02 (81.96 ∼ 289.43)]. designated adverse events (DMEs) associated with Oseltamivir included fulminant hepatitis [ROR (95% CI), 12.12 (8.30-17.72), n=27], ventricular fibrillation [ROR (95% CI), 7.68 (6.01-9.83), n=64], toxic epidermal necrolysis [ROR (95% CI), 7.21 (5.74-9.05), n=75]. Baloxavir Marboxil exhibited 34 specific AEs, including Melaena [ROR (95% CI), 21.34 (14.15-32.18), n = 23], cystitis haemorrhagic [ROR (95% CI), 20.22 (7.57-54.00), n = 4], ileus paralytic [ROR (95% CI), 18.57 (5.98-57.71), n = 3], and haemorrhagic diathesis [ROR (95% CI), 16.86 (5.43-52.40)), n = 3]. DMEs associated with Baloxavir Marboxil included rhabdomyolysis [ROR (95% CI), 15.50 (10.53 ∼ 22.80), n = 26]. CONCLUSION: Monitoring fulminant hepatitis during Oseltamivir treatment, especially in patients with liver-related diseases, is crucial. Oseltamivir's potential to induce abnormal behavior, especially in adolescents, necessitates special attention. Baloxavir Marboxil, with lower hepatic toxicity, emerges as a potential alternative for patients with liver diseases. During Baloxavir Marboxil treatment, focused attention on the occurrence of rhabdomyolysis is advised, necessitating timely monitoring of relevant indicators for those with clinical manifestations. The comprehensive data aims to provide valuable insights for clinicians and healthcare practitioners, facilitating an understanding of the safety profiles of these influenza treatments in real-world scenarios.

An Annual Symposium on Disparities in Milwaukee, WI, with a 2023 Focus on Older Adults with Cancer.

PURPOSE OF REVIEW: Cancer-related inequities are prevalent in Wisconsin, with lower survival rates for breast, colorectal, and lung cancer patients from marginalized communities. This manuscript describes the ongoing efforts at the Medical College of Wisconsin and potential pathways of community engagement to promote education and awareness in reducing inequities in cancer care. RECENT FINDINGS: While some cancer inequities are related to aggressive disease biology, health-related social risks may be addressed through community-academic partnerships via an open dialogue between the community members and academic faculty. To develop potential pathways of community-academic partnerships, an annual Cancer Disparities Symposium concept evolved as a pragmatic and sustainable model in an interactive learning environment. In this manuscript, we describe the programmatic development and execution of the annual Cancer Disparities Symposium, followed by highlights from this year's meeting focused on geriatric oncology as discussed by the speakers.

Identification of drug-side effect association via correntropy-loss based matrix factorization with neural tangent kernel.

Adverse drug reactions include side effects, allergic reactions, and secondary infections. Severe adverse reactions can cause cancer, deformity, or mutation. The monitoring of drug side effects is an important support for post marketing safety supervision of drugs, and an important basis for revising drug instructions. Its purpose is to timely detect and control drug safety risks. Traditional methods are time-consuming. To accelerate the discovery of side effects, we propose a machine learning based method, called correntropy-loss based matrix factorization with neural tangent kernel (CLMF-NTK), to solve the prediction of drug side effects. Our method and other computational methods are tested on three benchmark datasets, and the results show that our method achieves the best predictive performance.

A longitudinal evaluation of oxidative stress - mitochondrial dysfunction - ferroptosis genes in anthracycline-induced cardiotoxicity.

BACKGROUND: Antineoplastic medications, including doxorubicin, idarubicin, and epirubicin, have been found to adversely affect the heart due to oxidative stress - mitochondrial dysfunction - ferroptosis (ORMFs), which act as contributing attributes to anthracycline-induced cardiotoxicity. To better understand this phenomenon, the time-resolved measurements of ORMFS genes were analyzed in this study. METHODS: The effect of three anthracycline drugs on ORMFs genes was studied using a human 3D cardiac microtissue cell model. Transcriptome data was collected over 14 days at two doses (therapeutic and toxic). WGCNA identified key module-related genes, and functional enrichment analysis investigated the biological processes quantified by ssGSEA, such as immune cell infiltration and angiogenesis. Biopsies were collected from heart failure patients and control subjects. GSE59672 and GSE2965 were collected for validation. Molecular docking was used to identify anthracyclines's interaction with key genes. RESULTS: The ORMFs genes were screened in vivo or in vitro. Using WGCNA, six co-expressed gene modules were grouped, with MEblue emerging as the most significant module. Eight key genes intersecting the blue module with the dynamic response genes were obtained: CD36, CDH5, CHI3L1, HBA2, HSD11B1, OGN, RPL8, and VWF. Compared with control samples, all key genes except RPL8 were down-regulated in vitro ANT treatment settings, and their expression levels varied over time. According to functional analyses, the key module-related genes were engaged in angiogenesis and the immune system pathways. In all ANT-treated settings, ssGSEA demonstrated a significant down-regulation of angiogenesis score and immune cell activity, including Activated CD4 T cell, Immature B cell, Memory B cell, Natural killer cell, Type 1 T helper cell, and Type 2 T helper cell. Molecular docking revealed that RPL8 and CHI3L1 show significant binding affinity for anthracyclines. CONCLUSION: This study focuses on the dynamic characteristics of ORMFs genes in both human cardiac microtissues and cardiac biopsies from ANT-treated patients. It has been highlighted that ORMFs genes may contribute to immune infiltration and angiogenesis in cases of anthracycline-induced cardiotoxicity. A thorough understanding of these genes could potentially lead to improved diagnosis and treatment of the disease.

Polypharmacy in psychiatry and weight gain: longitudinal study of 832 patients hospitalized for depression or schizophrenia, along with data of 3180 students from Europe, the U.S., South America, and China.

Epidemiologic data indicate that overweight and obesity are on the rise worldwide. Psychiatric patients are particularly vulnerable in this respect as they have an increased prevalence of overweight and obesity, and often experience rapid, highly undesirable weight gain under psychotropic drug treatment. Current treatment strategies in psychiatry are oriented towards polypharmacy, so that the information on drug-induced weight gain from earlier monotherapy studies is of very limited validity. We have analyzed the longitudinal data of 832 inpatients with ICD-10 diagnoses of either F2 (schizophrenia; n = 282) or F3 (major depression; n = 550) with the goal of ranking treatment regimens in terms of weight gain, side effects, and response to treatment. The patient data were complemented by the data of 3180 students aged 18-22 years, with which we aimed to identify factors that enable the early detection and prevention of obesity and mental health problems. After 3 weeks of treatment, 47.7% of F2 patients and 54.9% of F3 patients showed a weight gain of 2 kg and more. Major predictive factors were "starting weight" (r = 0.115), "concurrent medications" (r = 0.176), and "increased appetite"(r = 0.275). Between 11 and 30% of the observed variance in weight gain could be explained by these factors, complemented by sex and age. The comparison between monotherapy (n = 409) and polypharmacy (n = 399) revealed significant drawbacks for polypharmacy: higher weight gain (p = 0.0005), more severe side effects (p = 0.0011), and lower response rates (F2: p = 0.0008); F3: p = 0.0101). The data of 3180 students made it clear that overweight and obesity often begin early in life among those affected, and are interconnected with personality traits, while increasing the risk of developing psychosomatic disturbances, mental health problems, or somatic illnesses. Although the available data did not readily lead to a comprehensive, clinically applicable model of unwanted weight gain, our results have nevertheless demonstrated that there are ways to successfully counteract such weight gain at early stages of treatment.