Protein interaction networks underlie countless biological mechanisms. However, most protein interaction predictions are based on biological evidence that are biased to well-known protein interaction or physical evidence that exhibits low accuracy for weak interactions and requires high computational power. In this study, a novel method has been suggested to predict protein interaction partners by investigating narrow funnel-like interaction energy distribution. In this study, it was demonstrated that various protein interactions including kinases and E3 ubiquitin ligases have narrow funnel-like interaction energy distribution. To analyze protein interaction distribution, modified scores of iRMS and TM-score are introduced. Then, using these scores, algorithm and deep learning model for prediction of protein interaction partner and substrate of kinase and E3 ubiquitin ligase were developed. The prediction accuracy was similar to or even better than that of yeast two-hybrid screening. Ultimately, this knowledge-free protein interaction prediction method will broaden our understanding of protein interaction networks.  •Proteins that interact show narrow funnel-like interaction energy distribution•Protein interaction prediction using energy distribution is accurate across species•This method can predict substrates of kinase and EUL•Deep learning model can predict protein interaction using energy distribution Artificial intelligence; Biochemistry; Biochemistry methods; Machine learning; Protein After the genome sequencing of several tens of thousands of organisms, understanding biomolecule networks beyond the individual genes is becoming increasingly important. In particular, protein-protein interactions (PPI) are among the most diverse biomolecular networks and the center of system biology. Several experimental techniques, such as yeast two-hybrid screening, coimmunoprecipitation, affinity-chromatography, tandem affinity purification-mass spectroscopy, etc., have been developed to identify PPI.In silico PPI prediction methods utilize biological, structural, and physical results. Biological evidence includes gene or domain fusion,,,,,,,,,,,in silico protein docking.,Arabidopsis encode kinases and EULs.,,,,,,,,,,,,,One of the most difficult PPIs to predict is that of enzyme-specific interaction partners, such as substrates, inhibitors, and regulators, or signaling pathway interactions. Their diversity of complicated signaling pathways,,In this study, I suggest a new, interaction energy distribution-based PPI prediction strategy. PPIs with a specific interaction partner, such as PPIs of kinase and EULs, exhibit the specificity to maintain sophisticated signaling pathways. For instance, in humans, kinase phosphorylates specific phosphorylation sites ranging from one to hundreds among approximately 700,000 potential phosphorylation sites.Although there are considerable differences of interaction energy distributions between interacting and non-interacting protein complexes, setting a classification criterion with high accuracy is difficult. Recent advances in deep learning have made it possible to extract hidden features from complicated data and classify them.,,To analyze the interaction energy landscape of kinases/EULs and their interaction partners, I retrieved 135 human kinase-interaction partners, including regulator, inhibitor, and substrate, and 189 human EUL-substrate pairs randomly choosen from known databases.Using Phyre2, a server for template-based structure modeling,To analyze the interaction energy landscape A, I calcAs every theoretical protein energy exhibits differences compared with the experimental data, I ignored energy change because of structure change and considered only the affinity to minimize error. To indicate interacting positions, I used interface root-mean-square deviation (iRMS).As To illustrate the structural meaning of the narrow funnel-like interaction energy distribution, For the quantitative analysis of the interaction energy distribution, I devised a strategy to distinguish narrow funnel-like interaction energy distribution A. DockinAs per my hypothesis, if a protein pair with specific interacting partners showed narrow funnel-like interaction energy distribution, specific partners could be predicted with interaction energy distribution analysis by taking specific criteria. Therefore, I calculated the difference between the true-positive (identified pair with narrow funnel-like interaction energy distribution) and false-positive (random pair with narrow funnel-like interaction energy distribution) ratios for each criterion. As per the hypothesis, the narrow funnel distribution of the functionally interacting protein complex was higher than that of random protein pairs in the case of most criteria.For whole protein pairs, docking structures generated by RosettaDock, HDOCKlite showed up to 11.9%–20.3% difference of true- and false-positives . InteracAs protein global docking works well for small proteins,As kinase and EUL PPIs represent only a small part of the whole PPIs, I investigated the interaction energy distribution in other protein complexes. I retrieved 183 curated experimentally determined protein complex structures randomly choosen from the IntAct database,t test, t test). These processes are well-known to display molecular cascades with specific PPIs. Among those, cellular process-, developmental process-, and response to stimuli-related proteins pairs showed higher differences (99% confidence using Student’s t test). In particular, developmental process-related proteins pairs showed the biggest differences of iRMSs between the interacting complexes and non-interacting pairs . This result might be related to developmental processes controlled by complex molecular cascades and networks with specific PPIs.To confirm whether other protein complexes showed narrow funnel-like interaction energy distribution, I calculated the iRMS averages of simulated docking structures. As per the hypothesis A, the avAs I have heretofore demonstrated, there are significant differences in the interaction energy distribution between interacting and non-interacting protein complexes. Thus, I developed deep learning models to predict protein interactions based on complex interaction energy distributions. As described in ,To develop the prediction model, protein interaction energy distributions of 183 experimentally determined interacting protein complex from the IntAct databaseTo apply this deep learning method to predict substrates of EULs and kinases, I retrieved EULs and kinases from various species along with information about their substrates from the Ubibrowser 2.0For substrate prediction of EULs, the optimal prediction model showed a 77.7% accuracy using an Adam optimizer containing a 1024-2-layer structure and 61 epochs. For substrate prediction of kinases, the optimal model for prediction showed a 61.4% of accuracy with an Adam optimizer containing a 1024-64-2-layer structure and 9 epochs  and S15.iRMS is a very useful indicator for analyzing protein interaction positions. However, as the positional information necessary for protein interaction analysis  is condensed into a single indicator, this approach has several limitations. Therefore, it is necessary to introduce various indicators. Especially, structural analysis using root-mean-square deviation, including iRMS, is sensitive to local error.Reciprocal TM-score (rTM-score) is the harmonic average of the TM-scores of the components of the protein complex. Therefore, TM-score of one protein cannot dominate rTM-score of protein complexMoreover, TM-score has shape dependence when used for protein interaction analysis. A slight change in interaction angle can lead to large structural changes depending on the shape. In the case of a long protein, when it interacts to a position similar to the template, it has a smaller TM-score than when a round protein interacts. Therefore, I introduced interface TM-score  in the ROC curve analysis  force field.,,,,,In this study, the development of EUL substrate prediction using deep learning enabled the proposal of new application. Innovative methods such as CRISPR/Cas9, siRNA, or miRNA, which inhibit DNA and RNA levels in the central dogma, have limitations as potential medicines for regulating the expression of a specific gene. Therefore, recently, studies on degrading undrugable targets at the protein level using small molecules called proteolysis targeting chimera (PROTAC) that help interaction to EUL have been actively conducted.,Another possible interaction energy distribution analysis application is that narrow funnel-like interaction energy distribution can be a specificity indicator. Finding specific interactions is important in drug discovery. Nonspecific interactions can result in side effects.Still, this study has several limitations. Although over 1,000 PPIs were analyzed in this study, this number is far less than the number of discovered PPIs. As demonstrated in this study, different types of protein interactions exhibit distinct patterns of interaction energy distribution. Therefore, limited protein interaction analysis leads to restricted data on the distribution patterns of interaction energy. Especially, Artificial Intelligence (AI) model for predicting PPIs trained on limited data can exhibit low accuracy for diverse proteins. Therefore, further research involving large-scale analysis utilizing effective protein docking program and high-performance computing device such as supercomputer will improve PPI prediction.,Moreover, indicators for PPI used in this study  require strongest interacting docking structure as a model structure. Therefore, these indicators may be not appropriate for analyzing PPIs with multi-binding sites or allosteric regulation. Furthermore, because of small dataset and curse of dimensionality,This study only presents an initial model that demonstrates the possibility of predicting protein interactions using the distribution of interaction energy. Deep learning model applied in this study is simple recurrent neural network. Diverse AI structure including convolutional neural networkrgdfs@sogang.ac.kr)Further information and requests for resources should be directed to and will be fulfilled by the lead contact, Juyoung Choi  -docking:partners A_B -dock_pert 3 8 -randomize1 -randomize2 -spin -out:path:all (Output directory)Next, I generated 1,000 docking structures using the following command../bin/docking_protocol.static.linuxgccrelease -in:file:s (prepacked files) -docking:partners A_B -dock_pert 3 8 -randomize1 -randomize2 -spin -use_ellipsoidal_randomization true -nstruct 1000 -out:path:all (output directory)To analyze interacting proteins complexes and non-interacting protein pairs, I optimized their side-chain conformations (prepacking) using the following command../bin/docking_protocol.static.linuxgccrelease -in:file:s (protein complex structure file) -docking:partners (chain ID of proteins which participating in interaction)_(chain ID of proteins in opposite side of interaction) -dock_pert 3 8 -randomize1 -randomize2 -spin -out:path:all (output directory)Then, I generated 1,000 docking structures using the following command../bin/docking_protocol.static.linuxgccrelease -in:file:s (protein complex structure file) -docking:partners (chain ID of proteins which participating in interaction)_(chain ID of proteins in opposite side of interaction) -dock_pert 3 8 -randomize1 -randomize2 -spin -use_ellipsoidal_randomization true -nstruct 1000 -out:path:all (output directory)To analyze kinase and E3 ubiquitin ligase interactions using HDOCKlite,./hdock (kinase or E3 ubiquitin ligase) (Interaction partner) -out (docking file)./createpl (docking file) top1000.pdb -nmax 1000 -complex -modelsAll commands were written in a Linux shell file and executed.For further analysis, kinase or E3 ubiquitin ligase chain IDs were designated as “A” and those of interacting partners as “B” using PyMOL.To calculate interaction energy, I calculated the Rosetta energy of each protein or complex using the following command:./bin/score_jd2.linuxgccrelease -in:file:s (protein or complex) -out:file:scorefile (score file)Then, I calculated the interaction energy using the following equation:Then, iRMS were calculated using DockQ./DockQ.py (Docking structure) (Strongest interacting docking structure) -short ≫>>(iRMS file)For interacting protein complexes and non-interacting protein pairs, iRMS were calculated using the following command:./DockQ.py (Docking structure) (Strongest interacting docking structure) -short -native_chain1 (chain ID of proteins which participating in interaction) -model_chain1 (chain ID of proteins which participating in interaction) -native_chain2 (chain ID of proteins in opposite side of interaction) -model_chain2 (chain ID of proteins in opposite side of interaction) -perm1 -perm2 >>(iRMS file)All commands were written in a Linux shell file and executed.,Moreover, in this study, rTM-score, iTM-score and riTM-score are applied to interaction energy distribution analysis.  To calcuInterface in iTM-score and riTM-score is defined as every residue pairs from two proteins within 10 Å of each other. Interface are selected using interface selection module to calculate iRMS in DockQ program.The bioinformatics analysis was performed as described previously,To construct training dataset for general PPI prediction using deep learning, iRMS and interaction energy of 183 protein complex structures, retrieved from IntAct databasehttps://github.com/cjy318/protein_interaction).Deep learning models were designed as a simple recurrent neural network model with one block using Keras library.


To examine the association between self-managed abortion and the self-reported experience of abortion complications in India, a country with a high incidence of self-managed abortion.The study used a cross-sectional multivariable logistic regression analysis of data from the National Family Health Survey (NFHS-4) of 2015–2016 to compare the odds of self-reported complications experienced during abortion between self-managed and clinician-managed abortions in India.On average, self-managed abortions occurred earlier in gestation than clinician-managed abortions, 7.8 weeks and 11.3 weeks, respectively (p < 0.001). Self-managed abortion was associated with fewer self-reported abortion-related complications than clinician-managed abortions when adjusted for covariates not including gestational age (Adjusted Odds Ratio (aOR) 0.82, 95% confidence interval (CI) 0.69, 0.97). However, once adjusted for gestational age, there was no longer a clinically meaningful or statistically significant difference in the odds of self-reported complications between self-managed and clinician-managed abortions .These findings suggest that people in India are using safe methods to self-manage abortions and support the hypothesis that self-managed abortion can improve access to abortion and reproductive choice without increasing risk. Abortion has been legal in India since 1971.6Provided that people access accurate information and correct medications and dosages for self-use medication abortion, self-managed abortion can be as safe as clinician-managed abortion.9Though self-managed abortion is common in India, it is unknown how often people experience complications with self-managed abortion compared to clinician-managed abortion. Assuming that accurate information and correctly dosed high-quality abortion medications are not widely accessible throughout India without a prescription, we hypothesized that self-managed abortion would have higher complication rates than clinician-managed abortion.This is a cross-sectional analysis of the association between self-managed abortion versus clinician-managed abortion and self-reported abortion-related complications among a nationally representative cohort of reproductive aged women (15–49 years old) in India. Data was collected from the National Family Health Survey (NFHS-4) of 2015–16. The NFHS-4 version was chosen for analysis because previous versions of this survey addressed other women’s health topics but did not explicitly elicit information about who performed an abortion or abortion complications; subsequent enumerations were not publicly available at the time of the analysis. Of the 699,686 female respondents, 8251 women reported an abortion in the five previous years. The survey was administered by the International Institute of Population Sciences (IIPS); data were collected by female interviewers in the preferred language of the participant.We defined self-managed abortion (SMA) as a self-reported abortion performed by or supervised by the pregnant person, a friend, a family member, or Dai . We defined clinician-managed abortion (CMA) as a self-reported abortion performed by or supervised by a licensed clinician . The type of abortion (self-managed vs clinician-managed) and the reported abortion location  were compared to validate these definitions. The locations were grouped into clinic-type locations  and other locations (at home or other/elsewhere). We defined a self-reported abortion-related complication as a “yes” answer to the question “Did you have any complication from the abortion?” We included the following covariates in the analysis: age of the pregnant person, parity, having a living son, caste (upper vs lower), completed education, wealth quintile, residence , and gestational age of the pregnancy. Gestational age at the time of abortion was asked in months; these data were subsequently converted to weeks through multiplication by a factor of 4.34 (average number of weeks in a month) for ease of results interpretation. All covariates were chosen a priori as potential confounders of the association between self-managed abortion and self-reported abortion-related complications and were identified using a directed acyclic graph.10Bivariate comparisons were tested using chi-squared tests. Logistic regression models were used to assess the relationship between self-managed abortion and self-reported abortion-related complications, including unadjusted bivariable regressions, and two multivariable models. The first multivariable model included age, parity, having a living son, belonging to a “scheduled caste, scheduled tribe or other backward caste” , education, wealth quintileAnalyses were conducted using STATA (version 15.1).All participants provided verbal informed consent. Ethical approval for data collection was provided by the Institutional Review Board of International Institute of Population Sciences and ICF. Ethical approval for analysis of the de-identified dataset was approved by the Institutional Review Board of the University of California, San Diego.Of the 699,686 female respondents, 8251 women reported an abortion in the five previous years (1.2%). Of the 8251 pregnancies ending in abortion, 2418 (28%) were reported as self-managed, and 2111 (87%) of self-managed abortions were reported to have occurred outside of clinics (data not shown in tables). Of the 5833 clinician-managed abortion, 5709 (98%) were reported to have occurred in clinics (data not shown in tables). There were no significant differences in demographic characteristics between those having self-managed abortion and those having clinician-managed abortions, other than gestational age and level of completed education . AbortioSelf-managed abortions occurred on average at a significantly earlier gestational age for SMA than for CMA  (data not shown in tables).After adjusting for age, parity, having a living son, caste, education, wealth quintile and residence, self-managed abortion was associated with fewer self-reported abortion-related complications than clinician-managed abortions (Adjusted Odds Ratio (aOR) 0.82, 95% confidence interval (CI) 0.69, 0.97) . There wWe found that approximately one in four abortions in India are self-managed. Our findings suggest that on average, people in India access self-managed abortion at an earlier gestational age than clinician-managed abortion. Globally, people access self-managed abortion for various reasons – some do so by choice and others by necessity.Contrary to our hypothesis, this study found that self-managed abortion in India is associated with lower odds of self-reported abortion-related complications. While this is not in line with some existing state-level research,14We found that self-managed abortions occur on average, at an earlier gestation. Increasing gestational age is known to be a risk factor for abortion complications.Self-managed abortion with medication abortion can be a safe method of accessing abortion care when correctly dosed, affordable, and using quality medications with accurate, accessible administration information.Strengths of our study include a nationally representative dataset which offers an opportunity to assess for self-managed abortion and compare self-reported outcomes between self-managed abortion and clinician-managed abortion. In addition, our dataset is limited to recent abortions (experienced from 2010 to 2016) reflecting more contemporary availability of medication abortion.Our findings should be considered in light of several study limitations. The percent of women reporting an abortion in the last 5 years (1.2%) was lower than other studies in India have estimated (4% per year).While the self-reported complication rates were similar between groups, we found higher rates of complications in both groups than studies which ascertain clinical outcomes and cite an adverse event rate ranging from 1.3% to 8.1%.We found that self-managed abortion and clinician-managed abortion had comparable rates of self-reported complications after adjusting for gestational age, suggesting that people are accessing accurate information, medication dosage and quality medications to self-manage abortions.


As common gynecological oncology, ovarian cancer has a high fatality rate and poor overall survival, mainly because of nonspecific symptoms in the early stages and chemotherapy resistance. Exosomes, nano-sized vesicles secreted by almost all types of cells, carry valuable commodities such as proteins, lipids, enzymes, mRNAs, and miRNAs between cells. They take part in remodeling the tumor microenvironment, promoting tumor angiogenesis and metastasis, and regulating immune metastasis and chemotherapy resistance in ovarian cancer. Previous studies have reported that exosomes could transfer chemotherapy resistance from drug-resistant tumor cells to sensitive ones by delivering proteins and miRNAs. Also, exosomes are involved in chemotherapy resistance by transferring multidrug-resistance-related transporters, decreasing apoptosis, promoting epithelial-to-mesenchymal transition, and changing signal transduction pathways. Furthermore, they play a significant role in early detection, chemotherapy efficacy evaluation, and treatment of ovarian cancer. Exosomes are applied as chemotherapeutic delivery vehicles and therapeutic targets to inhibit anti-tumor immune responses. In addition, exosomes can be developed for cancer immunotherapy because of their immunomodulatory potential. Therefore, the article reviews the latest research progress of exosomes in ovarian cancer to elaborate on the mechanisms of exosome-mediated chemotherapy resistance in ovarian cancer patients and provide a forecast on their clinical therapeutic potential in improving chemotherapy sensitivity. Ovarian cancer is the most lethal gynecologic malignancy among female patients and the eighth cause of cancer-related deaths in women worldwide. There were approximately 19,710 estimated new cases and 13,270 estimated deaths of ovarian cancer, accounting for 5% of all cancer deaths in the United States in 2023Early endosomes are formed by the fusion of primary endocytic vesicles. Then Multivesicular bodies (MVBs) information begins by inward membrane budding, leading to cargo sequestration and distribution into vesicles. MVBs, also called late endosomes to escape degrading lysosomes in the presence of a high concentration of ceramide lipid family, fuse with the plasma membrane through soluble N-ethylmaleimide (NEM)-sensitive factor attachment protein receptor (SNARE) complex and release their contents called exosomes from the intraluminal vesicles (ILVs) into the extracellular space. In addition, Rab27A and Rab27B are the essential mediators to lead the MVBs toward the cell peripheryExosomes greatly influence a few pathways, such as tumor microenvironment remodeling, signal transduction, tumor metastasis and invasion, immune regulation , and chemotherapy resistance, by releasing bioactive moleculesMeanwhile, tumor-derived exosomes modulated the tumor microenvironment which consisted of tumor cells, stromal cells, mesenchymal cells, infiltrating immune cells, and cytokines by stimulating extracellular receptor signaling. Ovarian cancer-derived exosomes induced fibroblasts to cancer-associated fibroblasts (CAFs) and promoted the production of transforming growth factor 1 (TGFβ1) and activation of somatic mutations in the Mothers Against Decapentaplegic Homolog (SMAD) signaling. In the hypoxic microenvironment, it suggested that tumor cells secreted more exosomes with enhanced angiogenic and metastatic potential to promote tumor microenvironment alteration and tumor progressionExosomes released by tumor cells carried vascular endothelial growth factor (VEGF), transforming growth factor β (TGF-β), and tumor necrosis factor α (TNF-α) to induce vascular formation which is important for tumor growth and metastasis. Growing evidence on exosomes' function in angiogenesis among different types of tumors indicated that tumor-derived exosomes were essential tools for promoting angiogenesis. Epithelial ovarian cancer cells derived exosomes could transfer metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) to recipient HUVECs and stimulate angiogenesis-related gene expression in recipient human umbilical vein endothelial cells (HUVECs), eventually promoting angiogenesisExosomes derived from immune cells play an important role in regulating immune response. For example, exosomes derived from antigen-presenting cells (APCs) are enriched in antigen-presenting molecules (MHC-I and MHC-II) on their surface and present antigens to T cells and activate the immune responseThe most common ovarian tumor is epithelial carcinoma, classified into seven main histological subtypes: high-grade and low-grade serous, mucinous, endometrioid, clear cell, Brenner, seromucous, and undifferentiated carcinomas, according to the World Health Organization. One of the epithelial carcinomas is high-grade serous ovarian carcinoma accounting for 70%, of which the majority derives from the fallopian tube epithelium and leads to the most significant ovarian cancer deaths. About 40-50% of high-grade serous ovarian cancer shows homologous recombination (HR) deficiency caused by germline or somatic mutation of breast cancer susceptibility gene (BRCA1/2) and other DNA repair genes or epigenetic silencing through methylation. Moreover, it shows high chromosome instability and ubiquitous TP53 mutationsThe response rate to initial first-line chemotherapy drugs for patients diagnosed with high-grade serous ovarian cancer (HGSOC) is greater than 80%, but the 5-year survival rate for them is between 35% and 40%Although platinum-based chemotherapy is currently considered the primary therapy for high-grade serous ovarian cancer patients, chemotherapy resistance remains significant obstruction in treating ovarian cancer patients. In addition, the issue of PARPi resistance has emerged in clinical cancers, and the mechanisms of PARPi-acquired resistance remain to be investigated. Therefore, it is essential to elucidate the molecular mechanisms in chemotherapeutic resistance and design novel and effective therapeutic agents targeting resistant cancer cells to improve survival in relapsed HGSOC patients.However, molecular mechanisms associated with platinum-intrinsic resistance still need to be fully understood. CCNE1(Cyclin E) amplification which encodes cyclin E1, not only provides an oncogenetic stimulus through activation of the cell cycle to promote the occurrence of malignant tumors; but increases platinum resistance and recurrence rate because of chromosome instability, ultimately leading to poor prognosis of HGSOC patientsAlmost 75% of patients sensitive to initial chemotherapeutic compounds relapse within two years because of acquired resistance. Ovarian cancer is a disease characterized by extensive structural genomic variation. Patch et al. used whole-genome sequencing of tumor and germline DNA samples from 92 patients with primary refractory, resistant, sensitive, and matched acquired resistant disease. They revealed that gene breakage commonly inactivated the tumor suppressor genes RB1, NF1, RAD51B, and PTEN in HGSOC and substantially contributed to acquired chemotherapy resistance. As known, chemotherapy resistance is a complicated phenomenon consisting of many mechanisms, including drug inactivation, drug target alteration, upregulation of drugs efflux bumps, DNA damage repair mechanisms, apoptosis inhibition, EMT, epigenetic effects like miRNAs, modifications of the tumor microenvironment, cancer stem cells, or any combination of these mechanisms Ovarian cancer is a peritoneal disease where tumor tissues spread directly to surrounding organs and implant on the surface of the omentum and abdominal organs. The omentum is a highly vascularized connective tissue with many cells like adipocytes, fibroblasts, lymphocytes, and macrophages. Au Yeung suggested that miR21-containing exosomes released by cancer-associated adipocytes (CAAs) or cancer-associated fibroblasts (CAFs) within the omental microenvironment significantly promoted the acquisition of a platinum-resistant phenotype in ovarian cancerMany studies illustrate that ovarian cancer cells expressing stemness markers cause tumor chemotherapy resistance and promote tumor development in ovarian cancer patients. Cancer stem cells are defined as a subpopulation of cancer cells with the potential for self-renewal, infinite proliferation, and multiple differentials. The following surface and intracellular markers have been discovered in ovarian cancer stem cells: aldehyde-dehydrogenase (ALDH), CD133, CD44, CD177, CD24, and EpCAMChemotherapy-resistant marker like ALDH1 exists on the surface of cancer stem cells and plays important roles in drug resistance. They found that the overexpression of ALDH1 was observed in paclitaxel- and topotecan-resistant cell lines, indicating that ALDH1-positive cancer cells were related to drug resistance and tumor development in ovarian cancerThe ability of ovarian cancer cells to repair platinum-induced DNA damage can decide the efficiency of chemotherapeutic drugs. The nucleotide excision repair (NER) pathway recognizes DNA damage caused by platinum compounds, and then Excision Repair Cross-Complementing 1(ERCC1) protein, NER protein complex, repairs the lesionRestoring the homologous recombination repair is another important mechanism. The BRCA1/2 gene is an important section in the HR pathway; germline or somatic mutations in BRCA genes result in genomic instability and ovarian cancer development. Many studies have shown that ovarian cancer with BRCA gene mutations is more sensitive to platinum and PARP inhibitors than BRCA wild-the type tumors. Furthermore, they observed several molecular events associated with acquired resistance in recurrent samples, including secondary mutations in BRCA1/2 that restored the open reading frame, which leads to functional homologous recombination repair, loss of BRCA1 promoter somatic methylation, an molecular subtype alteration, promoter fusion, and translocation involving the 5' end of ABCB1Non-homologous end joining (NHEJ) is the second important DNA double-strand break (DSB) repair pathway. 53BP1(TP53BP1) promotes DSB repair by NEHJ in G1phase cells by forming 53BP1-RIF1 complexes which protect DSB ends from exonuclease processing. Loss of 53BP1 results in HR reactivation in homologous recombination deficiency tumors, rendering tumor cells resistant to PARPiRecent studies have revealed other mechanisms to mediate PARPi resistance, including replication fork protection, increased drug efflux pumps, and alterations in the PARP and the PAR glycohydrolase (PARG) proteinsTumor-derived exosomes participate in numerous pathological processes such as cancer proliferation and metastasis, immune suppression, and chemotherapy resistance. Chemotherapy resistance is divided into intrinsic resistance due to genetic or phenotypic changes and extrinsic resistance interacting with the tumor microenvironment. Exosomes transfer resistant phenotypes to drug-sensitive cells and make them resistant to drugs by delivering ABC transporters, increasing anti-apoptotic signaling, mediating EMT, and increasing tumor-stroma interaction. Except for proteins contained in exosomes, non-coding RNAs such as lncRNAs and miRNAs are loaded in exosomes and alter protein expression in recipient cells to activate chemo-resistant pathways. So the identified molecular mechanisms of exosome-mediated chemo-resistance mainly include several aspects Fig. : 1) expoDecreasing cellular anti-cancer drugs' concentration is a major mechanism in therapy resistance in ovarian cancer. The efflux of drugs and their decomposed products via exosomes reduces effective drugs' concentration and promotes drug resistance in tumor-donor cells. The hydrophobic character of drugs interacts with lipids with exosome membranes, so chemotherapeutic drugs are secreted into exosomes, contributing to drug resistance. So it is crucial to explore in-depth mechanisms of exosome-mediated cisplatin efflux and seek novel clinical therapies in chemo-resistant ovarian cancer. Safaei et al. found that the amount of cisplatin in exosomes released from cisplatin-resistant ovarian cancer cell line 2008/C13*5.25 was 2.6 times higher than that released from cisplatin-sensitive cellsO-GlcNAcylation, a critical protein post-translational modification, plays a vital role in biological processes. Previous studies have reported that O-GlcNAcylation transferase (OGT) downregulation increased cisplatin resistance in ovarian cancer cells. In this study, downregulation of OGT-mediated O-GlcNAcylation of SNAP-23 promoted the formation of SNAP-23-Stx4-VAMP8 complex and increased exosomes release in ovarian cancer, which increased cisplatin efflux through exosomes and reduced the intracellular cisplatin concentrationTransmembrane family proteins (TMEM) have been demonstrated to play a role in cancer development and chemo-resistance. TMEM205 and its co-localized protein CD1B are expressed higher in platinum-resistant ovarian cancer cell lines and patient samples than in platinum-sensitive groups. TMEM205 regulates chemo-resistant associated proteins like exosome-regulatory proteins Rab11 and contributes resistance via increased exosomal efflux of intracellular platinum. Furthermore, they found TMEM205 inhibitors, L-2663, reduced exosome secretion, inhibited exosomal platinum efflux, and re-sensitized to platinum in resistant cellsCleft lip and palate transmembrane protein 1-like (CLPTM1L)/cisplatin resistance-related protein 9 (CRR9) are cytoprotective oncofetal protein and chemo-resistance factors on the tumor cell surface. Many types of research have demonstrated that overexpression of CLPTM1L on the plasma membrane of ovarian cancer cells was associated with poor outcomes in serous ovarian adenocarcinoma. Except for the cell-autonomous effects of CLPTM1L on the resistance, they also found CLPTM1L from exosomes conferred chemo-resistance to bystander ovarian cancer cells in an ectodomain-dependent manner. Therefore, human anti-CLPTM1L mAbs could re-sensitize to platinum-resistant ovarian cancer cells achieved in orthotopic isografts and patient-derived cisplatin-resistant xenografts modelsExosomes are formed by the inward budding of the multivesicular body membrane, which is usually controlled by the endosomal sorting complex required for transport (ESCRT). Finally, they are released outside the donor cells and fused with the plasma membrane of the recipient cells. Drug-resistant cells release exosomes and transfer their cargoes like P-glycoprotein (P-gp) to drug-sensitive cells, which contributes to disseminating therapy resistance phenotype in vivo and in vitro.Levchenko et al. reported that P-gp transferred between tumor cells and tumor stroma reserved their functions and conferred drug resistance upon the recipient cells in 2004 for the first time. Furthermore, in solid metastatic tumors, they observed that the number of tumors expressing P-gp increased after chemotherapyAnnexin A3 is a part of the phospholipid binding proteins family, which rely on calcium channels and participate in membrane fusion, cell exocytosis, modulation of ion channels, and signaling transductionExcept for genetic factors, it has been proved that epigenetic changes like aberrant genomic DNA methylation and histone modifications are involved in tumor initiation, progress, recurrence, and drug resistance. DNA methyltransferase 1 (DNMT1) is essential to maintain genome‐wide methylation during DNA replication and damage repair. It has not been investigated that exosomes regulate epigenetic factors such as DNMT1 in ovarian cancer. Studies by Ercan et al. suggested that RGS10 was reduced via promoter hypermethylation mediated by HDAC1 or DNMT1 and supported that inhibition of HDAC1 or DNMT1 was viewed as an adjuvant therapeutic approach to overcome chemo-resistance in ovarian cancerApoptosis evasion is involved in tumor recurrence and chemotherapy resistance. Exosomes decrease pro-apoptotic signaling and increase anti-apoptotic signals to promote cell survival. Cisplatin dissociates GSN from the GSN-FLIP complex in chemo-sensitive cells, promoting GSN cleavage and caspase-3 activation. Werehene et al. reported for the first time the functions of exosomes containing plasma gelsolin (pGSN) (Ex-pGSN) on chemosensitivity in ovarian cancer. Ex-pGSN in chemo-resistant cells induces resistance in chemo-sensitive cells by targeting α5β1 integrin-FAK-Akt-HIF1α signaling pathway, upregulating pGSN, and inhibiting cisplatin-induced apoptosisEpithelial-to-Mesenchymal Transition is a complex phenomenon that involves tumor initiation and metastasis and increases resistance to chemotherapy and immunological therapy. Through EMT, epithelial cells in the tumor microenvironment lose their apical-basal polarity and become mesenchymal cells to promote cancer metastasis. In breast and ovarian cancer, invasiveness is an essential aggressive property. The first-line chemotherapy for HGSOC patients consists of platinum agents and paclitaxel. Several studies showed that tumor cells resistant to carboplatin and paclitaxel acquired a mesenchymal phenotype associated with EMT, which regarded EMT as the driver of chemotherapy resistance. In this review article, EMT-mediated mechanisms leading to therapy resistance in tumor cells include the following mechanisms: decreased cisplatin influx mediated by the copper transporter 1 (CRT1), an increased outflow of drugs, increased DNA damage repair, capacity by activating the PARP enzyme, inhibited p53-mediated cell apoptosis signals, changes in cell cycle, changes in different cellular pathways like TGFβ-SMAD, JAK/STAT. In another research, Bhattacharya et al. observed that CD44s, a mesenchymal spliced variant, was upregulated by TGF-β1-induced EMT. Furthermore, in ovarian cancer cells, overexpression of this mesenchymal isoform induced EMT, subsequently gaining stem-like characteristics and chemotherapy resistanceWhen platinum-sensitive cell A2780 was incubated with exosomes from platinum-resistant cells, it showed a two-fold increase in cell viability upon treatment groups treated with carboplatin compared to the control groups. Crow et al. discovered that previously unreported somatic mutations in the Mothers Against Decapentaplegic Homolog 4 (SMAD4) enhanced the chemo-resistance of epithelial ovarian cancer. SMAD4 is an essential part of TGF-β/SMAD signaling, and mutant SMAD4 ovarian cancer cells decrease PDCD4's expression, which regulates EMT. Also, they found that resistant A2780 cells, which exogenously expressed SMAD4 mutations, presented up-regulation of EMT markers and secreted exosomes, which increased by 1.7 fold in chemotherapeutic resistance compared to previously sensitive A2780 cells. Besides, it represented a novel mechanism that exosomes secreted from platinum-resistant ovarian cancer cells could continue the EMT phenotype and develop platinum-resistant cell subgroupsCancer-derived fibroblasts are essential components in the tumor environment that interact with tumor cells to mediate progression, metastasis, and chemotherapy resistance. Li et al. presented that exosomes derived from cancer-associated fibroblasts were co-cultured with ovarian cells SKOV-3 and CAOV-3 induced malignant tumor behaviors, including an increased potential of migration and invasion and promoting EMT through activating the SMAD signaling pathwayIncreasing evidence has demonstrated that tumor cells can acquire chemo-resistance undergoing many tumor types. Exosomes may induce the characteristics of EMT cell morphology to induce chemotherapy resistance, indicating that targeting EMT is a feasible method for reversing resistance.miRNAs are short, non-coding RNAs 22 nucleotides in length, involved in post-transcription genetic modification. Interestingly, miRNAs encapsulated in exosomes are protected by exosomes from the degradation of RNases compared with naked miRNAs. It is worth noting that cells exposed to chemotherapeutic agents selectively parcel miRNAs into exosomes and are associated with chemotherapy resistance. Exosomes have been found to transfer miRNAs from drug-resistant cells to drug-sensitive ones, contributing to oncogenesis, the pre-metastatic niche formation, tumor migration, tumor invasion, and ultimately drug resistance by regulating drug-sensitive cells' expression locally and systemically in the tumor environmentExosomes transfer miRNAs to sensitive tumor cells and induce chemotherapy resistance by inducing anti-apoptotic signals. Au Yeung et al. found that exosomes isolated from cancer-associated adipocytes and fibroblasts expressed a significantly higher level of miR-21 which could decrease apoptotic protease-activating factor 1 (APAF1) protein's expression in neighboring tumor cells and increase the chemo-resistance to paclitaxelFurthermore, exosomal miR-433 promoted resistance to paclitaxel by inducing cellular senescence and inhibiting the proliferation of neighboring cellsThe abnormal tumor angiogenesis affects the delivery and sensitivity of chemotherapy drugs in tumor cells. So inhibiting tumor angiogenesis and inducing normalization decreases the vessel wall's permeability, thus increasing the accumulation of chemotherapeutics and improving drug sensitivity. Targeted delivery of miR-484 via RGD-modified exosomes inhibits tumor angiogenesis in ovarian cancer, which enhances the chemotherapy sensitivity and prolongs the survival time of tumor-bearing mice in the OC xenograft model after chemotherapyDDP-Exos, exosomes derived from SKOV3/cisplatin (SKOV3/DDP) cells, were used to treat SKOV3 cells in vitro. The level of pyruvate dehydrogenase E1 subunit alpha 1 (PDHA1) in DDP-resistant SKOV3 cells was decreased significantly while miR-21-5p increased compared with control groups. They found that SKOV3/DDP-resistant-Exos treatment increased glycolysis, improved cell survival, and inhibited chemosensitivity of SKOV3 cells by upregulating PDHA1 exosomal miR-21-5p derived from DDP-resistant SKOV3 cellsExosomal miR-429 secreted by multidrug-resistant SKOV3 cells displayed higher expression than sensitive A2780 cells. miR-429 dysregulation increased cell viability and chemo-resistance among lots of cancers. Exosomal miR-429 induced DDP resistance via targeting the calcium-sensing receptor (CASR)/STAT3 pathway in A2780 cells and xenograft mice tumors. So targeting exosomal miR-429 might be a novel therapeutic target in epithelial ovarian cancerZhu and colleagues also revealed that exosomal miR-223 derived from hypoxic macrophages could enhance drug resistance in epithelial ovarian cancer cells via the PTEN-PI3K/AKT pathways in vitro and in vivo. Moreover, exosomal miR-223 could be used as a biomarker for predicting response to chemotherapy and might be considered a possible drug target to overcome chemoresistance in advanced epithelial ovarian cancer patientsThe PI3K/AKT/ mTOR signaling pathway plays a prominent role in tumor progression, inhibiting apoptosis and resistance to chemotherapyThe most useful detection method in ovarian cancer patients is the concentration of serum carbohydr5ate antigen 125 (CA125) and transvaginal ultrasound (TVS). However, it had limitations at the early stage of ovarian cancer, so developing higher specific and sensitive biomarkers is attractive to improve the diagnostic rate of ovarian cancer patients. In recent years, liquid biopsy has become a promising new area of early detection of ovarian cancer, which has the advantage of being less invasive, easier to obtain, and easier for serial measurements in the period of treatment. It includes the collection and analysis of circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), circulating cell-free miRNAs (cfmiRNAs), and circulating exosomesRecent studies have demonstrated that exosome miRNAs have the potential to serve as biomarkers for the diagnosis and prognosis of OC. The expression levels of 5 miRNAs  in plasma exosomes of patients with OC significantly elevated, which indicated that the expression levels of exosomal miRNAs might be considered as noninvasive biomarkers for the diagnosis of OC patients Except for miRNAs, exosomal proteins might also be potential biomarkers for the diagnosis of OC. It is also demonstrated that the serum levels of HSP27 were significantly increased in patients with epithelial ovarian cancer, which was secreted by exosomes. Therefore, serum HSP27 may be used as a potential biomarker of response to treatment in epithelial ovarian cancer patientsIn recent years, much evidence has displayed that exosomes have been used as therapeutic targets for cancer. Exosomes released from a cell line are heterogeneous, meaning treatment is efficient for one type of ovarian cancer patient but not for another. Therefore, targeted and personalized treatments have obtained more and more attention in current searches. Exosomes are ideal therapeutic tools for advancing ovarian cancer patients. Many strategies exist to reverse the chemotherapy resistance by exosomes in ovarian cancer, including utilizing exosomes as delivery vehicles which can increase drugs' concentration and block exosome secretion from ovarian cancer cells. Furthermore, exosomes play an essential role in anti-tumor immune responses, and they are specific biomarkers to predict the efficiency of immune responses. Nowadays, there exist three treatments based on exosomes Fig. : 1. drugMDR1 to overcome P-gp-mediated drug effluxA suitable drug delivery vehicle has been in great demand for cancer treatment because it improves the therapeutic efficacy and safety profile of the chemotherapeutic drugs and their metabolites. Exosomes are used as drug-delivered vehicles because of their high stability in circulation, high permeability, and low immunogenicity. They deliver drugs and their metabolizes across the biological membrane into the cytoplasm of recipient cells without the endosomal and lysosomal pathways. Nanoscale drug delivery systems incubate or electroporate chemotherapeutic drugs and nucleic acids into exosomes presenting more opportunities for unsolved clinical issues like chemotherapeutic resistanceAlthough many studies have demonstrated that exosomes assist cancer treatment, studying exosome application and nanotechnology development would require more clinical research. A phase I trial study (NCT03608631) studied the best dose and side effects of mesenchymal stromal cells-derived exosomes with KrasG12D siRNA in treating metastatic pancreatic cancer patients with KrasG12D mutation, aiming to treat pancreatic cancer by exosomes. In addition, a Phase II clinical trial (NCT01854866) recruited thirty malignant ascites or pleural effusion patients and they were injected with tumor cell-derived microparticles packaging chemotherapeutic drugs. It demonstrated that tumor cell-derived microparticles might be useful for treating malignant ascites and pleural effusion. However, there remains to be an urgent problem dealing with the isolation, characterization, storage, and administration of exosomes, to achieve mass production of clinical therapeutic exosomes. Furthermore, it remains to be answered which cell type to utilize for the isolation and purification of exosomes.Considering the importance of exosomes in chemotherapy resistance, it has a clinical implication for prospective tumor therapies to remove tumor-derived exosomes through targeting exosome biogenesis and secretion. The levels of exosomes are down-regulated by using RNAi and other small molecular inhibitors in exosome biogenesis and secretion. Under hypoxic conditions, STAT3 knockdown changed the expression of Rab27a and Rab7 to reduce exosome release. Combined inhibitor Amiloride, a STAT3 inhibitor with cisplatin, blocked the release of exosomes and significantly inhibited colony formation numbers and cell proliferation, demonstrating that STAT3 inhibitor increased chemosensitivity to cisplatinIn recent years, several interventions, including immune checkpoint blockade, cancer vaccines, and adoptive cell therapy, have attracted interest among solid tumors like melanoma, NSCLC, and classical Hodgkin lymphoma. However, no approved immune treatments for ovarian cancerNotably, exosomes could be used as carriers for tumor immunotherapy because of their immunogenicity and transfer function. Also, they were involved in antigen presentation, activation of T cells, and trigger of CD8+T cell-dependent anti-tumor responses in vitro and in vivo. It's thought that PD-L1 generally interacts with PD-1 on the surface of tumor-infiltrating lymphocytes, resulting inhibition of T-cell signaling. In addition, PD-L1 also exists on the surface of exosomes. Exosomal PD-L1 suppresses CD8+ T cell function, including proliferation, cytotoxicity, and cytokine production in vitro and in vivo. So it represents an expected therapeutic method that genetically blocks exosome biogenesis. The release of exosomal PD-L1 can promote T cell activation and proliferation and overcome resistance to the current antibody approachRecent research has shown that tumor-derived exosomes increase anti-tumor immune responses by inducing immune effector cells and suppressing the immune suppressor cells' response. Exosomes derived from natural killer cells(NK-Exo) could enhance ovarian cancer cells' killing effect in vitro by increasing DDP uptake and reversing the immunosuppression capacity of NK cellsIn addition, exosomes could be used as cell-free vaccines in cancer immunotherapy because they contain numerous tumor antigens. Cho et al. showed that exosomes derived from tumor cells were novel vesicles for delivering tumor-derived antigens to induce effective immune responses against tumors because of their immunogenicityTherefore, how exosomes could deliver immunotherapy drugs and antigens to stimulate anti-tumor immune responses in the tumor microenvironment is vital regarding cancer treatment. Although exosomes have attracted more and more attention as cancer vaccines and carriers for various cancers, there have been no animal and clinical trials to verify their efficacy in ovarian cancer.Ovarian cancer threatens female gynecological health due to a high mortality rate. In recent years, new ideas about eliminating specific exosomes, encapsulating chemotherapeutic drugs in exosomes to protect against degradation, and inhibiting exosome biogenesis and secretion, have been proven to slow down tumor progression and improve chemotherapeutic sensitization. Current studies suggested that exosomes derived from tumor cells and peripheral body fluid were expected to use as predictable biomarkers for early diagnosis of ovarian cancer and treatment targets to overcome chemo-resistance. With their high stability in circulation and accessibility to obtain from the body fluid, detecting exosomes in the peripheral blood via liquid biopsies might be a promising method. However, the immaturity of the purification and detection methods hinders their translation into clinical application. We propose that larger multi-institutional studies including more pre-clinical and clinical research, standardization, and best strategies for exosome isolation and application remain implemented to move this field forward for the benefit of patients in clinical.Exosomes could transfer multiple biological molecules to the recipient cells or the cancer microenvironment, so they might be served as a drug delivery system to overcome drug resistance. In addition, engineered exosomes achieve cell-specificity and tumor-specificity by modifying the surface molecules on exosomes. In the future, more studies are urged to investigate the utility of exosomes as natural delivery vehicles to guarantee the safety and accuracy of ovarian cancer patients' treatment at lower drug doses and fewer side effects.In conclusion, although many studies on exosomes have confirmed their great diagnostic and therapeutic potential in improving the prognosis of ovarian cancer, challenges in this field need to be overcome before providing exosomes as biomarkers and treatment targets for ovarian cancer patients.


A special problem in the immune therapy of AML is few neo-antigens compared with solid cancers because of a relatively low mutation frequency. Studies of CAR-T-, CAR-NK-adaptor CAR-T- and allogeneic NK-cells are progressing as are approaches using synthetic biology. Presently, there are no convincing data of efficacy of immune therapy in AML.Although safe and effective immune therapies have been developed in several cancers, this has not been so in acute myeloid leukaemia (AML). Studies of antibodies to CD33, CD123 and CLL-1 report with unconvincing efficacy and substantial adverse events. Lacking AML-specific target antigens, these approaches using non-specific antigen targets often cause unacceptable bone marrow toxicity and off-target adverse events. Studies of AML incidence in persons with immune deficiency indicate little if any immune surveillance against AML. In contrast, data studies of recipients of haematopoietic cell transplants support an effective allogeneic anti-AML effect associated with graft- Corynebacterium parvum. Some subjects received allogeneic or autologous leukaemia cells. We concluded immune therapy was ineffective in AML.Forty years ago, we reviewed the role of immune therapy in AML . We inteIn the past 10 years, immune therapy has been developed in B-cell and solid cancers •, 3, 4•.Lymphoid cancers and AML are different in several aspects. For example, only about 10 million lymphoid cells are produced daily compared with about 600 billion myeloid cells. Also, granulocytes and platelets survive only a few hours or days compared with lymphoid cells which live years. The consequence is a disruption of myelopoiesis is much more dangerous than a disruption of lymphopoiesis. Second, this is the different targetability of lymphoid compared with myeloid antigens. The target of immune therapy of lymphoid cancers is B-lineage antigens which are not cancer specific. Killing substantial numbers of normal B-cells is compatible with life, whereas this is not so for killing substantial numbers of myeloid cells. Consequently, immune therapy of AML is limited by the absence of AML-specific target antigens and potential damage to normal bone marrow function.Risks of lymphomas, melanoma and kidney cancer are substantially increased in lymphoma risk in persons with immune deficiency or iatrogenic suppression. In contrast, there is only a small increased risk of AML in these settings suggesting immune surveillance does not operate in AML Fig.  5].Fig..Fig.5].Fversus-host disease (GvHD) outcomes  leukemia-specific antigens. Whether this allogeneic graft-versus-leukemia effect (GvL) is distinct from GvHD is controversial . Aft. Aftversoversial .Fig. 2PrSeveral variables correlate with success of immune therapy including antigenicity, immunogenicity, accessibility, sensitivity to killing and collateral damage to normal cells. In AML, we are faced with the lack of a AML-specific target antigen(s). In solid cancers, efficacy checkpoint inhibitor antibodies correlate with mutation frequency Fig.  8]. How. How8]. Several immune therapies have been studied in AML including monoclonal antibodies, drug-, -toxin and radionuclide antibody conjugates, bi-specific antibodies and others Table  11, 12], 1211, 1Studies of cell therapies in AML include NK- and CAR-T- and CAR-NK-cells and cytokine-induced NK-cells (CIK) •, 14. ThCompared with immune therapy of B-cell lymphomas and several solid cancers, there has been little progress in immune therapy of AML outside the context of an allogeneic haematopoietic cell transplant. The predominant challenges are the lack of an AML-specific target antigen(s) and presently unavoidable collateral damage to normal bone marrow function. Whether these challenges can be overcome is unknown.


Cohort studies that quantify volumetric brain data among individuals with different levels of COVID-19 severity are presently limited. It is still uncertain whether there exists a potential correlation between disease severity and the effects of COVID-19 on brain integrity. Our objective was to assess the potential impact of COVID-19 on measured brain volume in patients with asymptomatic/mild and severe disease after recovery from infection, compared with healthy controls, using artificial intelligence (AI)-based MRI volumetry. A total of 155 participants were prospectively enrolled in this IRB-approved analysis of three cohorts with a mild course of COVID-19 , a severe hospitalised course , and healthy controls  all undergoing a standardised MRI protocol of the brain. Automated AI-based determination of various brain volumes in mL and calculation of normalised percentiles of brain volume was performed with mdbrain software, using a 3D T1-weighted magnetisation-prepared rapid gradient echo (MPRAGE) sequence. The automatically measured brain volumes and percentiles were analysed for differences between groups. The estimated influence of COVID-19 and demographic/clinical variables on brain volume was determined using multivariate analysis. There were statistically significant differences in measured brain volumes and percentiles of various brain regions among groups, even after the exclusion of patients undergoing intensive care, with significant volume reductions in COVID-19 patients, which increased with disease severity (SEV > MILD > CTL) and mainly affected the supratentorial grey matter, frontal and parietal lobes, and right thalamus. Severe COVID-19 infection, in addition to established demographic parameters such as age and sex, was a significant predictor of brain volume loss upon multivariate analysis. In conclusion, neocortical brain degeneration was detected in patients who had recovered from SARS-CoV-2 infection compared to healthy controls, worsening with greater initial COVID-19 severity and mainly affecting the fronto-parietal brain and right thalamus, regardless of ICU treatment. This suggests a direct link between COVID-19 infection and subsequent brain atrophy, which may have major implications for clinical management and future cognitive rehabilitation strategies. There is mounting evidence of brain-related pathology due to COVID-19, both during the acute phase of the disease and in the longer-term course , individIt has been postulated that patients severely ill with COVID-19 may also experience more severe CNS damage . HoweverThis monocentric longitudinal prospective cohort study was conducted at Bonn University Hospital and is one of three subprojects within a three-pronged research consortium known as “COVIMMUNE: Studies on immune system function and disease progression of COVID-19”, funded by the German Ministry of Health. In addition to clinical and neuropsychological examinations conducted by trained and qualified medical investigators at three timepoints , standardised brain MR imaging was scheduled at two time points (baseline and after 12 months) for all participant groups. In this paper, we present the baseline MR imaging findings  from the radiological project arm. MRI follow-up at 12 months is planned and currently pending, and therefore not yet part of the analysis below. The local Internal Review Board  reviewed the study protocol, and final approval was obtained on 10 March 2021. All participants provided written informed consent before taking part in any study-specific procedures.This study has been preregistered at the German Clinical Trials Registry .A total of 172 participants with similar age and sex distribution were prospectively enrolled using frequency matching (as of 10 March 2022), of whom 10 failed screening and 7 were lost during the scheduled MRI examination stage due to claustrophobia (n = 4) or metallic implants (n = 3). The remaining 155 participants were categorized into three groups based on their health status: healthy control subjects (n = 56), patients with an asymptomatic/mild course (n = 51), and patients with a severe course of COVID-19 (n = 48), as explained below.The entire study protocol was recently described elsewhere  and can aged 25 to 75 yearsGeneral inclusion criteria:Cohort I: asymptomatic course (MILD) of COVID-19 (SARS-CoV-2–positive) or mild course Cohort II: severely affected course (SEV) of COVID-19 (SARS-CoV-2–positive) according to simplified WHO classification, defined as having been admitted to hospital (any ward type) for at least 24 h due to SARS-CoV-2 infection at any timepoint during the course of the diseasemust perform > –1.0 SD on the Hopkins Verbal Learning Testno substance abuseno known history of or current diagnosed psychiatric illnessnegative nCoV IgG/IgM Rapid Test before inclusion, indicative of no recent COVID-19 infectionCohort III: healthy controls (CTL) will only be included in the study if they also meet all of the following criteria:Cohort-specific inclusion criteria:general contraindication for MRIsevere or unstable medical conditioncurrent major depressive episodepsychotic disorder, bipolar disorder, substance abuse at present or in the pastknown neurodegenerative disorder vascular dementia or history of strokehistory of malignant diseaseGeneral exclusion criteria:All participants underwent standardised brain MRI at baseline following enrolment in the study. MR imaging was performed on a clinical whole-body 3 T MRI system  equipped with an 8-channel head coil with identical scanning protocols. Morphological brain imaging included three-dimensional (3D) T1-weighted magnetisation-prepared rapid acquisition with gradient echo (3D MPRAGE), 3D fluid-attenuated inversion recovery (3D-FLAIR), diffusion-weighted imaging (DWI), susceptibility-weighted imaging (SWI) and T2-weighted imaging (T2W). Details of the MRI scanning parameters are summarised in Board-certified radiologists with several years of neuroradiological experience visually examined the MRI datasets of all subjects for acute cerebral pathology and possible exclusion criteria.Automated AI-based software was used to determine quantitative analyses of the volume of different brain areas in mL and age- and sex-adjusted percentiles . This commercially licensed MRI post-processing software, named “mdbrain”, is provided by mediaire GmbH, Berlin, Germany, an approved medical device manufacturer according to the European Medical Device Directive 93/42/EEC, and is certified according to DIN EN ISO 13485:2016. The “mdbrain” software is approved as a CE-marked medical device. It performs automatic brain volumetry of different brain regions using native 3D T1-weighted sequences to allow quantitative statements based on an extensive population-based normative database. The algorithm and embedded normative database are trained nationwide, not limited to the experience of a single centre, and have been validated for accuracy .The 3D T1w MPRAGE sequence was manually transferred from the clinical PACS to the mdbrain software, v4.4.1 or higher, for automatic volumetrization and percentile calculation. The volumes and percentiles of all evaluated structures were automatically computed, saved and checked for plausibility. The volumetrized structures were the whole brain, whole white matter, whole grey matter, cerebral cortex, cerebellar cortex, frontal lobe, parietal lobe, precuneus, occipital lobe, temporal lobe, hippocampus, parahippocampal gyrus, entorhinal cortex, caudate nucleus, putamen, globus pallidum, thalamus, brainstem, mesencephalon, pons, lateral ventricle, third ventricle, and fourth ventricle. Volumes were determined separately for each of the paired structures.https://www.R-project.org/ accessed on 15 March 2023) with the jtools package . All applicable demographic and imaging data are given as mean ± standard deviation, unless otherwise specified. The statistical significance level was set at p < 0.05. A priori statistical power analyses were performed and recently described elsewhere [2 for BMI, the severity scale MILD or SEV for COVID-19), while keeping all other parameters unchanged. Further parameters included the corresponding standard error, t-value, p-value for the respective coefficient, p-values and R-squared values for the multivariate model. Statistical analyses were conducted using SPSS statistical software  and R . Smaller percentiles were detected in the supratentorial grey matter (p = 0.042), frontal lobe right (p = 0.013) and the parietal lobe right (p = 0.03) and left (p = 0.023).Comparing all COVID-19 recovered patients (MILD + SEV) with healthy controls, statistically significant smaller volumes in COVID-19 recovered patients were identified for the brainstem volume  revealed statistically significant differences in several brain volumes see also : whole bPost hoc pairwise analyses showed statistically significant differences with increasing volume decline with disease severity (SEV > MILD > CTL) in the following brain areas, as additionally outlined in After excluding the ICU-treated patients (n = 9) from the data analysis, additional pairwise post hoc comparison between the three groups revealed statistically significant decreases in brain volumes that corresponded with increasing severity of disease (SEV > MILD > CTL). This is summarised in In addition to established determinants of brain volume such as age and sex, the BMI, height and COVID-19 severity (MILD and SEV) were co-analysed as variables in a multivariate model, yielding statistically significant effects of COVID-19 on brain volume decline in the following areas : whole bThis MR imaging study evaluated the potential impact of COVID-19 on brain volume in patients after recovery from asymptomatic/mild and severe SARS-CoV-2 infection using automated AI-based volumetry. To date, this study includes the largest number of severely affected COVID-19 patients compared to previously published imaging studies, to the best of our knowledge. Our volumetric analyses revealed small but statistically significant differences in measured brain volumes according to COVID-19 severity. These atrophy patterns primarily affected the total and supratentorial grey matter, both frontal and parietal lobes, and the right thalamus. These findings were further supported by reduced percentiles normalised across the general population in the corresponding brain areas. Notably, the observed group differences were significant even after excluding 9 ICU-treated patients . This implies that the potential influence of relaxation, mechanical ventilation or intensified drug therapy on the overall outcome appears largely negligible ,13. More18F-FDG-PET studies, which reported a decrease in glucose uptake in the bilateral fronto-parietal regions of hospitalised patients during the subacute stage of COVID-19 [Previous imaging studies have mainly focused on gross CNS abnormalities in acute and hospitalised COVID-19 patients that could only be interpreted by visual assessment . TherefoCOVID-19 . AdditioCOVID-19 . A recenCOVID-19 . AnotherCOVID-19  also fouCOVID-19 , which aCOVID-19 . Many COVID-19 patients requiring hospitalisation present with mild to moderate neurocognitive deficits . DedicatThe pathomechanisms of the acute and long-term neurologic damage resulting from COVID-19 remain largely unclear. However, secondary immune-mediated inflammatory complications  are discSeveral limitations of this exploratory study must be acknowledged. Cross-sectional comparisons were made between healthy controls and COVID-19 patients who had already recovered. Accordingly, it cannot be determined with certainty whether relevant brain changes in COVID-19 patients existed before SARS-CoV-2 infection. The observed effects could ultimately be related to a pre-existing increased susceptibility of the brain to the effects of COVID-19, or therapeutic procedures. However, a recent large-scale pre-post imaging study supports our findings through the substantial overlap with the longitudinally assessed brain atrophies . FurtherWe identified a consistent spatial pattern of grey matter loss and focal atrophy in COVID-19 recovered patients, particularly in the frontal and parietal lobes and the right thalamus. These structural changes in the brain are broadly consistent with preliminary volumetric observations of grey matter loss, and were more pronounced in patients with greater disease severity of SARS-CoV-2 infection, regardless of prior ICU treatment. This strongly suggests a causal relationship between COVID-19 and the observed brain changes. Notably, our volumetric results were additionally substantiated by a concomitant decline in percentiles in the corresponding brain areas, setting our study apart from most previous imaging studies. This means that the measured brain volume differences of post-COVID-19 subjects not only exist in absolute numbers compared to our matched control and patient groups, but that the differences also increase with respect to the age- and sex-adjusted general population, with increasing severity of the initial disease. Whether these abnormal changes are due to the spread of the disease or the virus itself, which may result in future vulnerability to neurocognitive deficits or exacerbation of pre-existing neurodegenerative conditions in these patients, remains to be investigated. Our ongoing prospective re-imaging study promises to provide further insight into the cerebral effects of COVID-19, pending completion of the clinical and imaging follow-ups. To this end, our longitudinal investigation will specifically pursue two goals in the near future. Firstly, we aim to correlate the volumetric data with clinical neurologic, pneumonologic, and neuropsychologic findings to verify the potential clinical significance of the observed brain changes. Secondly, pending follow-ups may reveal whether there is a dynamic of brain atrophy over time, which might indicate the triggering of a chronic neurodegenerative process.


These differences were not due to variation in sample self-collection, which was consistent. For most individuals, longitudinal viral-load timecourses in different specimen types did not correlate. Throat-swab and saliva viral loads began to rise as many as 7 days earlier than nasal-swab viral loads in most individuals, leading to very low clinical sensitivity of nasal swabs during the first days of infection. Individuals frequently exhibited presumably infectious viral loads in one specimen type while viral loads were low or undetectable in other specimen types. Therefore, defining an individual as infectious based on assessment of a single-specimen type underestimates the infectious period, and overestimates the ability of that specimen type to detect infectious individuals. For diagnostic COVID-19 testing, these three single-specimen types have low clinical sensitivity, whereas a combined throat–nasal swab, and assays with high analytical sensitivity, was inferred to have significantly better clinical sensitivity to detect presumed pre-infectious and infectious individuals.SARS-CoV-2 viral-load measurements from a single-specimen type are used to establish diagnostic strategies, interpret clinical-trial results for vaccines and therapeutics, model viral transmission, and understand virus–host interactions. However, measurements from a single-specimen type are implicitly assumed to be representative of other specimen types. We quantified viral-load timecourses from individuals who began daily self-sampling of saliva, anterior-nares , and oropharyngeal (throat) swabs before or at the incidence of infection with the Omicron variant. Viral loads in different specimen types from the same person at the same timepoint exhibited extreme differences, up to 10 In a longitudinal study of SARS-CoV-2 Omicron viral loads in three paired specimen types , we found extreme differences among paired specimen types collected from a person at the same timepoint, and that viral loads in different specimen types from the same person often do not correlate throughout infection. Individuals often exhibited high, presumably infectious viral loads in oral specimen types before nasal viral loads remained low or even undetectable. Combination nasal–throat swabs were inferred to have superior clinical sensitivity to detect infected and infectious individuals. This demonstrates that single-specimen type reference standard tests for SARS-CoV-2, such as in clinical trials or diagnostics evaluations may miss infected and even infectious individuals.Measurements of viral load in respiratory infections are used to establish diagnostic strategies, interpret results of clinical trials of vaccines and therapeutics, model viral transmission, and understand virus–host interactions. But how viral loads change across multiple specimen types early in SARS-CoV-2 infection is not well understood. Specifically in the context of diagnostics, as new SARS-CoV-2 variants-of-concern (and new respiratory viruses) emerge with different viral kinetics , it is iSelecting testing strategies to achieve detection in the pre-infectious and infectious periods requires filling two critical knowledge gaps: (i) Which respiratory specimen type accumulates virus first? (ii) What is the appropriate test analytical sensitivity to detect accumulation of virus in the pre-infectious and infectious stages? These two gaps must be filled in parallel. Commonly, an individual's infection is described by the viral load sampled from a single-specimen type, which is appropriate when there is one principal specimen type (e.g. HIV in blood plasma). However, some respiratory pathogens, including SARS-CoV-2, can infect multiple respiratory sampling sites .Nasopharyngeal swabs have been the gold standard for SARS-CoV-2 detection but are poorly tolerated and challenging for serial sampling and self-collection. Many alternate specimen types are now widely used. Some of these are suitable for routine testing, and are approved for self-collection  in some countries. Cross-sectional studies comparing paired specimen types from the same person have shown that cycle threshold  values can differ substantially between specimen types , and theThe second knowledge gap is the analytical sensitivity needed for reliable detection of pre-infectious and infectious individuals. The assay analytical sensitivity is described by the limit of detection (LOD); generally, the LOD of an assay describes its ability to detect and quantify target at or above a certain concentration in that specimen type with >95% probability . Assays 4–107 RNA copies/mL are associated with the presence of replication-competent virus  before transmission occurs, and detection during the infectious period is critical to minimize outbreaks. Replication-competent (i.e. infectious) virus has been recovered from saliva , oropharnt virus , 31–41, nt virus , 42–44. The assumption made early in the COVID-19 pandemic that viral load always rises rapidly from undetectable to likely infectious  has beenFilling the two critical and inter-related knowledge gaps about specimen type and assay LOD requires high-frequency quantification of viral loads, rather than semi-quantitative Ct values, in multiple specimen types starting from the incidence of infection, not after a positive test or after symptom onset, as is commonly done. Moreover, quantification must be performed with a high-analytical-sensitivity assay to capture low viral loads in the first days of detectable infection. It is challenging to acquire such data. Individuals at high risk of infection must be prospectively enrolled prior to detectable infection and tested longitudinally with high-frequency in multiple paired specimen types.To our knowledge, four studies have reported longitudinal viral-load timecourses in multiple, paired specimen types from early infection. A university study  capturedHere, we measured and analyzed the viral-load timecourses of the Omicron variant in three specimen types appropriate for self-sampling  by individuals starting at or before the incidence of infection as part of a household-transmission study in Southern California. We then utilized these data to determine which specimen type and analytical sensitivity would yield the most reliable detection of pre-infectious and infectious individuals. A separate paper reports the results of daily rapid antigen testing in this study .This case-ascertained study of household transmission  was conducted in the greater Los Angeles County area between November 23, 2021, and March 1, 2022. All adult participants provided written informed consent; all minor participants provided verbal assent accompanied by written permission from a legal guardian. Children aged 8–17 years old additionally provided written assent. See Supplemental Information for details.A total of 228 participants from 56 households were enrolled; 90 of whom tested positive for SARS-CoV-2 infection during enrollment Fig. . We limiEach day, participants reported symptoms, then self-collected saliva, anterior-nares  swab, and posterior oropharyngeal (hereafter throat) swab specimens for RT-qPCR testing in Zymo Research SafeCollect devices (CE-marked for EU use), following manufacturer's instructions , 57. ParQuick SARS-CoV-2 RT-qPCR kit, with results assigned per manufacturer criteria  using the FDA-authorized criteria . Additio8 copies/mL) for each specimen type was generated at Caltech and underwent extraction and RT-qPCR as described above. All three replicates at 250 copies/mL of specimen were detected, independently validating the reported LOD for the assay. For each specimen type, the standard curve generated an equation to convert from SARS-CoV-2 N gene Ct values to viral loads in genomic copy equivalents (hereafter copies) per mL of each specimen type. See Supplemental Information for additional details and equations. Positive specimens with viral loads that would be quantified below the assay LOD were considered not quantifiable.To quantify viral load in RT-qPCR specimens, contrived specimens across a 13-point standard curve  with an infectious viral load until the first timepoint after which no specimen has an infectious viral load , 60. Or,To quantify the difference between viral-load timecourses, we first aligned each timecourse to the time of collection of the first SARS-CoV-2-positive specimen (of any type) for each participant. Differences between viral loads from the same infection timepoint were quantified Fig. A, B. We RNase P control target measurements .Participants were considered infected from the time of collection of the first SARS-CoV-2-positive specimen (any type) until negative in all three specimen types by RT-qPCR. Individuals were presumed infectious when viral load in any specimen type was above an IVLT  Are differences in viral loads between specimen types large enough to impact detectability by assays with varying analytical sensitivity? (ii) Are differences in viral loads attributable to variability in participant sampling behavior? (iii) Are viral-load timecourses in different specimen types within a person correlated with each other?4 copies/mL, a scale of difference likely to impact the detection of SARS-CoV-2.First, we calculated the absolute Fig. A and relRNase P control marker. However, RNase P Ct measurements were relatively stable for each specimen type collected by participants across their timecourse  over enrollment  from different specimen types in a person might still be synchronized. For example, viral loads in one specimen type  might be consistently lower than those in another , but follow the same pattern throughout acute infection. If this were the case, viral load measured in one specimen type would still be associated with the viral load in another specimen type despite extreme absolute differences. To test whether timecourses from different specimen types were synchronized, we quantified the correlation between viral-load timecourses for each specimen type collected from a single participant, and across different participants. These intra- and interparticipant correlations are represented as a matrix for the 42 viral-load timecourses , at least two specimen types from the same participant had significantly different timecourses. In 38% of comparisons (16 of 42), we observed significantly different timecourses for each of the three specimen types from the same individual . Additionally, in 29% (4 of 14) of individuals, saliva and nasal-swab viral-load timecourses differed significantly. Finally, despite the proximity of the two oral sampling locations in 21% 3 of 14) of individuals, their own saliva and throat viral-load timecourses were significantly different  yielded significantly better inferred clinical sensitivity to detect infected persons than testing with a low-analytical-sensitivity assay , nasal swabs were predicted to miss more than half (54%) of timepoints from infected persons. Saliva and throat-swab specimens had significantly better inferred clinical sensitivity than nasal swabs when a high-analytical-sensitivity assay was used, and worse (but not significantly) when a low-analytical-sensitivity assay (LOD of 106 copies/mL) was used  to test the robustness of our conclusions.Prompt identification of individuals who are or will become infectious can prevent further transmission. We next compared the ability of each specimen type and assay analytical sensitivity to detect presumably infectious individuals. An individual was presumed to be infectious if the viral load in any specimen type collected from that participant at a given timepoint was above an infectious viral load threshold. We performed separate analyses for four well-accepted infectious viral-load thresholds , a presumed noninfectious viral load in one specimen type , a presumed noninfectious viral load in one specimen type correctly inferred a noninfectious participant only about 24% of the time (47 of 197 timepoints).We found that because of the extreme differences in viral-load timecourses, a presumed noninfectious viral load in one specimen type did not reliably indicate that a participant would have presumed noninfectious viral loads in all specimen types. At the highest infectious viral-load threshold  or low- (LOD 106 copies/mL) analytical sensitivity in each bin , except when infectious period is defined as the 5 days following the first infectious specimen. This definition selects earlier timepoints, prior to the rise in nasal-swab viral loads , whereas saliva and throat swabs tended to fluctuate between 104 and 107 copies/mL . Third, a nasal–throat combination swab always had the highest inferred clinical sensitivity at all LODs.Three additional trends held across all infectious viral-load thresholds and all definitions of the infectious period. First, nasal swabs had similar performance to saliva and throat swabs when testing with high-analytical-sensitivity assays  collected at the same timepoint from the same individual. In all 14 individuals, we also observed that the viral-load measurements in different specimen types followed significantly different longitudinal timecourses. These intraparticipant differences were as extreme as those observed between participants Fig. C, D. TheBecause of the extreme differences in viral-load patterns in the early and pre-infectious periods of infection, of the three specimen types considered here, none is optimal for detecting Omicron. However, nasal swab was the poorest specimen type for detection in the first 4 days of infection. In most participants, we observed a delay in nasal-swab viral loads relative to oral specimens similar to what has been observed previously , 22, 80 3 copies/mL) were inferred to improve clinical sensitivity in all specimen types and at all stages of infection. We also found that even with high-analytical-sensitivity testing, none of the three specimen types considered here were optimal for detection of presumed infectious individuals . Of the three single-specimen types, nasal-swab testing was inferred to miss the lowest proportion of presumed infectious individuals overall; yet nasal swabs still missed at least a quarter of all presumably infectious timepoints because of high viral loads in oral specimen types  was inferred to yield significantly improved clinical sensitivity to detect both infected Figs.  and presafter the onset of COVID-like symptoms and/or after an initial positive test ; thus, they likely did not sample the earliest days of infection, which is the period when we found the greatest benefit of sampling with saliva or a throat swab. One prospective cohort study that did begin testing early (using pre-symptomatic and asymptomatic close contacts) and used combination oropharyngeal–nasal swabs with an RT-qPCR assay as reference to evaluate two antigen rapid diagnostic tests . Third,Viral loads are used in many clinical and basic-science contexts, including diagnostics, epidemiological models, clinical trials, and studies of human immune response. Our results show that early in SARS-CoV-2 infection, viral load cannot be defined for a person, only for a specific specimen type within a person. Thus, when viral-load studies or viral-detection studies are performed with only single-specimen type, the results should be interpreted while considering the heterogeneity of viral loads across specimen types. Additional quantitative longitudinal studies of differences in viral loads in multiple specimen types starting immediately at the incidence of infection are needed for new emerging variants and new respiratory viruses. In the absence of such studies, combination specimen types and tests with high-analytical sensitivity are likely to be the most robust approaches for earliest detection and for the design of studies seeking to assess infection status or presence of infectious virus.pgad033_Supplementary_DataClick here for additional data file.