Two dogs (1.6%) had suspected anosmia as reported by their handlers; one of which was seropositive. Known exposure to the dog’s COVID-19 positive handler or household member was identified as a significant risk factor. Demographics factors including sex, altered status, and type of work were not associated with canine seropositivity. Further work is warranted to understand the impact of SARS-CoV-2 and other infectious diseases in working dogs.Susceptibility to and infection with SARS-CoV-2 in companion animals has been well-documented throughout the COVID-19 pandemic. Surveillance for the virus in dogs has largely been focused on household pets; however, other canine populations may also be impacted. We partnered with a local veterinary hospital with a high working dog patient volume to conduct viral and neutralizing antibody testing in working dogs and identify potential risk factors in the dog’s work and home environments. Surveillance of SARS-CoV-2 in law enforcement and security working dogs in Arizona found 24.81% (32/129) of dogs to be seropositive. Thirteen dogs presenting with clinical signs or with reported exposure to COVID-19 in the 30 days prior to sample collection were also tested by PCR; all samples were negative. 90.7% ( Working dogs play a critical role in supporting the health and safety of the public. These dogs are trained to perform specific tasks and are employed in meaningful work, including protection, detection, and therapy . ProtectDuring the COVID-19 pandemic, susceptibility to and infection with SARS-CoV-2 was well-documented in companion animals, including dogs , 4. EvidSurveillance for SARS-CoV-2 in dogs has largely been focused on household pets. While surveillance has been conducted for a variety of infectious diseases, such as bartonellosis and Chagas in working dogs , 10, effDogs were recruited from a veterinary hospital in Maricopa County, Arizona, which experiences a high working dog patient volume. Approval for this work was granted by TGen’s Animal Care and Use Committee (#20163) and the ADHS’s Human Subjects Review Board (#20-0017). Written consent was received from the handlers of the dogs sampled.Working dogs presenting to the veterinary hospital for routine examinations, vaccinations, or clinical illness were recruited by the veterinary hospital staff. Serum collection via venipuncture was performed on all dogs; these samples were initially centrifuged and stored in the refrigerator for 48–72 h. Working dogs with known exposure to COVID-19 within the past 30 days or dogs exhibiting signs compatible with viral infection also had nasal and rectal swabs collected. Swab samples were placed in conical vials with 2mls of sterile phosphate-buffered saline and frozen until shipment. A short questionnaire was administered to collect demographic information , type of work the dog was involved in, COVID-19 exposure status, and any signs or symptoms observed by the handler.Serum was centrifuged at 2,500 rpm for 10 min upon arrival, aliquoted and tested for the presence of viral neutralizing antibodies (vNAbs) using the GenScript cPass™ SARS-CoV-2 Neutralizing Antibody assay per the manufacturer’s instructions . This isSARS-CoV-2 genomic material was extracted from nasal and rectal swab samples using Zymo DNA/RNA extraction kits. Extracted material was tested using a previously described real-time polymerase-chain reaction (rRT-PCR) assay , 13, devDescriptive statistics were determined for breed, age, sex, altered status, type of work, health status, COVID-19 exposure status, SARS-CoV-2 serology, and PCR testing. Chi-square analysis was employed to assess the relationships between serology results and COVID-19 exposure status using SAS version 9.4.Results were reported back to the veterinary hospital and the collaborating veterinarian communicated those to the handlers of the working dogs. If a working dog tested positive, recommendations regarding COVID-19 prevention strategies were provided to the handler. Handlers of seropositive dogs were also invited to participate in a semi-structured interview with project staff to gain a better understanding of canine medical history, household dynamics, and home and duty environments , 15.During November 2021 through June 2022, 129 working dogs were recruited into the study. Serum samples were collected from all 129 enrolled dogs. Paired nasal and rectal swabs were collected on 13/129 dogs (10.1%) with known exposure to COVID-19 within the past 30 days or if the dog exhibited signs compatible with viral infection. Repeat sampling was performed on 4/13 dogs (3.1%) due to new exposures to COVID-19 positive people and to confirm serology results.n = 78) and German Shepherds (n = 15) were the most common breeds of dog, comprising 72.1% of the study population, although other breeds were represented. A majority of the dogs (n = 108) were intact males (63.6%) or spayed females (20.2%). Police patrol and detection was the most common role , followed by detection only , hospital patrol/security , and police patrol only .n = 117) of sampled dogs were reported to be asymptomatic or have no change in performance as noted by their handler at the time of sampling. Twelve dogs (9.3%) had mild clinical signs, including vomiting, diarrhea, coughing, lethargy, and decreased performance. Six of the 12 dogs were symptomatic at the time of sampling and 6/12 dogs had symptoms previous to collection but within the month after handler’s reported COVID-19 infection. Two of these dogs were diagnosed with Valley Fever. Of the 12 dogs, 1.6% (n = 2) were reported by their handlers to have “missed known finds,” indicating potentially compromised scent detection ability . Evidence of neutralizing antibodies was present in 24.8% (n = 32) of dogs; one (3.1%) of which had suspected anosmia. All 13 dogs tested by PCR was negative.90.7% , illustrated in 53.5% (69/129) of working dogs had known or suspected exposure to SARS-CoV-2 , while 46.5% (60/129) had no documented exposures in their home or duty environment . Of the Project staff conducted in-depth interviews with handlers for 40.6% (13/32) of seropositive dogs. All 13 dogs lived in the home of their handler; 92.3% were single family homes and 76.9% of homes had additional household members. Documented or suspected exposure to SARS-Cov-2 in the home environment was identified for all 13 (100%) dogs. It was further reported that these dogs, when off duty, had frequent and close interactions with their handler and other household members. While in a duty environment, 69.2% of dogs had regular interaction with their human co-workers , but less than a quarter (23.1%) of dogs interacted with members of the public . Interviews also identified a recent history of valley fever in four (30.8%) dogs.Working dogs bring diverse and critical skill sets to the law enforcement, military, and search and rescue environment, ultimately helping to protect the health and safety of the public . Given tThe authors originally hypothesized that working dogs may have a higher risk of SARS-CoV-2 exposure (and therefore likelihood of infection) due to their unique and expanded interactions in their duty environment compared to pet dogs. However, the results of this study are comparable with previous surveillance studies in pet dogs , 6, 13 aThere are a few noteworthy limitations to report. First, samples were collected from dogs as they visited the veterinary clinic for routine examination, vaccination, and only in some cases onset of clinical illness. The timing of sample collection, therefore, did not specifically correlate with possible SARS-CoV-2 exposure , and further explains why only a small number of enrolled dogs had swab samples collected. Second, the single-dilution SARS-CoV-2 neutralizing antibody assay used in this study does provide only a semi-quantitative measurement of antibody response and is not able to differentiate between a viral exposure that occurred more recently  versus an exposure that occurred 6 months or more prior. Therefore, we were unable to pinpoint the exact timeframe of the dog’s exposure, unless clearly linked with known human positive cases in the household. An additional limitation is that complete information about potential exposures, work and home environment, and other risk factors were not captured for every dog enrolled. This limitation was recognized by the authors, and the study was designed to capture limited information from the canine handlers since the veterinary clinic was responsible for enrollment, consent, and sample collection. The goal was for the protocol to be minimally burdensome to the staff and fit into their standard operating procedures.Exposure to varying endemic zoonotic diseases has been previously investigated in working dogs, and continuing this type of surveillance for emerging diseases, like SARS-CoV-2, in this population is important. Although only reported in a limited number of dogs, this study also sheds light on the potential for working dogs to diminish or lose their sense of smell after SARS-CoV-2 exposure. Further investigation in this area is needed to understand the impact that SARS-CoV-2 may have on this population of dogs’ work capacity. Agencies that deploy working dogs routinely assess scent detection capabilities during training, however these detection and mitigation plans may need revision. Additionally, this work can inform veterinary practices, policy development , and guide further management and prevention efforts that may be unique to this canine population .The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The animal study was reviewed and approved by TGen’s Animal Care and Use Committee (#20163). Written informed consent was obtained from the owners for the participation of their animals in this study.GH, HY, and HV designed and coordinated the surveillance study with collaborating veterinary hospital. GH, NS, and HY led the preparation of the manuscript, including tables and figures. WS collected samples and data from dogs, as well as provided feedback on the manuscript. NS conducted the laboratory testing of the samples. IR provided guidance on analyses and reviewed manuscript. HM, JA, and DE provided technical support and oversaw the laboratory and surveillance efforts. Additionally, HM, JA, and DE provided important revisions to the manuscript. All authors contributed to the article and approved the submitted version.This study was supported in part by an appointment to the Applied Epidemiology Fellowship Program administered by the Council of State and Territorial Epidemiologists (CSTE) and funded by the Centers for Disease Control and Prevention (CDC) Cooperative Agreement Number 1NU38OT000297-03-00.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The relationship between non-alcoholic fatty liver disease (NAFLD) and triple-negative breast cancer (TNBC) has been widely recognized, but the underlying mechanisms are still unknown. The objective of this study was to identify the hub genes associated with NAFLD and TNBC, and to explore the potential co-pathogenesis and prognostic linkage of these two diseases. We used GEO, TCGA, STRING, ssGSEA, and Rstudio to investigate the common differentially expressed genes (DEGs), conduct functional and signaling pathway enrichment analyses, and determine prognostic value between TNBC and NAFLD. GO and KEGG enrichment analyses of the common DEGs showed that they were enriched in leukocyte aggregation, migration and adhesion, apoptosis regulation, and the PPAR signaling pathway. Fourteen candidate hub genes most likely to mediate NAFLD and TNBC occurrence were identified and validation results in a new cohort showed that ITGB2, RAC2, ITGAM, and CYBA were upregulated in both diseases. A univariate Cox analysis suggested that high expression levels of ITGB2, RAC2, ITGAM, and CXCL10 were associated with a good prognosis in TNBC. Immune infiltration analysis of TNBC samples showed that NCF2, ICAM1, and CXCL10 were significantly associated with activated CD8 T cells and activated CD4 T cells. NCF2, CXCL10, and CYBB were correlated with regulatory T cells and myeloid-derived suppressor cells. This study demonstrated that the redox reactions regulated by the NADPH oxidase (NOX) subunit genes and the transport and activation of immune cells regulated by integrins may play a central role in the co-occurrence trend of NAFLD and TNBC. Additionally, ITGB2, RAC2, and ITGAM were upregulated in both diseases and were prognostic protective factors of TNBC; they may be potential therapeutic targets for treatment of TNBC patients with NAFLD, but further experimental studies are still needed. Breast cancer is the most commonly diagnosed cancer and the leading cause of cancer death among women . Triple-Non-alcoholic fatty liver disease (NAFLD) is a common chronic liver disease intimately related to metabolic syndrome and abdominal obesity ,12,13. IIn addition, breast cancer patients often develop non-alcoholic fatty liver disease during the course of disease. Bilici et al.  reportedRecently, advances in sequencing technology and bioinformatics have made it possible to explore the pathogenesis of diseases and the interactions between different diseases at the gene level, which is expected to shed new light on the pathogenesis, diagnosis, and treatment of diseases ,23,24. Hhttp://www.ncbi.nlm.nih.gov/geo/, accessed on 1 November 2022) database. The nature of the three microarray datasets from the GEO database is summarized in Three microarray datasets, GSE63067 and GSE48452 of NAFLD, and GSE38959 of TNBC, were collected from the GEO  database (TCGA-BRCA cohort). TNBC samples were selected according to the status of ER, PR, and HER2 by referring to the method of Craven et al. [RNAseq profiling in the form of fragments per kilobase million (FPKM) and clinicopathological breast cancer data were obtained from the TCGA  + 2 × sd(|logFC|)] were defined as DEGs. The overlapping DEGs between NAFLD and TNBC were delineated using the R package “ggVennDiagram”. These common DEGs with consistent upregulation or downregulation trends were retained for subsequent analysis.DEGs were extracted and analyzed separately using the R package “limma”. The fold changes (FCs) were calculated for individual gene expression levels. Genes meeting specific cut-off criteria of p-value < 0.1 were selected.The above overlapping DEGs were submitted to Gene Ontology (GO) functional enrichment analysis, which consisted of biological process (BP), cellular component (CC), and molecular function (MF) analyses, and to Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment analysis using the R package “cluster Profiler”. The enriched GO terms and KEGG pathways with an adjusted http://string-db.org/, accessed on 12 November 2022) was used for PPI network construction. Subsequently, Cytoscape software was used to visualize the PPI network. The Cytoscape plug-in Minimal Common Oncology Data Elements  was used to screen out key protein expression molecules and multiple topological analysis algorithms in the cytoHubba plug-in , such as MCC, MNC, Degree, and EPC, were used to screen the hub genes in the PPI network.To further explore the interactions among the common genes obtained as described above, the Search Tool for the Retrieval of Interacting Genes (STRING)  and 95% confidence intervals (CIs), and survival curves were derived using Kaplan–Meier (KM) survival analysis with log-rank tests for comparison.The expression levels of the identified hub genes were validated in 132 TNBC samples and 113 controls from the TCGA cohort, and 18 NASH samples and 14 controls from the GEO database. The Wilcoxon test was used to compare the data between the two groups, and a two-sided http://cis.hku.hk/TISIDB/data/download/CellReports.txt, accessed on 20 November 2022) to quantify the infiltration of immune cells in TNBC tissue and determine the level of immune infiltration in each sample. Pearson’s correlation analysis was used to reveal the relationships between hub genes and immune cells.The ssGSEA  algorithm is a rank-based method that defines a score representing the degree of absolute enrichment of a particular gene set in each sample ,29. The In the NASH and control groups in the GSE63067 dataset, there were 498 up-DEGs and 163 down-DEGs screened with a logFC threshold of 0.472 A. In thep < 0.1, significantly enriched GO terms and KEGG terms were selected.To better understand the biological functions of the identified DEGs, GO and KEGG pathway enrichment analyses were performed. After screening with the threshold of adjusted As shown in p value < 1.0 × 10−16, was obtained and imported into Cytoscape software v3.9.1 for visualization  and a maximum number of interactions of no more than 20 interactors to increase the maximum number of interactions and the number of proteins directly related to the input proteins. Then, a PPI with 47 nodes and 122 edges, with a PPI enrichment lization .The MCODE plug-in was used to conduct module analysis to detect crucial clustering modules. Three modules were retrieved from the PPI network. The criteria were set as follows: Degree Cutoff = 2, Node Score Cutoff = 0.2, K-Core = 2, and Max. Depth = 100. Module 1 included 10 nodes and 84 edges with a cluster score (density times the number of members) of 9.333. Module 2 and module 3 had 6 nodes and 20 edges and 3 nodes and 6 edges, respectively, and the scores were 4.000 and 3.000, respectively B–D.The CytoHubba plug-in was used to identify hub genes. Based on the MCC, MNC, Degree, and EPC algorithms, the top 15 important hub genes in the PPI networks were predicted. The intersection of these 15 genes from the four algorithms revealed 14 candidate hub genes: TLR4, CYBB, NCF1, NCF2, S100A8, S100A9, ITGB2, RAC2, ITGAM, CYBA, ICAM1, CXCL10, CXCR3, and ITGAL.Combined with the logFC values of the hub genes in the GSE38959 dataset, the GO and KEGG enrichment pathways were analyzed. The top 10 GO terms and KEGG pathways are shown in Validation was performed in the TCGA-BRCA cohort for TNBC and the GSE48452 dataset for NASH. For TNBC, the differences in the expression levels of all hub genes between normal tissues and TNBC samples were statistically significant A. Comparp ≤ 0.05) were subjected to multivariate Cox regression analysis, and it was found that the N stage was an independent risk factor for overall survival and the remaining factors were not significant.To evaluate the clinical relevance of hub gene expression, the TNBC samples were divided into a high-expression group and a low-expression group according to the median expression level of each gene for prognostic analysis. Univariate Cox analysis suggested that high expression of ITGB2, RAC2, ITGAM, and CXCL10 was associated with better overall survival (OS) . The corp = 2.42 × 10−9; ICAM1, r = 0.705, p = 1.32 × 10−7; CXCL10, r = 0.802, p = 1.03 × 10−10) and activated CD4 T cells . Specifically, NCF2, CXCL10, and CYBB were correlated with regulatory T cells , and myeloid-derived suppressor cells (MDSCs) . In addition, ITGB2 was associated with activated B cells  and immature B cells .In recent years, the relationship between NAFLD and breast cancer has become a research hotspot, and an increasing number of studies have confirmed the correlation. Some studies have reported that breast cancer is a common extrahepatic complication of NAFLD ,30. SimuIn this study, we explored common DEGs of NASH and TNBC datasets in public databases through bioinformatics analysis and observed the biological processes and signaling pathways in which they jointly participate. GO and KEGG enrichment analyses of the common DEGs showed enrichment in leukocyte aggregation, migration and adhesion, apoptosis regulation, and the PPAR signaling pathway, suggesting that TNBC in NAFLD patients was likely due to enhanced leukocyte recruitment in the inflammatory response and abnormal apoptosis. Interestingly, the PPAR signaling pathway not only controls the expression of genes encoding proteins of lipid metabolism, but is also involved in anti-cancer responses . One of A total of 14 candidate hub genes most likely to mediate NASH and TNBC occurrence were identified, including TLR4, CYBB, NCF1, NCF2, S100A8, S100A9, ITGB2, RAC2, ITGAM, CYBA, ICAM1, CXCL10, CXCR3, and ITGAL.CYBB, CYBA, NCF1, NCF2, and RAC2 are NADPH oxidase (NOX) subunit genes and are associated with inflammation and fibrosis in multiple organs, such as the liver ,40, lungITGAM, ITGB2, and ITGAL are involved in the most common integrins expressed on leukocytes, including Mac-1 (αMβ2 or CD11b/CD18) and leukocyte function-related antigen 1 (LFA-1 or αLβ2) ,51. ActiCXCL10 and its homologous receptor CXCR3 are critical in the development of specific features of the NAFLD phenotype, wherein they are mainly involved in the induction of inflammation, regulation of adipogenesis and oxidative stress, and other related processes ,59. In tAlthough many studies have linked metabolic syndrome to the development of cancer and poor prognosis, it may be a symptom of a general metabolic disorder. Our study explored the relationship between NAFLD and TNBC at the genetic level for the first time, and found that the hub genes ITGB2, RAC2, and ITGAM were upregulated in both diseases and were prognostic protective factors in TNBC. This is inconsistent with our understanding of risk factors such as obesity, a high-fat diet, and NAFLD that promote the occurrence and progression of breast cancer. Therefore, further experimental studies will be of great significance and are expected to find new targets for diagnosis, prognostic assessment, and treatment of TNBC.The study had several limitations. First, although the role of these genes has been elucidated in multiple studies, the key pathways and hub genes identified have not been validated in experiments. Second, due to the lack of a dataset, the validation of the hub gene was performed in patients with only NAFLD or TNBC, but not in patients with NAFLD combined with TNBC. Third, the relationship between the hub genes and the prognosis of TNBC patients needs to be confirmed by prospective clinical studies.In conclusion, this study explored the hub genes of NAFLD and TNBC and illustrated the possible mechanisms for the co-occurrence trend of these two diseases. Redox reactions regulated by the NOX subunit genes and the transport and activation of immune cells regulated by integrins may play a central role in the development of NAFLD and TNBC. Additionally, the expressions of ITGB2, RAC2, ITGAM, and CXCL10 were significantly correlated with a good prognosis in TNBC and may be potential therapeutic targets for the development of gene therapies for TNBC patients with NAFLD.


Hypertension is a leading problem; it affects around thirty million adult Egyptians, according to the last national registry. The exact prevalence of resistant hypertension (RH) in Egypt wasn’t spotted before. The purpose of this study was to determine the prevalence, predictors, and impact on adverse cardiovascular outcomes among adult Egyptians with RH.The present study examined a cohort of 990 hypertensive patients who were divided into two groups based on their blood pressure control; group I (n = 842) patients who achieved blood pressure control and group II (n = 148) patients who met the RH definition criteria. All patients underwent a close follow-up for one year to evaluate the major cardiovascular events.2, and NSAID use. After one year of follow-up, the RH group displayed noticeably higher rates of major cardiovascular events, including new-onset atrial fibrillation , cerebral stroke , myocardial infarction , and acute heart failure .The prevalence of RH was 14.9%. The main predictors impacting the cardiovascular outcomes of RH were advanced age (≥65 years), the presence of chronic kidney diseases, a BMI ≥ 30 kg/mThe prevalence of RH in Egypt is moderately high. Patients with RH have a far higher risk of cardiovascular events than those whose blood pressure is within control. Hypertension (HTN) is a major reversible clinical obstacle leading to increased morbidity and mortality globally. It affects approximately 1.3 billion people and is responsible for 7.5 million deaths a year . Accordi5Resistant hypertension (RH) is a subtype of hypertension characterized by the difficulty of achieving BP goals below 140/90 mmHg despite the maximal dose of three anti-hypertensive medications, including diuretics, or achieving BP goals on four anti-hypertensive medications . The cliUncontrolled BP is associated with a higher increase in all cardiovascular morbidity and mortality . Each 10The current study was conducted on patients who visited the cardiology outpatient clinic at Tanta university hospital with a confirmed diagnosis of hypertension between May 2020 and September 2021. The diagnosis of HTN was established either by the previous history of diagnosis and treatment of HTN or by blood pressure ≥ 140/90 mmHg on 2 to 3 office visits two weeks apart, besides their home BP >130/80 mmHg or a single BP measurement of ≥180/110 mmHg. The sample size was calculated using the EPI 7 ™ info program  [According to the control of blood pressure, the patients were divided into two groups: group I (controlled hypertension) included 842 patients who achieved blood pressure measurements < 140/90 on two different visits, and group II (resistant hypertension) included 148 patients whose BP was ≥140/90 mmHg, despite being adherent to medications and taking 3 antihypertensive drugs on the optimal dose, including the diuretic, or taking four or more drugs, nevertheless they weren’t controlled. All patients who enrolled in the study signed informed consent. The study was accepted by the Ethical Committee of Tanta Medical Faculty and agreed with the principles of the Declaration of Helsinki II.At baseline, a detailed history was taken for risk factors evaluation, including age, sex, smoking, diabetes, dyslipidemia, physical activity, excess use of salt diet >5 g sodium per day, previous history of cardiovascular, cerebrovascular, chronic kidney, and peripheral arterial diseases, obstructive sleep apnea, family history of hypertension, and history of their medications, which include antiplatelets, statins, chronic use of nonsteroidal anti-inflammatory drugs (NSAIDs) if taken more than three times per week, antidepressants, and corticosteroid drugs. In addition, the dose of antihypertensive drugs, and their adherence to prescribed medication were assessed by a questionnaire with every single visit. Moreover, the patients’ level of education, income category, place of residence, and presence of health insurance were assessed. Consequently, occupational and marital status were asked.A physical examination was done for all patients, including an assessment of their weight, height, body mass index (BMI), heart rate, blood pressure, and ankle-brachial index. Blood pressure measurements at baseline were average for the second and third visits after the established diagnosis of HTN. All patients underwent laboratory investigations, an electrocardiogram (ECG) was done for all patients at baseline, at the end of 12 months, and at any cardiovascular events. Transthoracic echocardiography was carried out for all patients using the Vivid E9 ultrasound system  equipped with an M5S phased array transducer (2.5–5.0 MHz) according to the guidelines of the American Society of Echocardiography . Two-dimSecondary causes of hypertension were excluded, including renal artery stenosis, coarctation of the aorta, and endocrine abnormalities, before patients› enrolment. the compliance to prescribed medication and its doses was checked during the first visit and the scheduled follow-ups at the 2nd and 4th weeks from the first visit then after 3, 6, 9, and 12 months using a questionnaire at every single visit. Patients with non-compliance or non-adherence to medication were excluded from the study. Patients who were absent during follow-ups, experienced an acute systemic illness, such as COVID-19 were also excluded from the study. We used both home and office BP monitoring to rule out the white-coat effect and patients with white coat hypertension were excluded from the study. At each visit, blood pressure and heart rate were measured, along with clinical evaluations for recording and assessing the occurrence of any major cardiovascular events. The primary endpoint of the study was the occurrence of mortality or major adverse cardiovascular events (MACE), including cardiac events , cerebral events , resuscitation after cardiac arrest, and acute peripheral vascular ischemia.2) test was used to assess two qualitative parameters. A two-sided P value > 0.05 was considered statistically significant. Multivariate regression analysis was done to identify the independent predictors of RH.Statistical analysis was performed using SPSS 23, IBM, Armonk, NY, USA. Quantitative data were expressed as mean ± standard deviation. Qualitative data were expressed as frequency and percentage. The student’s t-test was used to test the significance between the two groups in the quantitative data. A chi-square (XThe current study included 990 patients with hypertension divided into two groups: 842 patients (85.1%) had a controlled BP <140/90 mmHg (group I), and 148 patients (14.9%) had RH (group II). Baseline characteristics, risk factors, and socioeconomic factors of all patients in both groups were summarized in 2) than the other group (26.72 ± 4.45 kg/m2). The RH group had significantly elevated systolic and diastolic blood pressure values . However, the discrepancies in glycemic and lipid profiles between the two groups were negligible. Raised serum creatinine was substantially greater in the RH group, with a decrease in e-GFR in relation to the controlled BP group  and , respectively. Microalbuminuria, serum potassium, uric acid, ankle-brachial index, and carotid IMT levels did not differ significantly between the two groups as shown in As regarding the clinical, laboratory, and echocardiographic data for both groups. The RH group had a higher BMI .Concerning echocardiographic findings, E/E’ was significantly higher in the RH group , and LAVI was noticeably higher P = 0.016. Furthermore, the percentage of patients with LVH was raised in the RH group in comparison to the controlled BP group  with a higher LVMI , and NSAIDs use , as illustrated in The major cardiovascular events that occurred in all participants were demonstrated in Resistant hypertension is a challenge in controlling the BP and management of hypertension-related complications, resulting in a remarkable increase in cardiovascular diseases, end organ damage, and premature death. Hereditary genes, aberrant sympathetic and renin-angiotensin-aldosterone system activation, endothelial dysfunction, decreased arterial compliance, and increased systemic vascular resistance all contribute to the pathogenesis of resistant hypertension 16. SubseThe prevalence of RH was 14.9% in the current study. The incidence of RH differed around the world and was estimated at around 10–20% . In-deptComparable to the age of the patients in the present study, patients with RH were significantly older than the controlled BP group . In agreement, many studies demonstrated a significant increase in RH incidence with aging 89, which wThe current study revealed that the number of patients suffering from CKD was higher in the RH group. In correspond with these results, a meta-analysis demonstrated the high prevalence of RH in CKD 29%) and renal transplantation patients (56%) . In para82728% and ren2 doubled the possibility of RH; moreover, visceral obesity was attached to a high aldosterone level with a final result of uncontrolled BP. In the present study, RH was linked to obstructive sleep apnea (P = 0.043). Supporting this result, several studies, including a large cohort study in the USA, demonstrated an increase in the intensity risk of RH with obstructive sleep apnea by 60% [33The prevalence of obesity and BMI was significantly higher in patients with RH in the current study. These results came in agreement with Gijón-Conde et al. , Naseem a by 60% 3334. By by 60% 33 and Buhnby 60% 33 remarkedby 60% 33.Concerning echocardiographic findings, there was a noticeable increase in the incidence of LVH and LVMI in patients with RH. In concordance with the present results, Dobrowolski et al.  revealedThe RH group had a poorer outcome after one year of follow-up, and MACE rates were significantly higher in this group of patients. The RH group had a considerably greater incidence of new-onset atrial fibrillation (6.8%) than the controlled group (2.5%). It is the consequence of high E/E’ and LAVI, resulting in left atrial remodeling and an increase in its filling pressure. Additionally, acute heart failure incidence was higher in the RH group (4.7% vs. 1.8%). Heart failure with preserved ejection fraction was previously known to be the predominant type in RH, which may be explained by the current results of an increase in LA filling pressure and a reduction of diastolic function .Additionally, cerebral stroke was (4.1%) in the RH group and (1.2%) in the controlled BP group. Likewise, the myocardial infarction rate was significantly elevated in the RH group. Nevertheless, no significant difference in mortality could be detected owing to the short follow-up period. Similar to the previous results, Chun et al.  after 4.2, and the use of NSAIDs. In the ALLHAT Trial, old age, BMI ≥ 30 kg/m2, and serum creatinine above 1.5 mg/dl were linked to failure to reach BP goals, a high incidence of atherosclerosis, and stiffness in the vessels [2) was an independent factor associated with a high risk of RH, and patients with CKD were at a higher risk of increased cardiovascular events, especially heart failure. Also, the use of NSAIDs was combined with an increase in the prevalence of RH, especially in the elderly and CKD patients, due to deterioration of kidney function, renal ischemia, and activation of the renin-angiotensin systems [Concerning the independent predictors influencing the cardiovascular outcomes in the present study, a multivariate regression analysis was done, showing the following predictors: age ≥ 65 years, CKD, BMI ≥ 30 kg/m vessels . Therefo vessels  studied  systems .The present study was a one-center study with only one-year follow-up, and a quietly small number of populations, to a certain extent, reflected the prevalence of RH in Egypt. As a result, multicenter studies on RH are needed to identify the prevalence of RH in other geographical areas in Egypt, such as Upper Egypt, and to augment awareness of the disease’s hazardous complications as well as the importance of lifestyle adjustments, and regular follow-ups.Resistant hypertension is a heading socioeconomic burden on global health, especially in growing countries like Egypt. Its prevalence is rising and differs according to the demographic characterization of the populations. It was an adjunct to the significant risk of MACE. So, early identification of the affected patients and establishing a proper modifiable plan of management are crucial, along with reliable follow-up. Furthermore, a special interest in geriatric, obese, NSAIDS users, and CKD patients is prudent to reduce the progress of end-organ damage and MACE.


The literature has identified that a sense of coherence plays a protective role in the relationship between adverse events and mental health. The current study examines the role of a sense of coherence (SOC) in the relationship between fear of COVID-19, perceived stress, and dimensions of post-traumatic stress disorder (PTSD). Participants (n = 322) were students at a metropolitan university in South Africa who completed the Fear of COVID-19 Scale, the Perceived Stress Scale, the 13-item Sense of Coherence Scale, and the PTSD Checklist. Path analysis was used to conduct a serial mediation analysis. The results show that SOC mediates the relationship between perceived stress and the dimensions of PTSD but does not mediate the relationship between fear of COVID-19 and PTSD. Furthermore, the relationship between fear of COVID-19 and dimensions of PTSD was mediated by serial perceived stress and sense of coherence, supporting the hypothesis that higher levels of fear of COVID-19 leads to higher levels of perceived stress. However, while high levels of fear of COVID-19 increase perceived stress, SOC significantly mediates the subsequent impact on PTSD symptoms. The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, has had an unparalleled impact on global health systems, economies, and daily life. Despite widespread awareness of the disease and general support for public health measures to contain it, the implementation of social distancing and lockdown protocols, coupled with the potentially severe outcomes associated with infection, contributed to elevated levels of stress and adverse mental health outcomes among many population groups . Fear reThe persistent fear associated with the outbreak of the disease has been found to lead to symptoms commonly associated with PTSD, such as intrusive thoughts, heightened anxiety, and emotional numbing. A meta-analysis focusing on PTSD among schoolteachers  reportedDespite the prevalence of adverse mental health outcomes, many studies have provided compelling evidence of resilience and adaptation to stressors during the disease outbreak. For example, Shanahan and colleagues , using aSense of coherence (SOC)  is a fouIn the context of health research, the salutogenic approach, with SOC at its core, shifts the focus from factors leading to disease to those promoting health and resilience . A compePrior research on SOC has predominantly focused on acute stressors  that are universally experienced across various societies. However, the unprecedented nature of the COVID-19 pandemic introduced a distinct and widespread chronic stressor, transcending specific populations and geographical boundaries. The pervasive nature of the COVID-19 disease outbreak, which affected individuals from diverse backgrounds and communities, represents an opportunity to understand the role and influence of a SOC in the face of a ubiquitous threat. Given that a SOC offers insights into how individuals perceive, manage, and find meaning in life’s challenges , it is cWe reasoned that higher levels of fear of COVID-19 could potentially lead to higher levels of perceived stress and that SOC would mediate the impact of fear of COVID-19 and perceived stress on the dimensions of PTSD. In this regard, we hypothesized that:1:H SOC has a direct effect on the dimensions of PTSD.2:H SOC mediates the relationship between fear of COVID-19 and the dimensions of PTSD.3:H SOC mediates the relationship between perceived stress and the dimensions of PTSD.4:H The relationship between fear of COVID-19 and the dimensions of PTSD is mediated by serial perceived stress and SOC.α = 0.05, 1 − β = 0.95). The email also contained a description of the aims of the study as well as an invitation to participate. The majority of the sample were women (77%) and most of them resided in urban areas. The mean age of the sample was 26.01 years (SD = 10.19). Data collection occurred in the period March–July 2022 and therefore also included COVID-19-related information. In this regard, 80.1% of the sample indicated that they knew people who had contracted COVID-19, 86.6% were vaccinated, and 40.7% had lost a family member due to COVID-19.Participants were students (n = 322) at a university in Cape Town, South Africa. Google Forms were used to develop an online survey instrument. The electronic link was sent to a random sample of 1700 students via email, as G*Power indicated that 262 participants were required  , the Perα = 0.82) and provided evidence of concurrent validity [α = 0.91, Mokken scale reliability = 0.92) and provided evidence for construct, convergent, and concurrent validity [α = 0.87).The FCV-19S is a seven-item scale that assesses emotional fear reactions related to COVID-19. Responses to the items were made on a five-point scale that ranges from “strongly disagree” (1) to “strongly agree” (5). An example of an item from the FCV-19S is “My hands become clammy when I think about coronavirus-19”. The authors of the scale reported satisfactory internal consistency  and provided evidence for construct validity [α = 0.85).The PSS is a 10-item measure of the extent to which respondents appraise events in their lives as stressful. The items are scored on a five-point scale, with scale anchors of “never” (0) and “very often” (4). An example of an item on the PSS is “How often have you felt difficulties were piling up so high that you could not overcome them?” The authors of the PSS reported a satisfactory estimate of internal consistency .The SOC-13 is a measure of the extent to which respondents view situations in life as meaningful, manageable, and comprehensible. Responses to the 13 items are made on a seven-point scale with different scale anchors for the 13 items. An example of an item of the SOC-13 is “Do you have the feeling that you are in an unfamiliar situation and don’t know what to do?” with scale anchors of “very often” (1) and “very seldom or never” (7). The author of the scale provided a review of the psychometric properties of the scale and found reliability coefficients ranging between 0.74 and 0.91, and evidence for construct and criterion validity across 16 studies . In Soutα = 0.94) and convergence (rs = 0.74 to 0.85) as well as discriminant (rs = 0.31 to 0.60) validity [α = 0.89), avoidance (α = 0.89), negative alternations in mood (α = 0.88), and hyper-arousal (α = 0.82).The PCL-5 is a 20-item measure of the 20 symptoms of PTSD listed in the Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) . The 20 validity . A Southvalidity . In the The Humanities and Social Sciences Ethics Committee of the University of the Western Cape  approved the study. The study was conducted in line with the guidelines of the Declaration of Helsinki. Participants provided informed consent on the landing page of the electronic survey. Participation was voluntary, and no identifying particulars were collected.t-test, and the relationship between age and the outcome variable was examined using Pearson’s r. Due to the relatively large sample size, we evaluated the correlation coefficients in terms of effect size and, in this regard, used the guidelines provided by Cohen [IBM SPSS for Windows Version 28  was used to obtain descriptive statistics (means and standard deviations), indices of kurtosis and skewness, intercorrelations between study variables (Pearson’s r), and reliabilities  of all the instruments. With respect to skewness and kurtosis, it is suggested that skewness values between −2 and +2, as well as kurtosis values between −7 and +7, indicate that the data are approximately normally distributed . Gender by Cohen : small =p-values.We used IBM SPSS Amos for Windows Version 28  to conduct a path analysis with the dimensions of PTSD as the outcome variables and perceived stress and SOC as the mediators. The significance of the direct and indirect effects was evaluated using 95% bootstrapped confidence intervals and The descriptive statistics (means and standard deviations), indices of skewness and kurtosis, intercorrelations between study variables (Pearson’s r), and reliabilities of scales  are reported in α = 0.82 to 0.89). Fear of COVID-19 was significantly positively associated with re-experiencing , avoidance , negative alterations in mood and cognition , and hyper-arousal . Thus, higher levels of fear of COVID-19 were associated with higher levels of PTSD. Similarly, perceived stress was significantly positively associated with re-experiencing , avoidance , negative alterations in mood and cognition , and hyper-arousal . Higher levels of perceived stress were thus associated with higher levels of PTSD. SOC was significantly negatively associated with re-experiencing , avoidance , negative alterations in mood and cognition , and hyper-arousal . Higher levels of a SOC were therefore associated with lower levels of PTSD.The indices of skewness (−0.18 to 0.35) and kurtosis (−1.18 to −0.16) were within an acceptable range and confirmed that the data were approximately normally distributed. All the scales demonstrated satisfactory reliability , avoidance , negative alterations in mood and cognition , and hyper-arousal . Age was significantly negatively correlated with re-experiencing , avoidance , negative alterations in mood and cognition , and hyper-arousal .There were no significant gender differences with regard to re-experiencing , avoidance , negative alterations in mood and cognition , and hyper-arousal . These results regarding the direct effects of a SOC of the dimensions of PTSD confirm Hypothesis 1.β = 0.01, p = 0.373), avoidance , negative alterations in mood and cognition  and hyper-arousal . Thus, Hypothesis 2 was rejected.β = 0.17, p = 0.001), avoidance , negative alterations in mood and cognition , and hyper-arousal . These results confirm Hypothesis 3.β = 0.04, p < 0.001), avoidance , negative alterations in mood and cognition , and hyper-arousal . These results confirm Hypothesis 4.Finally, The path analysis model with standardized regression coefficients is presented in This study aimed to examine the association between fear of COVID-19, perceived stress, SOC, and the dimensions of PTSD. Rooted in the premise that a heightened fear of COVID-19 may invariably precipitate increased perceived stress, we sought to determine the mediating role of a SOC in this association. There were several salient findings.First, higher levels of perceived stress and fear of COVID-19 were positively associated with PTSD symptom clusters and overall PTSD. This suggests that individuals with greater perceived stress and fear of COVID-19 were more likely to relive traumatic events related to the pandemic and avoid reminders of the experience. This could translate to avoiding discussions about the pandemic, staying away from news reports, or avoiding places or activities that may have been triggers . They weSecond, higher levels of a SOC were associated with lower levels of PTSD. This finding offers further support for existing research on the role of a SOC after trauma exposure. For example, in a meta-analytic study , SchäferDominant cognitive behavioral models of PTSD ,42 ascriThird, the results suggest a notable relationship between age and PTSD symptom clusters in older participants who reported fewer symptoms. The mean age of the sample was 26 years, and the aforementioned finding can be attributed to older participants possibly having more life experiences and coping mechanisms that enabled them to manage and process traumatic events more effectively than their younger counterparts. This accrued resilience could be a product of having faced and navigated past adversities, equipping them with better emotional and cognitive tools to deal with the challenges posed by the pandemic ,44. FurtFourth, the results of the path analysis found that SOC had a direct effect on PTSD symptom clusters and mediated the relationship between perceived stress and symptoms of PTSD. Furthermore, serial perceived stress and SOC mediated the relationship between fear of COVID-19 and dimensions of PTSD. This means that fear of COVID-19 can increase perceived stress, which in turn, based on the individual’s SOC, can influence the development of PTSD symptoms. These findings suggest that individuals with a stronger SOC are less likely to develop symptoms associated with PTSD, despite being exposed to potential trauma, which further supports the findings in the extant literature ,47. The Contrary to our expectations, a SOC did not significantly mediate the relationship between fear of COVID-19 and various dimensions of PTSD. This outcome suggests that the presence of a robust SOC, though pivotal in providing resilience against stress and trauma, may not directly influence the specific PTSD pathways triggered by the fear of COVID-19. The absence of mediation might be due to the unique and unprecedented nature of the COVID-19 pandemic, which introduced high levels of uncertainty, disrupted normalcy, and presented existential threats that individuals across the globe grappled with. The specific fears surrounding COVID-19, such as the fear of contracting the virus or spreading it to loved ones, may evoke a distinct stress response, leading to PTSD symptoms irrespective of the level of a SOC. These findings have potential implications. This study provides further empirical evidence suggesting that a stronger SOC can mediate the impact of perceived stress and pandemic-related fear, thus providing an argument for the development of resilience-focused interventions and preventive strategies. The observation of age-related resilience can contribute to developmental theories on stress and trauma, suggesting that accrued life experiences and developed coping mechanisms in older individuals can serve as protective factors against PTSD symptomatology. Given the significant association between perceived stress, SOC, and PTSD symptom clusters, there is a clinical imperative to develop interventions aimed at bolstering a SOC and managing stress more effectively. This can involve cognitive behavioral strategies, psychoeducation, and resilience-building activities. Tailored interventions can be developed for different age groups, focusing on enhancing coping mechanisms and stress management strategies in younger populations, given their observed vulnerability to developing PTSD symptoms in the context of the pandemic.This study had certain limitations. The study utilized a cross-sectional design, which captures data at a single point in time. This limited the ability to infer causality or track changes in perceptions, stress, SOC, and PTSD symptoms over time. A longitudinal approach might have provided additional insights into the evolution of these aspects over the course of the pandemic. Data collection in this study relied on self-reported measures, which can be susceptible to response bias, recall bias, or social desirability bias. The sample consisted primarily of university students with a mean age of 26 years. This may not be representative of the general population, limiting the generalizability of the findings to broader demographic groups, including older adults, non-students, or individuals from different socioeconomic backgrounds. Other unmeasured variables, such as pre-existing mental health conditions, coping strategies, social support levels, or exposure to previous traumas, might have influenced the observed relationships and were not accounted for in this study. While path analysis offers insights into potential causal relationships between variables, it is based on correlational data. Thus, it does not establish definitive causality. Furthermore, this study was undertaken during a specific phase of the pandemic. As public perceptions, government policies, and the overall pandemic situation have since evolved, the findings may vary if the study was replicated at a different point in time.In the context of the unprecedented challenges posed by the COVID-19 pandemic, this study sought to examine the relationships between fear of COVID-19, perceived stress, a SOC, and PTSD symptoms. Our findings underline the substantial role of perceived stress and fear of COVID-19 in exacerbating PTSD symptom clusters. In our study, a strong SOC emerged as a potent buffer against the development of PTSD. The inverse relationship between age and PTSD symptoms suggested that accumulated life experiences and coping mechanisms might confer a degree of resilience against traumatic events like the pandemic. The path analysis underscores the significance of a SOC, not only as a standalone protective factor but also as a mediator in the interplay between perceived stress, fear of COVID-19, and PTSD symptoms.


Per-/polyfluoroalkyl substances (PFASs) are persistent organic pollutants and suspected endocrine disruptors.The aim of this work was to conduct a systematic review with meta-analysis to summarise the associations between prenatal or childhood exposure to PFASs and childhood overweight/obesity.The search was performed on the bibliographic databases PubMed and Embase with text strings containing terms related to prenatal, breastfeeding, childhood, overweight, obesity, and PFASs. Only papers describing a biomonitoring study in pregnant women or in children up to 18 years that assessed body mass index (BMI), waist circumference (WC), or fat mass in children were included. When the estimates of the association between a PFAS and an outcome were reported from at least 3 studies, a meta-analysis was conducted; moreover, to correctly compare the studies, we developed a method to convert the different effect estimates and made them comparable each other. Meta-analyses were performed also stratifying by sex and age, and sensitivity analyses were also performed.In total, 484 and 779 articles were retrieved from PubMed and Embase, respectively, resulting in a total of 826 articles after merging duplicates. The papers included in this systematic review were 49: 26 evaluating prenatal exposure to PFASs, 17 childhood exposure, and 6 both. Considering a qualitative evaluation, results were conflicting, with positive, negative, and null associations. 30 papers were included in meta-analyses . Positive associations were evidenced between prenatal PFNA and BMI, between PFOA and BMI in children who were more than 3 years, and between prenatal PFNA and WC. Negative associations were found between prenatal PFOS and BMI in children who were 3 or less years, and between PFHxS and risk of overweight. Relatively more consistent negative associations were evidenced between childhood exposure to three PFASs  and BMI, in particular PFOS in boys. However, heterogeneity among studies was high.Even though heterogeneous across studies, the pooled evidence suggests possible associations, mostly positive, between prenatal exposure to some PFASs and childhood BMI/WC; and relatively stronger evidence for negative associations between childhood exposure to PFASs and childhood BMI.The online version contains supplementary material available at 10.1186/s12940-023-01006-6. Childhood overweight and obesity are recognised worldwide issues. According to the World Health Organisation, 39 million children under 5 years were affected by overweight or obesity in 2020 and over 340 million aged 5–19 years were affected by overweight or obesity in 2016 , includiin-vitro studies showed that PFASs can disrupt some hormone functions [Per- and polyfluoroalkyl substances (PFASs) are a group of artificial compounds. Thanks to their surfactant, greaseproof, stain-proof, water repellent, and fire repellent properties, PFASs are used widely, including in food processing, medical articles, apparel, household products, electronics, and firefighting , 10. PFAunctions , 22, andunctions ; moreoveunctions , 25, higSeveral observational studies in humans were conducted to assess the effects of PFASs, and many studied their effect on prenatal growth: indeed, previous systematic reviews evidenced that a prenatal exposure to PFOA and PFOS is associated with a lower birth weight –28. SeveThe PECO statement  for the This review and its protocol were registered on PROSPERO, the International prospective register of systematic reviews , 40.st January 2000 to 31st December 2022 and in English language were considered. Articles retrieved were collected in electronic databases  independently read the titles and abstracts to select suitable papers for inclusion, while blinded to each other’s decisions. At the end of the process, discrepancies were discussed and evaluated with the final decision made by a third person acting as a supervisor (S.F.). To be eligible for inclusion, a human biomonitoring study quantifying one or more PFASs in biological samples  of pregnant women or in children/adolescent up to 18 years old had to be conducted. Furthermore, the measured outcomes had to include a measure of overweight/obesity in children (up to 18 years old) such as BMI, waist circumference (WC), or fat mass/body fat percentage (BFP), which can be obtained with different approaches such as Dual-Energy X-ray Absorptiometry (DXA) (considered the gold standard) or skinfold thickness , 45. TheAfterwards, papers judged suitable after reading their title and abstract were further inspected in their entirety. Finally, included papers were grouped in three categories: studies considering only prenatal exposure to PFASs, studies considering only exposure in children/adolescents, and studies considering both prenatal and children exposure.Information from included papers were collected in the Excel database . As for Furthermore, each article was evaluated for the quality of its reporting: a list of 28 items was established, most of which following the STrengthening Reporting of Observational studies in Epidemiology-Molecular Epidemiology (STROBE-ME) , while oAside from the extraction of the information reported in the previous section and the qualitative evaluation of studies, meta-analyses were performed among a subset of the included papers. Studies were eligible for inclusion in meta-analyses if they reported the estimated beta coefficient of the association between PFAS concentrations and the considered outcomes , along with the 95% confidence intervals (CI); or the association between PFAS concentrations and the risk of overweight/obesity, along with 95% CI. For each study, the estimates were considered separately if they were calculated considering the entire population (both sexes combined), only boys, or only girls. If the same cohort of subjects was considered in more than one paper, only one study was included in the meta-analyses. For each PFAS, outcome, and sex-category, a meta-analysis was performed only if at least three studies reported suitable estimates.The slopes of the continuous associations were considered; when only slopes from categories (such as percentiles) were reported, we considered the highest estimate reported (worst-case scenario) . For risA specific methodology was developed to ensure that the beta coefficients and the confidence intervals included for meta-analysis were comparable each other. The conversions were performed to have, for all the included studies, beta estimates and coefficient intervals that represent the mean increase of z-scores of the outcome for each unit (ng/mL) increase in the PFAS.Firstly, the standard error was calculated from the 95% CI as follow:Where Then, four different steps of conversion were performed:If the concentrations of PFASs were log-transformed before performing the linear models, the beta was changed according to the following formula elaborated by Rodríguez-Barranco and co-workers .While 95% CI was calculated as follows:Where 2)If also the outcome variable were log-transformed and the effect estimates were reported as percent change; first, the beta was calculated from the variation percentage (var%) with the following formula:and the standard error was calculated from the 95% CI of the var% as follow:then, beta and 95% CI were calculated with the following equation elaborated by Rodríguez-Barranco et. al .\documenWhile 95% coefficients intervals were calculated as follows:Where 3)If the effect estimate was reported by change in interquartile range, the slope was divided by the interquartile range in order to have a feasible beta for unit increase. Analogously, if the effect estimate was reported by change of a specific tercile versus the reference tercile, it was divided by the difference of means of tercile. The standard error was also divided accordingly, and the coefficient intervals calculated again.4)2 the reported BMI mean (or median) was converted into BMI z-score using the Word Health Organization (WHO) references [Finally, a further correction was carried out considering the type of data outcome used in each study. For BMI data, no change was made if the effect estimate was calculated from BMI z-score or BMI SDS, while if calculated from BMI expressed as kg/mferences , 50, impferences , which uferences :2) reported by the study; while the value used for Where ferences , 50 cons2) Analogously, estimates obtained from waist circumference (in cm) were transformed using the tables reported by Sharma et al. [2) by dividing the percentage per 100 and multiplying by mean (or median) BMI, then as fat mass index z-score using tables from Weber et al. [Where a et al. . For bodr et al. .All the above calculations were performed on the beta coefficients considering the associations between PFASs and BMI or WC. Instead, in order to properly make comparable the data related to the risk of overweight, the following procedure was applied to:If the data were reported as RR, they were converted as OR following a formula elaborated considering the one reported by , and expWhere OR is the odds ratio, RR is the risk ratio, P0 is the prevalence of overweight  in the reference population (e.g: the first tercile or the first quartile) or in the total proportion (when data were reported as continuous). The same formula was applied to the lower and upper limit of the 95% CI.b)Since OR is not a symmetrical measure, OR data were converted as standardized mean difference following a formula reported by . In partWhere d is the standardized mean difference and Log(OR) is the logarithm (base 10) of the OR. The same formula was applied to the lower and upper limit of the 95% CI.Finally, the obtained standardized mean differences were further converted using the same methodology reported above in steps 1) and 3) to suitably consider together PFAS concentrations calculated with different log-transformations and the different ranges of concentrations considered in each study.All these elaborations were carried out by developing a specific R-script, which is reported in the supplementary material (I2 statistics [2) [The generic inverse variance method was used, both fixed and random pooled estimates were calculated, heterogeneity was measured with the atistics  and the tics [2) . To assetics [2)  and an Etics [2) . All thetics [2) . Forest tics [2)   were also performed separately considering studies which measured the outcomes in children who were 3 or less years from those measuring the outcomes in children who were more than 3 years  to 1.82. Considering studies measuring PFASs in children, PFOA mean or median concentrations ranged from 0.3 to 117.34 ng/mL; PFOS from 0.04 to 41.5; PFHxS from 0.02 to 27.66; PFNA from 0.03 to 28.78; PFDA from less than the LOD to 0.604.For prenatal studies, across all studies, most reported no significant associations between considered PFASs and outcomes, as well as some controversial results as both positive and negative associations were reported: for PFOA and BMI, 14 studies reported no associations, 4 a negative association, 8 a positive association, and 1 both a positive and a negative association; for PFOA and WC, 7 studies reported no associations, 2 a negative and 4 a positive; for PFOA and FM/BFP, 9 studies reported no associations, 4 a positive and 1 both; for PFOS and BMI 12 reported no associations, 6 a negative, 7 a positive, and 2 both; for PFOS and WC, 10 reported no associations, 2 a negative, and 1 a positive; for PFOS and FM/BFP, 9 no associations, 2 negative, 1 positive, and 2 both; for PFHxS and BMI, 11 no associations, 2 negative, 4 positive, and 1 both; for PFHxS and WC, 9 no associations, 2 positive, and 1 both; for PFHxS and FM/BFP, 10 no associations and 2 postive; for PFNA and BMI, 14 no associations, 2 negative, and 4 positive; for PFNA and WC, 10 no associations, 2 positive; for PFNA and FM/BFP, 8 no associations, 1 negative, and 3 positive; for PFDA and BMI, 6 no associations, 1 negative and 1 positive; for PFDA and WC, 4 no associations; for PFDA and FM/BFP, 2 no associations, 1 negative, and 1 positive.For studies measuring PFASs in children, even though there were still several works reporting no significant associations, there were also several ones reporting a negative associations: in particular, for PFOA and BMI, 9 studies reported no associations, 10 a negative association, and 1 a positive association; for PFOA and WC, 5 studies reported no associations, and 2 a negative associations; for PFOA and FM/BFP, 7 studies reported no associations, 2 a negative; for PFOS and BMI 11 reported no associations, 7 a negative, and 1 a; for PFOS and WC, 11 reported no associations, 1 a negative, and 1 a positive; for PFOS and FM/BFP, 6 no associations, and 4 negative; for PFHxS and BMI, 9 no associations, 4 negative, 2 positive; for PFHxS and WC, just 4 no associations; for PFHxS and FM/BFP, 4 no associations and 3 negative; for PFNA and BMI, 10 no associations, and 5 negative; for PFNA and WC, 2 no associations, and 1 negative; for PFNA and FM/BFP, 4 no associations, and 3 negative; for PFDA and BMI, 7 no associations, and 3 negative; for PFDA and FM/BFP, 2 negative, and 1 positive.Each study was evaluated for the quality of reporting, considering the guidelines of the STROBE-ME . Most stAfter collecting the estimates from the studies included in this systematic review, considering the requirement of having suitable estimates from at least 3 studies, we calculated the pooled estimates for the overall association between prenatal concentrations of PFOA, PFOS, PFHxS, PFNA, PFDA, PFUA, PFDoA, PFBS and BMI; between prenatal concentrations of PFOA, PFOS, PFHxS, PFNA and risk of overweight; and between prenatal concentrations of PFOA, PFOS, PFHxS, PFNA, PFDA, PFUA and WC. Furthermore, suitable data were available for calculating pooled estimates of the association between childhood concentrations of PFOA, PFOS, PFHxS, PFNA and childhood BMI; and between childhood concentrations of PFOA, PFOS and risk of overweight. For all of the above, we were able to pool the estimates for both sexes combined, for boys exclusively, and for girls exclusively, with the exception of childhood PFNA and childhood BMI, and for all the data related to risk of overweight, for which only the effect of both sexes combined were calculated. When more studies describing the same cohort were reported, only one was included in the meta-analysis, in particular: for the HOME study, data from Liu et al.  were choAltogether, out of the 32 studies included in this systematic review evaluating prenatal exposure to PFASs, 23 could be included for the meta-analyses, in particular: data from 17, 12, and 13 studies were suitable for the meta-analysis evaluating the associations between prenatal PFOA and BMI for both sexes combined, boys, and girls, respectively; similarly, 17, 12, and 13 studies were included for PFOS; 11, 9, and 10 studies for PFHxS; 11, 9, and 10 studies for PFNA; 6, 6, and 6 studies for PFDA; 5, 5, and 5 studies for PFUA; 3, 3, and 3 studies for PFDoA; 3, 3, and 3 studies for PFBS. Considering the association between prenatal exposure to PFASs and risk of overweight, data from 8 studies were included for the meta-analysis for PFOA for both sexes combined, 8 studies for PFOS, 6 studies for PFHxS, 6 studies for PFNA. Considering the association between prenatal exposure to PFASs and childhood WC, data from 8, 8, and 9 were included for the meta-analyses for PFOA for both sexes combined, boys, and girls, respectively; 8, 8, and 9 for PFOS; 8, 7, and 8 for PFHxS; 8, 7, and 8 for PFNA; 4, 4, and 4 for PFDA; 3, 3, and 3 for PFUA .Out of the 23 studies included in this systematic review evaluating childhood exposure to PFASs, 11 were included in the meta-analyses, in particular: considering the association with childhood BMI, data from 8, 3, and 4 studies were included for the meta-analyses for PFOA for both sexes combined, boys, and girls, respectively; 8, 3, and 3 for PFOS; 6, 3, and 3 for PFHxS; and 5 for PFNA . Finally, considering the association between childhood exposure to PFASs and risk of overweight, data from 4 studies were suitable for PFOA and 3 for PFOS, in both cases only for both sexes combined .2. Among the 10 studies considering WC, 5 calculated the beta estimate from WC z-scores or WC SDS, while 5 from WC as cm. Among the 10 studies considering the risk of overweight, 5 reported the OR, 4 the RR, and 1 reported a linear association with a categorical variable “overweight—yes” (so was not modified considering steps a) nor b) reported in the “2.3.3” section). Thus, the data conversions described in the section “2.3.3” were useful to properly include all these studies in the meta-analyses ensuring that the effects estimates were comparable each other , 12 as natural logarithm, 5 as base-10 logarithm and 5 as base-2 logarithm. Moreover, one study also natural log-transformed the outcome variable and reported the result as percentage change in the outcome. Among all the 30 studies, 19 calculated the estimates out of continuous data, 6 reported mean changes for interquartile range increase, 2 reported differences between terciles, 1 reported differences between quartiles, 1 reported mean differences for each standard deviation increase, and 1 reported percentage change in the outcome. Among the 25 studies considering BMI, 20 calculated the beta estimate from BMI z-scores or BMI SDS, while 5 from BMI as kg/mch other .The results of all the meta-analyses performed are summarised in Table 2 = 0.58, 0.57) and in girls   and in girls , for prenatal PFNA and WC in both sexes combined , and for prenatal PFUA and WC in both sexes combined  Table . Convers3) Table .2 = 0.84 for childhood PFOA and BMI both sexes, 0.82 for PFOS and BMI both sexes, 0.81 for PFNA and BMI both sexes), and the Egger’s test p-value evaluating was below 0.05 for PFOA and PFOS  , PFOS and BMI both sexes  and only boys ; a significant associations was found for childhood PFNA and BMI both sexes in the analyses weighted per samples sizes  (Table y) Table .p-value was 0.047. Conversely, an inverse association between prenatal PFOS and BMI measured in children of both sexes who were 3 or less years was found only in the invariance method analyses . Significant positive associations were also found also, only with the analyses weighted per sample size, between prenatal PFNA and boys who were more than 3 years . Significant negative associations were also found in the analyses weighted per sample size between prenatal PFHxS and risk of overweight in children who were more than 3 years and positive associations were found between prenatal PFUA and WC; significant negative associations, confirmed both with inverse variance and weighted per samples size analyses were also found between childhood PFOA and BMI in children of both sexes who were more than 3 years, and between childhood PFOS and BMI in children who were more than 3 years in both sexes and in boys only; finally, a negative association, exclusively in analyses weighted per sample size, was found for childhood PFNA and BMI in children of both sexes who were more than 3 years: all of these results were the same of the non-age-stratified analyses as all the included studies considered children who were more than 3 years.In the meta-analyses performed stratifying by age Table , a signiThe sensitivity analyses stratified by age performed by excluding one study per time mostly confirmed the significant associations found, with an additional negative significant association in a single inverse variance method analysis between prenatal PFOA and BMI in children of both sexes who were than 3 years .We conducted a systematic review aimed to summarise the available evidence about an early-life exposure to PFASs  and the association with childhood overweight and obesity. Forty-nine different papers were included, among which 26 assessing prenatal exposure to PFASs, 17 childhood exposure, and 6 both. Overall, most associations were conflicting and there was no clear pattern of unequivocal evidence, but a certain number of studies reported of a negative association between PAFSs measured during childhood and outcomes assessed during childhood. We developed a methodology to convert the effect estimates from the different studies to make them comparable to each other, and when data were available and suitable from at least three studies, considering separately both sexes combined, only boys, and only girls, we conducted meta-analyses to pool the estimates. In particular, meta-analyses were performed with 30 studies . Considering differences in the two methods performed for pooling the evidence (inverse variance and weighting per sample size) and in the sensitivity analyses (excluding one sample per time), no overall strong significant association was observed between prenatal exposure to PFASs and the outcomes. Otherwise, relatively more consistent significant negative associations were found between childhood concentrations of PFASs (PFOA and PFOS in particular) and BMI in children.Concerning the PFASs measured in the included studies, PFOA and PFOS were always quantified and PFHxS and PFNA were measured in several studies. These are legacy compounds, whose presence is very persistent and ubiquitous in the environments despite having been restricted. Moreover, in the last years, other emerging PFASs have been introduced in the market; they include molecules with a shorter carbon chain such as PFBA and PFBS , 114. ElConcerning the analytical assay, PFASs were quantified using liquid chromatography coupled with tandem mass spectrometry, with isotopic dilution; this can be considered the gold standard approach to quantify PFASs in biological samples , 117. MoThe outcomes considered for this review were measures of overweight, obesity, or adiposity. Almost all studies measured BMI in children, and some of them also WC and/or FM/BFP. A possible source of bias is represented by how the anthropometric measures were collected: they were measured by experts in most studies (34), while in few studies they were only reported by questionnaires or in other indirect ways (6), or by a hybrid way of the two (2), while the information about the collection of anthropometric measures was not clear in a few other studies (4) . AlthougAlthough the reporting of the studies was judged of good quality, some limitations in the study design can be pointed out. Studies evaluating the prenatal exposure to PFASs were longitudinal studies , while mostly of those assessing the exposure to PFASs in children are cross-sectional studies. Results from longitudinal analyses are more likely to suggest a possible causal relationship than cross-sectional studies. Only 5 studies quantifying PFASs in children were conducted measuring outcomes some years later , 112 . Negative associations were found between prenatal PFOS and BMI in children who were 3 or less years, and between PFHxS and risk of overweight. Positive associations were also found between prenatal PFNA and WC, and between prenatal PFUA and WC . Nevertheless, these results were not consistent across meta-analyses performed with the generic invariance method or by pooling per sample size. The evidence pooled from studies measuring PFASs during childhood was more consistent as confirmed in both generic invariance and weighting per sample size analyses, and also in most sensitivity analyses: negative associations were found between PFOA and BMI, PFOS and BMI (in particular in boys), and between PFNA and BMI (the latter only weighting by sample size), even though heterogeneity among studies was high.Additional file 1: Supplementary text. The supplementary text contains the text strings used for the search in PubMed and Embase and all the R scripts implemented prepared for this workAdditional file 2: Supplementary figures. In the supplementary figures file, for each meta-analysis, three figures are reported: a forest plot pooling the estimates with the generic invariance method, a forest plot pooling the estimates weighting by sample size, and a funnel plot evaluating the bias for included studiesAdditional file 3: Supplementary Table S01. Table of entries retrieved from PubMedAdditional file 4: Supplementary Table S02. Table of entries retrieved from EmbaseAdditional file 5: Supplementary Table S03. Complete database of the considered studies, including: information retrieved from PubMed and Embase; whether the study was included or not in the systematic review and the category of inclusion or rejection; information collected from included articles; and score of reporting assigned to included articlesAdditional file 6: Supplementary Table S04. Data extracted from articles used for performing the meta-analysesAdditional file 7: Supplementary Table S05. Data extracted transformed using the conversion factor developedAdditional file 8: Supplementary Table S06. Results of the meta-analyses performed with the generic invariance method (“inv_var”) and weighting by sample size (“wgtd”). The I2 as a measure of heterogeneity is also reported. Both the original meta-analyses are reported together with the sensitivity meta-analyses repeated by excluding one sample per timeAdditional file 9: Supplementary Table S07. Results of the meta-analyses stratified by age (1-3 years or 4-18 years). Meta-analyses performed with the generic invariance method (“inv_var”) and weighting by sample size (“wgtd”) are reported. The I2 as a measure of heterogeneity is also reported. Both the original meta-analyses are reported together with the sensitivity meta-analyses repeated by excluding one sample per time


Despite advancements in the available radiological investigation techniques, not all cases of Distant Metastasis (DM) are diagnosed at initial clinical presentation. Also, there are currently no standard biomarkers of metastasis. Early, accurate diagnosis of DM is however crucial for clinical decision making, and planning of appropriate management strategies. Previous works have achieved little success in attempts to predict DM from either clinical, genomic, radiology, or histopathology data. In this work we attempt a multimodal approach to predict the presence of DM in cancer patients by combining gene expression data, clinical data and histopathology images. We tested a novel combination of Random Forest (RF) algorithm with an optimization technique for gene selection, and investigated if gene expression pattern in the primary tissues of three cancer types  with DM are similar or different. Gene expression biomarkers of DM identified by our proposed method outperformed Differentially Expressed Genes (DEGs) identified by the DESeq2 software package in the task of predicting presence or absence of DM. Genes involved in DM tend to be more cancer type specific rather than general across all cancers. Our results also indicate that multimodal data is more predictive of metastasis than either of the three unimodal data tested, and genomic data provides the highest contribution by a wide margin. The results re-emphasize the importance for availability of sufficient image data when a weakly supervised training technique is used. Code is made available at:  Proper management planning for carcinoma patients requires accurate diagnosis, and prognosis prediction. A major prognostic factor, metastasis, however is not always diagnosed at initial patient presentation. Metastasis refers to the dissemination of cancer cells away from the initial site of origin to form colonies at distant organs. This single hallmark of malignancies is responsible for the highest proportion (approximately 90%) of cancer related mortalities . In HNSCTP53 was significantly different between primary and metastatic samples in seven cancer types, while PTEN mutation level was different in five cancer types. Copy number variations also differ significantly in all 15 cancer types examined. TP53, PTEN, CDKN2A, and MYC as significantly mutated genes in the metastasis of various subsets of cancer types.Past studies have investigated the presence or absence of generalizable links between genes involved in the metastasis of different cancer types, either on the basis of mutation, or expression level. However, metastasis has proven to be a complex molecular and biochemical process. While similar mutation rate, and expression pattern have been reported in selected genes across groups of metastatic cancer, there are multiple claims that there are in fact no specific cancer metastatic genes. In a study carried out by In specific cancer types, designated differentially expressed signature genes are the basis of some of the past attempts to stratify cancer patients based on the risk of DM . A recenThe wealth of morphological information contained in the tumor microenvironment is routinely exploited by pathologists for making definitive diagnosis of cancer and predicting patient prognosis. However, aside from being time consuming, this tedious process has also been associated with inter- and intra-observer variability that sometimes lead to unresolved diagnosis or worse, errors in diagnosis. With the use of Convolutional Neural Network (CNN), histopathology images have proven to be good predictors of malignancy status, important molecular biomarkers of various clinical and research relevance, as well as other cellular and extracellular processes . While mRecent trends of use of multimodal data for prediction of diagnosis and outcomes in cancer patients have reported mostly improved metrics compared to use of singular modes of data . In the - We identify genomic markers of DM in three different carcinoma types using a novel combination of the random forest algorithm with an optimized feature selection approach described in . Genes s- Using various machine learning techniques, we investigate and substantiate claims that genes involved in DM tend to be more cancer type specific rather than general across all cancer types, and that there are no specific cancer metastasis genes.- We built separate models to predict DM from gene expression data, clinical data, or histopathology images, as well as multimodal combinations of the three data types. Models metrics show that multimodal data provides an edge for prediction of DM over genomic, clinical or histopathology data. However, the genomic data has the highest contribution by a wide margin.- Unlike with genomic data in which features tend to be more cancer type specific, models built from histopathology image dataset of all three cancer types in total had better metrics than those built from a single cancer type image dataset, emphasizing the importance of sufficient data to build a robust model when a weakly supervised technique is used.In this work, we attempt to predict DM using gene expression data, clinical data, and histopathology images from primary tumors of three carcinoma types - Pancreatic Adenocarcinoma (PAAD), Bladder Carcinoma (BLCA), and Head and Neck Squamous cell Carcinoma (HNSC). The contributions of this research include:Barcode of patients with DM in the TCGA-HNSC, TCGA-BLCA, and TCGA-PAAD projects were retrieved from Genomic Data Commons (GDC) Data Portal, and gene expression data, clinical data, and histopathology images of these patients were downloaded. The few number of available cases of DM is a common challenge in most studies on metastasis. BLCA, and PAAD were selected based on the number of cases of DM in these cancer types that are available on The Cancer Genome Atlas (TCGA), and the number of cases with available corresponding genomic, image, and clinical data. A few cancer types were dropped during this selection process due to the absence of some variables in their records. For example, Skin Cell Carcinoma (SKCM) was eliminated due to missing “number_of_lymph_nodes_positive_by_HE” variable which is present in BLCA, PAAD, and HNSC datasets. HNSC was added based on the interest of one of the authors. See We designed a study that utilized machine learning techniques to simultaneously identify biomarkers of DM in each of BLCA, PAAD, and HNSC, and investigate if genes involved in DM are similar across the three cancer types. Each cancer type dataset was pre-processed separately. Equal numbers of complementary gene expression data of samples without DM  were downloaded for each of the cancer types, and we ensured that there were no overlapping samples between the groups with DM, and those without DM. After extracting Transcript Per Million (TPM)) normalized values of protein-coding genes from the individual records, all samples in a group were merged to create a table, and NaN values were replaced with zero. Other initial preprocessing steps include log to base ten transformation of data to adjust for the wide and non-linear values, removal of genes with a value of zero in greater than 80% of the samples, and selection of only top 10000 genes with highest variance across all samples.To identify important genes that are involved in DM, we utilized the Random Forest (RF) algorithm with an optimization technique described in . RF is aTo assess the strength of the selected biomarkers for prediction of DM, and to investigate if genes involved in DM are similar across the three cancer types, first, we used only the selected genes in each of the groups as features to train multiple ML models for the task of DM prediction. Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and RF models were trained on datasets from each of the four  groups , and theTo further assess the strength of genes selected by the proposed method for predicting DM, under the same study design, we carried out an analysis to identify Differentially Expressed Genes (DEGs) between samples with DM and those without DM in each of the four groups. Instead of the TPM normalized values, raw counts of unstranded RNASeq were extracted from the TCGA RNASeq records. A sample information table was also generated. Differential Gene Expression (DGE) analysis was performed in R using the DESeq2 Bioconductor package. DESeq2 detects DEGs by normalizing raw count values of genes in the experimental groups, fitting negative binomial generalized linear models for each gene, and detecting significance by Wald test . ThreshoWe downloaded diagnostic pathology Whole Slide Histopathology Images (WSIs) of patients with DM in the TCGA projects of BLCA, HNSC, and PAAD from GDC Data Portal. The number of samples in each group reduced slightly after collating a list of only patients with available histopathology images, and RNASeq data, and clinical data. Number of samples with DM in BLCA, PAAD, and HNSC groups are 50, 44, and 18 respectively. Again, we downloaded random complementary WSIs  of patients without DM for each of the cancer types, and ensured no overlap between samples . Total nOne WSI was preprocessed per patient. Due to the typical large size of WSIs which hovers around 100000 pixels in both horizontal and vertical axis, we isolated representative regions within each WSI for analysis. First, a maximum of 500 random non-overlapping patches of size 512 * 512 pixels were extracted from each image at 20 × magnification. These were reduced to a maximum of 200 patches after checking for a tissue area of at least 80% in each patch. To ensure selection of tumor representative regions we imitated the concept of high cellularity described in  which as. Loss function was binary cross-entropy, and optimizer, Adam with a learning rate of 0.0001. Training epoch was set at 40.A DenseNet121 model, pretrained on Imagenet data, and KimiaNet  were cho. Loss fFor prediction on the test sets, patient level features were derived by passing all patches for each patient through the trained feature extractor models, and averaging all patch features from a slide. We trained traditional machine learning algorithm- SVM and a single layer MLP on the patient level features to classify samples as DM positive or DM negative . ConsideMultimodal concatenation of patient level features derived from the CNN models to log transformed values of TPM normalized RNASeq data of selected DM associated genes from above was carried out. For BLCA, and PAAD groups, the 50 genes selected using the (RF + Optimization) technique were combined with 32 features derived from histopathology images of each patient to train classifiers, and predict presence or absence of DM . 150 genLikely due to the effect of a larger training dataset given the weakly supervised training technique that was employed, it was observed from early results that image only models trained on data from the ALL3 group generally performed better in their predictions on the test sets than other groups. Similarly, multimodal models of genomic and histopathology image were generally better than unimodal model of either data type in the ALL3 dataset, while metrics of multimodal models are generally lower than either genomic or image only models in other individual cancer dataset . Therefore further experiments were carried out on the ALL3 dataset. We trained fully connected layers on KimiaNet, a DenseNet121 architecture model finetuned on about 250,000 histopathology images to extract features from the images in the ALL3 dataset. Metrics of multimodal models built from these features, and genomic data were compared to those from DenseNet121.Four variables—Age, T stage, N stage, and Number of lymph nodes positive by HE—were selected from the clinical data, and concatenated with genomic, and histopathology features to predict presence or absence of DM in the ALL3 dataset . As partFKBP6, ASIC5, MAPK8IP1, F11R, and PABPC5, while CTSV, BIRC5, SERPINA7, CST2, KLHL3 are the highest ranked genes in PAAD. In HNSC, TM4SF1, C19orf18, EXPH5, FKBP2, PTPRZ1 are the highest ranked genes, and ALL3 group had CHRNG, CPT1B, CGREF1, GPR31, SPTBN5. Past publications have confirmed the activities of some of these genes in the metastasis of various cancers, either as oncogene or as tumor suppressors , and simultaneous gene selection (top 50) in each of the groups . While Rpressors . There aactivity .Dataset in each group was randomly split into train and test set in a 75:25 ratio, and SVM with linear kernel, KNN, and RF models were trained on the highest ranked 30, 25, 20, 15, 10, 5, 3, 2, and 1 gene from list of selected top 50 genes. Outcomes of single instance predictions, and five-fold cross-validation on the test sets were recorded .p-value of 0.05, the number of DEGs in BLCA, PAAD, HNSC, and ALL3 are 229, 2142, 1100, and 658 respectively. Of these 5, 39, 19, and 13 overlap with the 50 genes selected using the ML method in each of the respective groups. Presence or absence of DM was predicted in the four groups using DEGs with the lowest 30, 25, 20, 15, 10, 5, 3, 2, and 1 adjusted p-value as variables. In almost all cases, genes selected using the ML (RF + optimization) techniques had higher evaluation metrics  than those selected via DESeq2 DGE analysis in the task of DM prediction . Highest mean AUROC score achieved over a five-fold cross validation was 0.87, 0.92, 0.97, 0.79 in BLCA, PAAD, HNSC, and ALL3 groups respectively, compared to 0.65, 0.80, 0.92, 0.62 with DESeq2 DGE analysis, when models were trained on top 15 selected genes (To test the strength of genes selected based on the (RF + Optimization) method as predictors of DM, a DGE analysis was carried out with the DESeq2 software package to identify DEGs between the DM and non-DM samples. With a threshold adjusted diction . Highesed genes .CGREF1, SPTBN5, TAS1R3, FAM241B, EL5, CSPG5, and MAPK8IP1), and 6 genes  overlap with those selected in BLCA, and PAAD respectively   models on BLCA, PAAD, and HNSC datasets, but with genes selected from the combined dataset of the three cancer types (3 group) . These m3 group) . To furt3 group) .DenseNet121 was trained on histopathology images from BLCA, PAAD, and ALL3 groups, after the HNSC group was dropped due to small sample size. One WSI was preprocessed per patient as described in the methods section. For each group, classification models—SVM, and MLP were trained on patient level features extracted by the trained CNN models, and prediction was carried out on the test set features . The imaEarly results showed that metrics of multimodal (Image + Genomic) models built from the ALL3 dataset generally improved on image, or genomic unimodal models by 1–3 percent margin, with genomic data contributing more to the multimodal metrics by a large margin. Following a five-fold Monte Carlo cross-validation, a multimodal (Image + Genomic) SVM classifier produced a mean AUROC score of 0.77, while the corresponding image and genomic unimodal models produced a score of 0.58, and 0.74 respectively. The mean AUROC scores from MLP classifiers are . In either BLCA or PAAD dataset, results of multimodal (Image + Genomic) models mostly improved on those of the image only models, however they were generally below that of genomic only models. The highest mean AUROC derived from a BLCA dataset multimodal (Image + Genomic) model is 0.62, and the corresponding values from image only, and genomic only model are 0.57, and 0.80 respectively. Similar pattern was seen in the PAAD dataset . These Based on these early results, we carried out further experiments with the ALL3 dataset. To evaluate the effect of using a CNN model with lower layers pre-finetuned on histopathology domain images as features extractor, fully connected layers were trained on KimiaNet with the ALL3 dataset histopathology images, and features derived from the model were combined with genomic data to predict DM in the test set. There was generally no additional advantage observed in the results of KimiaNet over those from DenseNet121. Highest mean AUROC from the MLP multimodal model (KimiaNet Image + genomic data) is 0.77. Unimodal image and genomic models from the same dataset produced AUROC scores of 0.55, and 0.80 respectively. The same pattern was seen with the SVM classifier  .Lastly, four clinical variables—Age, T stage, N stage, and Number of lymph nodes positive by HE were concatenated with genomic, and histopathology features to train SVM, and MLP classifiers. This led to an improvement of the mean AUROC score of the Image + Genomic SVM model from 0.77 to a score of 0.79. Mean accuracy, and F1 scores also improved from 0.67 and 0.67 to 0.71 and 0.71 respectively. MLP classifier however produced a mean AUROC score of 0.80 against the score of 0.81 achieved with Genomic + Histopathology data . CombiniZNF and FBKP family of genes which is present in the list of selected genes in the BLCA, and HNSC datasets have been severally associated with cancer progression, and metastasis in multiple cancers  outperform those built from genes selected by DESeq2 DGE analysis . While it is important to note that the datasets in our study are focused specifically on DM, similar studies on related tasks have been reported. Using just RF algorithm for gene selection in a breast cancer study , achieveThe reported AUC scores from our study were derived from the highest ranked 15 genes out of the initially selected 50. Perhaps a protein-protein interaction analysis including the initial 50 genes could lead to selection of fewer genes with higher relevance than the ranking we have employed here, which may in turn lead to improved prediction metrics. The biomarkers discovered in each cancer type using the strict ML approach we have employed should be further investigated for their potential as diagnostic indicators, and as basis for development of new therapies against cancer metastasis.Our results indicate that multimodal data  provides a predictive advantage over either of the unimodal data types in the task of DM detection, however the contribution of only gene expression data is highest by a wide margin. Similar studies have acknowledged the superiority of multimodal data for prediction of medical diagnosis and various outcomes in cancer patients , howeverThe highest mean AUC score of 0.79 derived from the SVM classifier in our study was from multimodal combination of gene expression, clinical, histopathology data, while the score of 0.81 derived from the MLP classifier was obtained from a genomic + histopathology image model. Overall, our results indicate that combining clinical, genomic, and histopathology image data increases the prediction metrics for DM, however genomic data alone is a strong contributor.