Due to concerns in public health and its negative impact on the pig industry the need for Influenza A virus (IAV) surveillance is rising. The gold standard procedure for detecting IAV is to sample acutely diseased pigs. Endemic infections with unspecific clinical signs and low disease prevalence need new approaches. Our study aimed to evaluate a standardized sampling procedure for the detection of IAV in epidemically and endemically infected farms. We performed a cross-sectional study in 131 farms investigating three different age groups per farm in 12 European countries. The results of our investigation indicate that 10 nasal swabs each in suckling piglets, weaners and middle of nursery is a valuable tool for influenza detection and identification of subtypes. However, for farms with a lower prevalence than 15% it is advisable to either increase the number of nasal swabs in each age group or to use group sampling methods. Interestingly, different subtypes were found in different age groups. Thus, our study underlines that sampling of different age groups is mandatory to obtain a comprehensive overview on all circulating variants on farm. In addition, our results highlight that sampling strategies should also consider piglets without obvious clinical signs for IAV infection.p = 0.048). Overall, the swIAV detection rate did not significantly differ between diseased or non-diseased suckling and nursery piglets, respectively; however, diseased weaners had significantly more positive pools than the non-diseased animals. Interestingly, in 9 farms, different subtypes were detected in different age groups. Our findings indicate that to detect all circulating swIAV subtypes on a farm, different age groups should be sampled. Additionally, the sampling strategy should also aim to include non-diseased animals, especially in the suckling period.Swine influenza A virus (swIAV), which plays a major role in the porcine respiratory disease complex (PRDC), is eliminated from the respiratory tract within 7–9 days after infection. Therefore, diagnosis is complicated in endemically infected swine herds presenting no obvious clinical signs. This study aimed to investigate the right time point for sampling to detect swIAV. A cross-sectional study was performed in 131 farms from 12 European countries. The sampling protocol included suckling piglets, weaners, and nursery pigs. In each age group, 10 nasal swabs were collected and further examined in pools of 5 for swIAV by Matrix rRT-PCR, followed by a multiplex RT-PCR to determine the influenza subtype. SwIAV was detected in 284 (37.9%) of the samples and on 103 (78.6%) farms. Despite the highest number of animals with clinical signs being found in the nursery, the weaners were significantly more often virus-positive compared to nursery pigs ( Streptococcus suis [Glaesserella parasuis [Influenza A infections in swine (swIAV), as part of the porcine respiratory disease complex (PRDC), can cause severe clinical signs that lead to decreased performance in growers and fatteners  and can cus suis  or Glaesparasuis , or withparasuis , can leaparasuis . Consequparasuis , samplesparasuis .The aim of the present study was to establish an efficient sampling approach for piglets on farms, either when an acute influenza outbreak is suspected or in an endemic course of the disease. To gain an overall overview of the infection dynamics, as well as the subtypes circulating on farms, a cross-sectional study investigating three different age groups was performed in 12 different European countries.A cross-sectional study was conducted in 12 different European countries. The farms were selected using the following inclusion criteria: farms with either acute clinical signs suggesting swIAV, including dyspnea, coughing, sneezing, nasal discharge, anorexia, and/or lethargy , or farms suspecting endemic swIAV circulation, due to either reproductive failure and fever in sows or recurrent respiratory distress in the nursery . Farm owners voluntarily participated in the investigation. Between January and December 2021, a total of 131 farms were examined once .The sampling protocol consisted of 10 nasal swabs in each of the following age groups: suckling piglets (1–4 weeks old), piglets around weaning (4–6 weeks old) and animals in the middle/end of the nursery period (7–9 weeks old). In the suckling period, one strong piglet per litter out of 10 litters was sampled. In piglets after weaning and in the middle/end of the nursery period, one piglet per pen and around 10 pens were sampled. The sample size was calculated using a fixed design prevalence of 15%, for a minimum of 95% probability of detection, assuming a diagnostic sensitivity and specificity of 90% and 100%, respectively. Therefore, the target number of sampled animals per farm was 30 animals within three age groups [®). Each tube contained a pool of 5 individual swabs, resulting in 6 pooled samples per farm. Plastic tubes were sent under cooling conditions (using ice packs) to the laboratory for RNA extraction and testing. In the laboratory, each pool of nasal swabs in the first step was investigated by real-time PCR (rRT-PCR) targeting the matrix gene of influenza A. Results with a cycle threshold (Ct) value < 38 were considered positive, and a Ct > 38 was considered negative. All positive samples with a Ct value below 30 were further investigated via multiplex RT-PCR to determine the influenza A subtype by hemagglutinin (HA) and neuraminidase (NA) gene amplification, targeting H1av, H1pdm, H1hu, H3hu, N1 , and N2. If only the HA could be determined, samples were counted as typable. IAV rRT-PCR and multiplex RT-PCR were performed according to the protocol of Henritzi et al. 2016 [The collection of nasal swabs was performed by inserting the swab 2–4 cm into both nostrils of each piglet and rotating it 360 degrees. Swabs were placed into a plastic tube with 2 mL of viral media , the farm size, which was categorized into three groups  , influenAll statistical analyses were performed using Stata 13.1 . The significance level was set at 0.05.p < 0.25 were offered to a full regression model at the same time, which was further reduced by backward elimination [Descriptive statistics of the collected data were produced. Chi-square tests were performed in order to investigate whether herd-level positivity differs according to the influenza status of the herd, vaccination status of the herd, and farm size. The investigation of the existence of an association between the rRT-PCR results and the collected information on potential risk factors was performed with the use of a three-level mixed-effect logistic regression model. The rRT-PCR result (either positive or negative) was the dependent variable, while the information/parameters collected were the independent variables and were offered as explanatory variables in the model. Recorded information included the age category of the piglets in the sampled pools, the influenza status of the herds , the farm size, influenza vaccination status, and clinical signs of the sampled animals . Our data were organized in a hierarchical structure; sampled piglets were clustered in farms and farms were clustered in countries; thus, random effect terms were incorporated to account for the within-herd and within-country correlation of observations resulting from the multilevel design of the study. For the screening process of candidate variables for multivariable modeling, a bi-variable approach was used, as suggested by Martin (1997) . The agemination  until onSubsequently, the above model was adapted accordingly and rerun thrice, within each piglet age group category, to investigate potential differences in the probability of occurrence of a positive rRT-PCR result between samples with and without apparent clinical signs, within samples from suckling piglets, weaners, and nursery piglets, respectively.Additionally, in order to investigate the existence of a potential difference in rRT-PCR-positive results with a cycle threshold (Ct) value below 30, between the different sampled age groups of piglets, a mixed-effects logistic regression model was employed. The dependent variable represented the positive rRT-PCR results with either a Ct value > 30 and <38 or <30. The procedure for model-building and the selection of explanatory independent variables followed that described above.Finally, as sampling was targeted primarily in piglets presenting clinical signs indicative of swine influenza, we were interested in investigating the degree of difficulty in finding these symptoms in the sampled age-group categories. In other words, we investigated potential differences in the probability of detection of the clinical signs between the sampled age categories of piglets. For this purpose, a three-level mixed-effect logistic regression model was employed, with the presence or absence of any clinical signs in the pool of sampled piglets as the dependent variable and the age-group category as the independent variable, while random effects terms at country and farm level were incorporated as well.A total of 131 farms fulfilled the inclusion criteria and could be incorporated into the present study. Overall, 750 pools of nasal swabs were analyzed for IAV.In the suckling period, the percentage of animals showing clinical signs indicative of IAV infection was 33.6% (87/259), whereas, in weaners, 64.7% (159/246) of the pooled samples originated from diseased animals. In the nursery, clinical signs were obvious in 80.4% (197/245) of the sampled animals .p = 0.771).Following the initial screening process, four variables, namely, the “age group category”, “herd size category”, “course of disease (acute/endemic)” and “clinical signs in the sampled pools” variables were eligible for and included in the full model. After the model-building processes, only the “age group category” and “clinical signs in the sampled pools” retained significance and remained in the final model. Their interaction was non-significant  more likely to find animals with clinical signs in the weaners compared to the suckling piglets, 28.37 times  more likely to find piglets with clinical signs in the nursery compared to the suckling period, and 3.46 times  more likely to detect animals with clinical signs in the nursery compared to weaners  and 76.7% (n = 69) of the endemic farms tested positive for Influenza A. The median herd size over all herds was 700 sows. Overall, 74.5% (n = 35) of the 47 farms ranging between 50 and 499 sows were positive for IAV, 68.2% (n = 30) of the 44 farms of between 500 and 999 sows, and 92.5% (n = 37) of the 40 farms between 1000 and 9000 sows. Sow vaccination (either reproductive or in a mass vaccination protocol) was implemented in 71 farms, of which 83.1% were IAV-positive, whereas of the 60 non-vaccinated herds, 80% were positive for IAV.IAV was found by rRT-PCR in 103 (78.6%) out of the 131 investigated farms, in at least one of the samples . Regardip = 0.913), the vaccination status (vaccinated/non-vaccinated sows) (p = 0.747), or the farm size (p = 0.485).There was no statistically significant association between influenza A positivity at the herd level and the disease status of a farm (epidemic or endemic) (p = 0.45) in terms of the odds of a positive PCR result, compared to suckling piglets (1–4 w.o.a.), and pooled samples from nursery pigs (7–9 w.o.a.) showed no difference (p = 0.28) in terms of the odds of a positive PCR result compared to suckling piglets. However, samples from weaners were 1.56 times more likely to be positive  compared to samples from nursery pigs  were positive for IAV. The percentage positivity for IAV for the suckling period, weaners, and nursery were 32.8 (85/259), 43.1% (106/246), and 38% (93/245), respectively. Pooled samples from weaners of 5–6 weeks of age (w.o.a.) showed no difference (ery pigs .p = 0.248), the vaccination status (vaccinated/non-vaccinated sows) (p = 0.72), and the farm size (p = 0.07).There was no statistically significant association between the influenza A positivity of samples and the disease status of a farm (epidemic or endemic)  more likely to be positive, compared to pooled samples from piglets without clinical signs. The results of the mixed-effects logistic regression models within each age group category detected no significant difference in the odds of a positive RT-PCR result, neither between pooled samples from healthy and clinically diseased suckling piglets  nor between pooled samples from healthy and clinically diseased piglets from the nursery . However, a statistically significant difference in the odds of a positive RT-PCR result between pooled samples from healthy and clinically diseased weaners (p = 0.031) was detected. Particularly, samples originating from weaners with clinical signs were approximately 7  times more likely to be positive, compared to weaners without clinical signs  of the positive samples from suckling piglets showed a Ct value below 30, allowing us to perform a subtyping multiplex RT-PCR. Of the weaners, 60.4% (64/106), and in the nursery, 50.5% (47/93) of the positive samples showed Ct values below 30.p = 0.051, p = 0.069, and p = 0.969), in suckling piglets vs. weaners, in suckling piglets vs. nursery, and in nursery vs. weaners, respectively, and between pooled samples with or without clinical signs (p = 0.183).In the mixed-effect logistic regression model for the investigation of the association of the probability of positive rRT-PCR results with either a Ct value > 30 and <38 or <30, with potential risk factors among the recorded parameters, only age group and clinical signs in the sampled pools were eligible for inclusion in the full model. However, none of these variables retained significance at the 0.05 level in the final model. Thus, there was no difference in the odds of occurrence of a Ct value > 30 and <38 or <30 among positive samples, both between any age group category (In 78 (75.7%) of the 103 IAV rRT-PCR-positive farms, one or more subtypes could be identified by multiplex RT-PCR. In 66 of these farms, only one subtype was detected. In 48 farms, this subtype was found in only one of the three investigated age groups; in 14 farms, the subtype was detected in two age groups, and in 4 farms, it was detected in all three age groups. In 12 farms, two subtypes were identified. From these findings, the two subtypes were detected in the same age group in 3 farms, whereas, in the other 9 farms, the subtypes were circulating in different age groups see the .Out of the 285 rRT-PCR-positive samples, 165 (57.9%) samples with a Ct value below 30 were tested by multiplex RT-PCR to detect the subtype. The highest subtyping rate was found in the suckling piglets. The majority of all positive samples, 55.8% (48/86), could be subtyped, compared to 54.7% (58/106) and 44.1% (41/93) of subtyped samples in weaners and the nursery, respectively. The different subtypes detected in this study are displayed in Diagnosis of swIAV can be very challenging, particularly in endemically infected herds with rather unspecific clinical signs and a low prevalence within the herd . The useThe detection rate in sows is regarded as low, due to frequent exposure and the development of immunity ,29. AddiUntil now, the sampling of animals with clinical signs has been propagated. However, in endemic scenarios with rather unspecific clinical signs and a low disease prevalence, finding diseased swIAV-positive animals can be challenging. The results of our study suggest that the approach of focusing on clinically diseased animals must be scrutinized critically as, despite the highest number of animals showing clinical signs being in the nursery, the weaners were significantly more often swIAV-positive, compared to the nursery pigs. The high percentage of nursery pigs with clinical signs may be explained by the fact that swIAV, as part of the PRDC complex , predispIt has been shown in previous studies that pigs around weaning age are an appropriate target population for swIAV sampling ,35,36,37The design of effective control measures for swIAV requires thorough knowledge of all circulating strains on the farm. As the protection of the most commonly used whole-virus-inactivated (WIV) vaccines is primarily linked to the HA , therefoSubtyping was performed to assess the number of subtypes circulating on the farm and to give advice regarding the sampling approach for the detection of all subtypes circulating on the farm. More than one subtype was found in 11 (10.7%) of the positive farms. The simultaneous detection of multiple strains in the same population is not new and has been described before ,39,40,47The sample size used in this study was calculated using a fixed design prevalence of 15%, for a minimum of 95% probability of detection, and assuming a diagnostic sensitivity and specificity of 90% and 100%, respectively. This led to a target number of 30 sampled animals per farm, which were taken from three age groups ,48, alloHowever, it is crucial to consider that all included farms had a history of respiratory distress and/or reproductive failure; thus, in farms with rather nonspecific clinical signs and/or a low intraherd prevalence, a higher sample size is needed to detect the virus. In these cases, the use of group methods can present a cost-effective alternative sampling strategy to testing individual samples. However, in order to identify the subtype/s, and if needed for sequencing purposes, individual samples are superior to group samples .This article provides new insights into sampling approaches to detect swIAV on sow farms. The results of our investigation indicate that for influenza A diagnosis, the use of 10 nasal swabs each in suckling piglets, weaners, and in the middle of the nursery stage is a valuable tool for influenza detection and the identification of subtypes in endemic and epidemic farms, independent of farm size and the vaccination status of the sows. However, for farms with an expected prevalence of lower than 15%, it is advisable to either increase the individual sample size or use additional group-sampling methods. Furthermore, our study underlines that the sampling of different age groups is mandatory to obtain a comprehensive overview of all circulating variants on a farm. It additionally highlights the fact that sampling strategies should also consider piglets without obvious clinical signs.


Enterobacteriaceae with great conjugation ability, high stability, no fitness cost, and the ability to improve biofilm formation in their bacterial hosts. IncX3 plasmids have spread swiftly, primarily in several nations and among different species over the last 10 years. NDMbla, KPCbla, and OXA-181bla are the carbapenemase genes carried by IncX3 plasmids. Among them, NDMbla is often located on the IncX3 plasmid, which is deemed as the primary vehicle of NDMbla transmission. Isolates harboring IncX3 plasmids are found in nations all over the world from human, animal, and environmental sources. Cointegrate plasmids related to IncX3 have recently been discovered to increase the antibiotic resistance spectrum and potentially broaden the host range of plasmids, restricting the use of antibiotics in the clinic. There are, however, few reviews based on the physiological and epidemiological properties of IncX3 plasmid, as well as studies on the plasmid itself. Hence, we conducted a retrospective literature review to summarize the characteristics of IncX3 plasmids aiming to provide a theoretical basis for controlling the global prevalence of IncX3 plasmids and directions for further research on the functions of the related genes on the IncX3 plasmid.IncX3 plasmids are narrow host range plasmids mostly found in  Enterobacteriaceae (CRE) have become common and pose a great threat to human health. Phenotypic resistance to carbapenems is complex and mainly caused by four mechanisms, including (1) β-lactamase activity combined with structural mutations; (2) production of carbapenemases, enzymes that hydrolyze carbapenem antibiotics; (3) activation and massive expression of drug efflux pump system; and (4) alterations in penicillin-binding proteins. The most typical mechanism is carbapenemase production  dissemination methods. Compared with chromosomes, genes encoding carbapenemases are generally found in incompatible plasmid groups . A number of plasmid replicon groups in CREs have been reported globally, such as IncF, N, X, A/C, L/M, R, P, H, I, and W. Compared with other Inc. groups, IncF, A/C, and X are the most commonly associated with carbapenemase production.Plasmid incompatibility refers to the inability of two plasmids to coexist in the same cell, resulting in the loss of one plasmid in daughter cells, which is determined by replication machinery. In taxC gene was used as a target to successfully identify the first IncX3 plasmid that represents the prototype IncX3 plasmid  summarize the global epidemiological and molecular characteristics of IncX3 over the last 10 years, providing a theoretical basis for controlling the spread of IncX3 plasmids; and (2) organize the literature that have studied the physiological characteristics of IncX3 plasmids, providing directions and ideas for future IncX3 plasmid research.Publications chosen for this review were found on PubMed using the keywords “IncX3” as search criteria. As of May 21, 2022, a total of 273 articles were retrieved.Isolates harboring IncX3 plasmids are found in many countries around the world , includiThe strains carrying IncX3 plasmids are from a rich source, mainly divided into clinical and non-clinical sources, with clinical sources accounting for two-thirds of the total . Among tNDM-5bla-producing Escherichia coli ST167 in both owners and companion animals suggests potential transmission between humans and dogs . The most popular E. coli with distinct sequence types are ST410, ST167, ST48, and ST101. In Chinese clinical settings, ST167 E. coli strains had close ties to NDM-5bla, and high-level tigecycline resistance Escherichia coli carrying NDM-5bla also belonged to the ST167 clonal lineage , partitioning (parA and parB), plasmid maintenance , conjugation/type IV secretion system , transcriptional activator (actX), and putative DNA transfer proteins (taxA and taxC)  . A retrond taxC) . The accnd taxC) . In a stin China , a totalIncX3 plasmids have lower fitness costs. A study using the typical IncX3 plasmid pNDM-HN380 suggests that the IncX3 plasmid affected the motility of the host in addition to conferring multiple antibiotic resistance. Furthermore, the plasmid-encoded new sRNA IGR plas2 can influence fucose metabolism and biofilm formation to increase strain survival in a competitive environment . To our NDM-5bla-carrying plasmids in one K. pneumoniae clinical isolate showed high stability in clinical isolates, with no apparent plasmid loss after repeated cultures for 5 days , and auxiliary genes were found in the basic structure of pKB11. Although this novel tiny plasmid lacks conjugation genes, the host of pKB11 contained additional plasmids from various incompatibility groups, which may aid the transfer of this novel IncX3 plasmid to a new host. Following that was an unnamed3 plasmid (accession number: MN847777) with 18,178 bp but no conjugation genes , is 12,757 kb in size . Only seon genes . This is plasmid . The 75,ronments .NDMbla had the highest prevalence, followed by OXA-181bla and its variants, KPCbla, and SHVbla. Interestingly, the majority of gene types carried on plasmids in China are NDMbla and its variations. Except for NDM-4bla, all NDMbla variants located on IncX3 plasmids can be found in China. And no IncX3 plasmid carrying KPCbla was recorded in China. The frequency distribution of carbapenemase genes reported in different nations and species covered by this review are shown in All IncX3 plasmids cited in this review carry ARGs, the majority of which are connected to carbapenemase genes. NDMbla-bearing IncX3 plasmids descended from a common ancestor plasmid through a series of mutations. The easy propagation of the IncX3 plasmid could be responsible for the spread of various NDM variants in Enterobacteriaceae isolates   , which i−9 and 10−2 –like protein that stabilizes plasmid DNA and regulates plasmid transfer at different temperatures . Furtherplasmids . The rol’s study . The autStrains carrying IncX3 plasmids tend to carry more than one plasmid. Inc. typing is a method of classifying plasmids based on their ability to coexist stably with other plasmids in the same bacterial strain, determined by their replication machinery. When co-resident plasmids have the same replication mechanisms, they are incompatible . The numKlebsiella pneumoniae was observed by the HIN Hospital in the United States, which could be a means for plasmids to seek stable forms in evolution , which was resistant to practically all antibiotics, including tigecycline, tetracyclines, carbapenems, cephalosporins, fluoroquinolones, and aminoglycosides (except amikacin)  . The fusmikacin) .NDMbla, KPCbla, and OXAbla), particularly NDMbla. IncX3 plasmid is not only a vehicle for spreading NDMbla but also a platform for evolution. Many NDM variations, particularly NDM-5bla, can be found in IncX3. IncX3 plasmids have been isolated from different human, animal, and environmental strains worldwide, especially in China. The plasmids are primarily found in E. coli, K. pneumoniae, C. freundii, and Enterobacter cloacae. There are usually 1–3 co-resident plasmids in the IncX3 plasmid, with the transfer frequency ranging from 10−9 to 10−2 and the size of the plasmid usually ranging from 30 to 60 kb. It can be used as a co-resident plasmid to form a fusion with a non-self-transfer plasmid, and IncX3 plasmids could help expand the resistance profile and disseminate resistance genes. The structure of the IncX3 plasmid is divided into two parts: the backbone and the accessory module. The backbone is highly conserved, and its related genes play an important role in the stability and spread of the plasmid. More research into the physiological aspects is still needed. Based on this, future research should focus on the physiological role of the plasmid structure to provide a theoretical basis for controlling the global prevalence of IncX3.Carbapenem resistance is a clinical problem to be solved urgently. The ability of antibiotic-resistant strains to produce carbapenemase is an important mechanism of carbapenem resistance, while plasmids play an important role in spreading and carrying antibiotic resistance genes. Among these, the IncX3 plasmid has low fitness costs, high stability, high transfer frequency, and improved strain biofilm-forming capabilities. The IncX3 plasmid is crucial for transporting and spreading carbapenemase genes  and the Youth Innovation Fund project of the First Affiliated Hospital of Zhengzhou University (no. YNQN2017168).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.


Obesity is a chronic disease in which abnormal deposition of fat threatens health, leading to diabetes, cardiovascular diseases, cancer, and other chronic illnesses. According to the WHO, 19.8% of the adult population in Italy is obese, and the prevalence is higher among men. It is important to know the predisposition of an individual to become obese and to respond to bariatric surgery, the most up-to-date treatment for severe obesity. To this purpose, we developed an NGS gene panel, comprising 72 diagnostic genes and 244 candidate genes, and we sequenced 247 adult obese Italian patients. Eleven deleterious variants in 9 diagnostic genes and 17 deleterious variants in 11 candidate genes were identified. Interestingly, mutations were found in several genes correlated to the Bardet–Biedl syndrome. Then, 25 patients were clinically followed to evaluate their response to bariatric surgery. After a 12-month follow-up, the patients that carried deleterious variants in diagnostic or candidate genes had a reduced weight loss, as compared to the other patients. The NGS-based panel, including diagnostic and candidate genes used in this study, could play a role in evaluating, diagnosing, and managing obese individuals, and may help in predicting the outcome of bariatric surgery. I40 kg/m2 . Obesity40 kg/m2 . Nonsurg40 kg/m2 , while s40 kg/m2 . Nonsurg40 kg/m2 ,11,12,13MC4R, LEP, LEPR, POMC and PCSK1) [Obesity is usually due to an imbalance between energy consumption and energy expenditure, but it can also be caused by genetic, environmental, psychological and economic factors . The gend PCSK1) ,21,22; sd PCSK1) ; polygend PCSK1) . In the d PCSK1) ,28,29,30d PCSK1) .A patient’s genetic risk score can be used to determine individual predisposition to obesity . MoreoveHere, we report the first Italian study and one of the first in Europe to evaluate the use of NGS in predicting the outcome of bariatric surgery. We performed an analysis of 316 genes by target NGS, including 72 diagnostic genes and 244 candidate genes, in 247 adult obese patients. Target sequencing of a panel of genes including diagnostic and candidate genes may be useful in evaluating, diagnosing, and managing obese individuals, and may predict the outcome of bariatric surgery.We analyzed obese Italian adults with BMI ≥ 30. All patients underwent pre-test counselling, during which clinical data—including personal and family history—were collected. The patients were informed about the significance of genetic testing. All of them gave their written informed consent, in compliance with the Declaration of Helsinki. Ethical approval and clearance were received from the Ethical Committee of Azienda Sanitaria dell’Alto Adige, Italy . Genomic DNA was isolated from peripheral blood using a commercial kit ) according to the manufacturer’s instructions.https://www.twistbioscience.com/ (accessed on 1 September 2022)). Genes were divided into diagnostic and candidate genes, as previously proposed by the laboratory [We designed an NGS panel of 316 genes, comprising 72 diagnostic genes and 244 candidate genes that are linked to obesity, food intake regulation, energy homeostasis, and lipid metabolism . The genboratory . Diagnosboratory ,48,49,50boratory ,52. DNA Fastq (forward–reverse) files were obtained after sequencing. The sequencing reads were mapped to the genome using Burrow-Wheeler Aligner (version 0.7.17-r1188) software. Duplicates were removed using SAMBAMBA (version 0.6.7) and MarkDuplicates GATK (version 4.0.0.0). The BAM alignment files generated were refined by local realignment and base quality score recalibration, using the RealignerTargetCreator and IndelRealigner GATK tools. Minor allele frequencies (MAF) were retrieved from the Genome Aggregation Database . In silit-test. The test was two tailed and a p value < 0.05 was considered as statistically significant.Categorical data were expressed as absolute or as frequencies. Continuous variables were presented as mean value and standard deviation. Differences between the two groups were analyzed by using the independent samples We enrolled 247 obese patients , with a median age of 48 ± 11 years, who were analyzed with a NGS panel of 316 genes, comprising 72 diagnostic genes and 244 candidate genes. Twelve patients carried deleterious variants in diagnostic genes. Another 21 patients carried at least one deleterious variant in candidate genes. In particular, 11 heterozygous deleterious variants in 9 diagnostic genes , and 17 heterozygous deleterious variants in 11 candidate genes  were detected. The genetic variants identified in the diagnostic genes are reported in Following the genetic analysis, 25 patients agreed to undergo a follow-up of 6 and 12 months to evaluate their weight loss after bariatric surgery. All the 25 patients completed the follow-up. Patients were subsequently divided in two groups, based on the pathogenicity classification of the genetic variant identified by the NGS sequencing: Non-deleterious  and Deleterious , suggesting a role of genetic variants in bariatric surgery outcome [In this study, we sequenced the genome of 247 patients with severe obesity before they underwent bariatric surgery. Among them, 25 were evaluated for their weight loss at 6 and 12 months after surgery and were divided in two groups. Considering that the weight and BMI of the two groups were not similar, we evaluated the %WL instead of the absolute weight loss. As can be seen from  outcome .Moreover, many of the diagnostic genes that were found mutated are correlated to the Bardet–Biedel Syndrome (BBS). BBS is an autosomal recessive syndrome that is characterized by several symptoms, among which are intellectual disability and obesity . Among tAll the genes in which likely pathogenic variants were identified are correlated to the onset of obesity ,75,76,77Our results support already published scientific articles ,37, and The major limitation of this study is the low number of patients recruited for the assessment of weight loss. Nevertheless, this is the first Italian study and one of the first in Europe that evaluates the connection between genetic obesity and bariatric surgery outcomes. Moreover, a variable that should be taken into account is the type of bariatric surgery that was performed on each patient. In considering these limitations, our results could spur newest studies with higher cohorts of patients, which will foster the role of genetics in affecting the outcome of bariatric surgery.We genetically screened 247 severe obese patients and we clinically followed 25 of them to assess their weight loss after bariatric surgery, the most up-to-date treatment for severe obesity. In the meantime, we performed in silico modelling studies of the likely pathogenic variants, which supported their deleterious effect on the protein structure. It resulted that patients carrying likely pathogenic variants had a reduced weight loss. Considering that more research on a bigger cohort and with similar types of bariatric surgery is needed to support our findings, we propose that testing the genetics of severe obesity patients before performing bariatric surgery could be useful in the clinical management of patients with severe obesity.


Breast cancer (BC) is the most common cancer in women, which is also the second most public cancer worldwide. When detected early, BC can be treated more easily and prevented from spreading beyond the breast. In recent years, various BC biosensor strategies have been studied, including optical, electrical, electrochemical, and mechanical biosensors. In particular, the high sensitivity and short detection time of electrochemical biosensors make them suitable for the recognition of BC biomarkers. Moreover, the sensitivity of the electrochemical biosensor can be increased by incorporating nanomaterials. In this respect, the outstanding mechanical and electrical performances of graphene have led to an increasingly intense study of graphene-based materials for BC electrochemical biosensors. Hence, the present review examines the latest advances in graphene-based electrochemical biosensors for BC biosensing. For each biosensor, the detection limit (LOD), linear range (LR), and diagnosis technique are analyzed. This is followed by a discussion of the prospects and current challenges, along with potential strategies for enhancing the performance of electrochemical biosensors. Cancer presently ranks among the deadliest diseases in the world ,2,3. In A particularly effective technique for the early diagnose of BC based on the detection and quantification of BC biomarkers is provided by an electronic device called a biosensor ,12,13,142-bonded carbon atoms that occurs within a carbon material [As a two-dimensional (2D) nanomaterial, graphene (Gr) has contributed greatly to the electronic and biosensor industries ,24,25. Tmaterial ,27,28. Gmaterial ,30. The material ,32. Othematerial ,34,35,36material ,38. Actumaterial ,40.The present review presents a compressive overview of the various electrochemical approaches available for the biosensing of BC biomarkers, with a particular emphasis on the graphene materials. The analytical performance of each biosensor, especially the LR and LOD, are discussed, and the final section discusses the challenges and successes of graphene-based biosensors for BC sensing.2O2 [A biomarker is an objective measurement and evaluation characteristic that can be used to determine whether an organism or pathogenic process is normal, or if therapeutic intervention is required ,42,43. I2O2 ,50,51,52Due to the growing number of BC patients, electrochemical biosensors have received increased consideration for the detection of BC biomarkers ,54,55. ISince its discovery in 2004, graphene has revolutionized a variety of scientific fields such as electronics, nanocomposites, and biosensing ,63. The The microRNAs (miRNAs) play a role in many biochemical processes , and areMany studies have reported that graphene can be used as a biosensor material for detecting BC biomarkers. For example, Pothipor et al.  designedBreast cancer 1 (BRCA1) is a human caretaker gene found in breast and other tissues . Several−1 toward CEA detection and LR ranging from 5 pg mL–1 to 50 ng mL–1.Carcinoembryonic antigen (CEA) is a broad-spectrum biomarker that is elevated in many types of cancer, including gastric, breast, liver, and pancreatic cancers . PositivAlthough graphene is relatively novel, GO was shown in the initial studies on the graphite chemistry , synthes2)/AuNPs nanocomposite. MoSe2 is an excellent material for sensing applications due to its high specific surface area and fast charge transfer [2 nanosheets, thereby indicating that the selenium in the 2D-MoSe2 structure is intrinsically conductive [–1 for CA 15-3, and 1.2 fM miRNA-21.The use of GO in the construction of biosensors for detecting BC biomarkers has been reported by many groups. Recently, Pothipor et al.  developetransfer . The peanductive . Meanwhinductive . ConsequVarious types of cancer are caused by abnormal glycosylation and overexpression of MUC1, a transmembrane glycoprotein found on the apical surface of epithelial cells ,118,119.−1 to 50 μg mL−1, with LOD = 2 fg mL−1.The differentiation-44 (CD44) antigen is a transmembrane glycoprotein found on cell surfaces . SeveralAn attractive feature of GO is its ability to reduce to graphene-like nanosheets when the oxygen-including groups are removed, and the conjugated structures are recovered. An rGO sheet is typically referred to as a chemically-derived graphene . A reduc–1, while that in the cancer patient blood changes from 15 to 75 ng mL–1 [–1 and 1 ng mL–1, and an LOD = 0.05 pg mL–1. In another study, Augustine et al. [3) anchored onto rGO, and found that rGO nanosheets with edgelike defects accelerated the charge transfer at the electrode–solution interface, thereby acting as electron promoters. MoO3 exhibits a number of promising properties, including its versatile chemical and physical properties, its variable oxidation state, a high catalytic activity, an extremely wide band gap, great electrical properties, as well as a high level of biocompatibility [3 nanorods further increased the loading capacity of biomolecules, thus allowing for better interactions between the electrode surface and the goal analyte. The offered biosensor displayed a wide LR of 0.001−500 ng mL−1, along with an LOD of 0.001 ng mL−1.Many teams have reported the usage of rGO and its nanocomposites as biosensor materials for the detection of BC biomarkers. For example, measurement of the biomarker human epidermal growth factor receptor 2 (HER2) is applied as a clinical test for the detection of BC . It has  ng mL–1 . Consequ ng mL–1  modifiede et al.  presentetibility ,165. Add–1, whereas levels higher than 100 U mL–1 can show the presence of BC [Carbohydrate antigen 15-3 (CA15-3) plays a vital role as a biomarker in the diagnosis of BC. A healthy individual has a serum CA15-3 concentration of less than 30 U mLce of BC . Moreovece of BC . For exace of BC , applyince of BC . CuS nanct) of the CuS/rGO/SPE was determined by measuring the diameter of the semicircle in the impedance spectrum, and was lower than that of the rGO/SPE and the bare SPE due to the superior electrical features of the CuS/rGO nanocomposites, which resulted in a large electron conduction pathway. In another work, Shawky et al. [2/rGO) ternary nanocomposite for the fabrication of an electrochemical biosensor. The synthetic process is shown schematically in 2 have made it an attractive biosensor material [2/rGO composite, the as-prepared biosensor had an ultrahigh electrochemical sensitivity and superior activity toward the quantification of the BC antigen CA 15-3, with LOD = 0.07 U mL–1 and LR of 0.1 to 300 U mL–1.The electron transfer resistance  is a vital ROS that is associated with many physiological and pathological processes, and can cause numerous illnesses such as cancer, Alzheimer’s, heart attacks, and Parkinson’s disease by damaging the proteins, lipids, and DNA within cells [2O2 than normal cells, H2O2 can act as a biomarker for detecting cancer cells [2O2 effectively and accurately in living cells in order to monitor its concentration and understand the relevant biological processes. For example, Dong et al. [2O2 by modifying a GCE with Au-Pd nanocube nanocomposites that were assembled on rGO, and demonstrated that the resulting adjustment of the electrode surface with 3D nanostructures, and the increased attendance of active sites, greatly improved the electrocatalytic H2O2 reduction performance of the electrode. The latter effect was qualified to the huge electroactive surface and superior electrical conductivity of the 3D nanocomposites. The resulting biosensor exhibited a low LOD of 4 nM, and a wide LR ranging from 0.005 μM to 3.5 mM. Similarly, Li et al. [2O2 by modifying a GCE with ZnMn2O4/rGO-wrapped rGO microspheres to provide a high surface area and effective conduction channels, thereby increasing both the conductivity and H2O2 reduction ability. Due to the synergistic effects of the ZnMn2O4 and rGO, the resulting ZnMn2O4/rGO-based biosensor exhibited a wide LR of 0.03−6000 μM, and an LOD of 0.012 μM.Many cancer cells are typified by the excessive presence of reactive oxygen species (ROS) as compared to their healthy counterparts ,170. In in cells ,172. Furer cells . Hence, g et al.  presentei et al.  preparedQuantum dots (QDs) are inorganic semiconductors with unique optical and electronic properties, and have generated significant interest over the past two decades ,175. In 6 cells mL–1 and a low LOD of 1 cell mL–1. Meanwhile, Hasanzadeh et al. [The attention of MCF-7 is another key biomarker for the early identification of BC, as a luminal-A subtype of the BC cell line. Hence, Tran et al.  designedh et al.  developeResearchers have been developing biosensors for detecting biomarkers of BC in recent years. Nanotechnology and new technologies have made biosensors an indispensable and useful tool for detecting BC. Detecting BC biomarkers using electrochemical biosensors has made remarkable progress in recent years due to nanotechnology and biosensor techniques. The present review has discussed the recent advancement in graphene-based biosensors, including the design strategies and results for the sensing of BC cells. The inherent features of graphene and its family were discussed, along with their applications in the biosensors of BC cells. Graphene oxide (GO) nanosheets modified with -COOH can be used to immobilize biomolecules more efficiently. Its functionalized groups make GO ideal for immobilizing bioreceptors on its surface. As compared to pristine graphene, reduced graphene oxide (rGO) contains a large number of defects. In contrast to popular belief, heterogeneous electron transfer occurs at the edges and defects of sp2 carbons, not at the basal plane. There is a wide variety of uses for rGO, such as drug delivery systems, reinforcing nanocomposites, increasing electrical properties, and especially as biosensors for measuring cancer biomarkers. Graphene quantum dots (GQDs) are new graphene-based nanomaterials used in a wide range of applications, including drug delivery, bioimaging, and biosensors. The development of GQD-based electrochemical biosensors to detect various analytes has received considerable attention in recent years. Numerous graphene-based nanomaterials were developed for use in biosensor devices, while probes such as DNA, antibodies, aptamers, proteins, or small organic molecules are used to recognize a selected target.In order to commercialize the developed biosensor for detection of BC biomarkers, the main challenges must be overcome first. While many graphene-based biosensors have been reported as stable and repeatable, other biosensors fail to achieve the desired detection results when used with real samples. This is mainly because the interfacial reactions between the graphene and various biological and chemical molecules are non-specific towards the desired targets. Moreover, the preparation of biological samples is necessary before the final tests, and involves potentially time-consuming and complicated procedures such as separation or preconcentration. On the other hand, dispersing graphene nanoparticles in other materials can be challenging and requires functionalizing the graphene surface, which increases the cost of biosensor manufacture. BC detection can be greatly improved by the discovery of specific and sensitive biomarkers. In spite of this, there has been little progress in the identification of new biomarkers and the development of highly sensitive and reproducible biosensors for early BC detection. Additionally, commercializing R&D technology may require more time and funding. In spite of several publications, developing substrates that are highly specific to BC biomarkers can be a challenge since most biomarkers are also found in other cancers. Therefore, developing strategies to simultaneously detect multiple biomarkers and avoid false positives is another important challenge for electrochemical biosensors. Because of the low concentration of most BC biomarkers, it is also possible that BC biosensors do not cover the whole range of biomarker concentrations. Therefore, continuous efforts are needed to overcome these challenges in order to produce large-scale graphene-based biosensors. Currently, efforts are being made to develop novel biosensors to detect BC cells to address these challenges. Ultimately, this study will aid researchers in the development of biosensors that can detect BC biomarkers in the early stages and improve cancer prognosis and treatment.The graphene-based nanomaterials may become a base for future contributions. For example, it could be used in a wide range of biosensor applications for early and faster diagnosis. Indeed, they could be used in liquid biopsy by detecting Circulating Tumor DNA (ctDNA) in a patient’s blood. In a new study, researchers from the University of Illinois at Urbana-Champaign found that “crumpled” graphene makes biosensors extremely more sensitive to DNA . Moreove


Treat-to-target (T2T) is a main therapeutic strategy in rheumatology; however, patients and rheumatologists currently have little support in making the best treatment decision. Clinical decision support systems (CDSSs) could offer this support. The aim of this study was to investigate the accuracy, effectiveness, usability, and acceptance of such a CDSS—Rheuma Care Manager (RCM)—including an artificial intelligence (AI)-powered flare risk prediction tool to support the management of rheumatoid arthritis (RA). Longitudinal clinical routine data of RA patients were used to develop and test the RCM. Based on ten real-world patient vignettes, five physicians were asked to assess patients’ flare risk, provide a treatment decision, and assess their decision confidence without and with access to the RCM for predicting flare risk. RCM usability and acceptance were assessed using the system usability scale (SUS) and net promoter score (NPS). The flare prediction tool reached a sensitivity of 72%, a specificity of 76%, and an AUROC of 0.80. Perceived flare risk and treatment decisions varied largely between physicians. Having access to the flare risk prediction feature numerically increased decision confidence (3.5/5 to 3.7/5), reduced deviations between physicians and the prediction tool , and resulted in more treatment reductions (42% to 50% vs. 20%). RCM usability (SUS) was rated as good (82/100) and was well accepted (mean NPS score 7/10). CDSS usage could support physicians by decreasing assessment deviations and increasing treatment decision confidence. RA is a chronic inflammatory disease that leads to joint damage and bone destruction, causing severe pain, disability, and reduced life expectancy ,2,3. TreIn clinical practice, patients and rheumatologists currently have little support in making the best treatment decision. Various studies have suggested predictive factors for RA flares that should be considered, such as remission duration, anti-citrullinated protein antibody (anti-CCP) status, and multi-biomarker disease activity (MBDA) score ,8,9. HowClinical decision support systems (CDSSs) may offer solutions to these challenges. CDSSs have been shown to improve clinical practice, medication dosing, preventive care, and other care aspects in a wide range of medical disciplines ,13, and The aim of this study was to investigate the accuracy, effectiveness, usability, and physician acceptance of an AI-powered flare prediction RCM to support the management of RA.The RCM, a CDDS, was used as the software prototype version 1.0.74. The RCM consists of two parts: (i) a floating patient overview and (ii) a flare risk prediction tool. The patient overview displays the patient’s history , smoker status, disease duration, comorbidities, anti-CCP status, disease status, last CRP (C-reactive protein) and ESR (erythrocyte sedimentation rate) values, and a visual timeline including DAS28-ESR  disease activity, and medication). The prediction tool displays the predicted risks of a disease flare for the RA patient in sustained remission within a period of 14 weeks as percentage bar graphs for two scenarios: the continuation of current medication vs. a half dosage of current medication.The flare risk prediction tool is a machine learning model that was developed based on data from clinical routines (73 RA patients) and RETRO studies (40 patients) . The RETThis study was approved by the ethics committee of the Medical Faculty of the Friedrich-Alexander-Universität Erlangen-Nürnberg , and informed consent was obtained from all subjects involved in the study.To ensure that the selected patient cohort was comparable to the cohort that was used for the originally developed model, we applied the same selection criteria. All patients met the following three criteria: (1) RA patients in sustained remission, defined as a DAS-28 ESR of less than 2.6 for at least 6 months; (2) patients receiving bDMARDs or biosimilars; and (3) time between two included visits was equal to or less than 14 weeks. Patient characteristics were retrieved from medical records. n = 5 physicians first without access (T1) and then with access to the Rheuma Care Manager (RCM) (T2) provided a flare risk prediction, a treatment decision, and their confidence in the treatment decision. Their attitude towards technology and their user experience were surveyed using various pre- and post-session questionnaires.The study was divided into two main parts , study pFlare prediction accuracy was estimated as sensitivity, specificity, positive and negative predicted values, and the area under the receiver operating curve (AUROC). An average cohort flare risk of 23% was used as a cut-off as described in other studies  to transPhysicians were recruited at the University Hospital Erlangen and were provided a usage guide for the RCM including the flare risk prediction tool. The guide gave an overview about its general structure, functions, and user interface (UI) elements. Attitudes towards technology and AI were surveyed in pre-and post-session questionnaires. Physicians were asked to fill out a baseline questionnaire assessing their personal attitude towards AI and general acceptance of a CDSS in rheumatology using the “Affinity for Technology Interaction (ATI) Scale” developed by Franke, Attig, and Wessel (2019)  and the The ATI scale comprises 9 items  and responses are provided on a 6-point response scale ranging from 1 (‘Completely disagree’) to 6 (‘Completely agree’). Negatively worded items were recoded prior to the computation of mean scores across all 9 items, i.e., higher scores represented a higher affinity for technology.The GAAIS consists of a positive subscale comprising 12 items and an 8-item negative subscale based on a 5-point rating scale from 1 (‘Strongly disagree’) to 5 (‘Strongly agree’). For data analysis, items on the negative subscale were inverted, and individual mean scores were calculated for each subscale separately so that higher scores indicated a more positive attitude towards AI .Flare prediction with and without access to the AI-powered flare risk prediction tool was compared in terms of flare risk, patient features considered most relevant for flare prediction, therapeutic decisions, and confidence.At T1, all study participants were presented the data subset of 10 RA patients, with access restricted to the RCM overview feature (T1) and no access to the actual AI-powered flare risk predictions. After studying each case, physicians completed a feedback form to assess their therapeutic decision, perceived flare risk, and confidence in treatment decision, see During the second part (T2), participants evaluated the same set of 10 patients and completed the same feedback form, now additionally having access to the prediction feature. Physicians completed a questionnaire assessing their affinity for technology, attitude towards AI, perceived system usability, and acceptance of the RCM. In addition, participants could provide feedback on RCM advantages and barriers and provided their basic demographic information and years of professional experience in rheumatology.To provide an individual estimation of a patient’s flare risk at T1, physicians were asked to estimate the risk of a flare within the following 3 months if medication is not adjusted and if medication dosage is cut in half. Responses were given as percentages. Participants had the chance not to answer this question if they felt they could not make an estimation at all. Similarly, at T2 (access to flare prediction), physicians were asked whether they agreed with the predicted flare risk. Again, they answered this question for full and half medication. If they did not agree, they were asked to provide their own estimation in percent.For each case, participants chose what prediction parameters they considered as most relevant by selecting one or multiple options from the following parameters: (current) dosage, (no) intravenous administration, anti-CCP, BMI, bDMARD, clinical disease activity index (CDAI), CRP, co-therapy, DAS28-ESR, disease duration, ESR, evaluator visual analogue scale (VAS) activity (mm), gender, HAQ, patient VAS activity (mm), patient VAS pain (mm), simple disease activity index (SDAI), SJC, smoker status, and TJC.For each patient, participants decided on whether to continue with current medication, change the dosage and/or type of bDMARD, or discontinue treatment with biologics. If they chose to change the dosage, they determined a new dose (in mg) and frequency. If they chose to change the type of bDMARD, they additionally selected the new bDMARD from a comprehensive list and the type of application . Subsequently, participants rated how confident they felt with their treatment decision on a 5-point scale from 1  to 5 (‘completely confident’). If physicians decided to change co-therapy, they could leave an open comment to describe. A one-sided paired samples Wilcoxon test was used to assess significance.Agreement among rheumatologists (raters) was evaluated for a subset of 10 patients with regard to treatment decisions  and the perceived flare risk (in %). Inter-rater reliability scores such as intraclass correlation (interpretation given by Cicchetti (1994) ) or FleiThe usability and acceptance of the RCM was measured using the system usability scale (SUS) and the net promoter score (NPS). The SUS is a widely established tool within the field of usability research . Its 10 The NPS, initially introduced by Reichheld , provideData of 50 RA patients  with a tFive physicians  with a mean age of 29.4 years working at the rheumatology outpatient clinic of the University Clinic Erlangen with varying years of work experience in rheumatology (1–5 years) were included. Four residents in training with 1, 2, 3, and 5 years of training and one board-certified rheumatologist with 5 years of training also participated in the study.The average affinity for technology was 4.13 (SD = 0.41) and general attitudes towards AI improved slightly after using the RCM, from 4.10 (SD = 0.53) to 4.17 (SD = 0.67) for the positive subscale of the GAAIS and from 3.65 (SD = 0.38) to 3.88 (SD = 0.41) for the negative subscale. A trend indicated that participants with a higher affinity for technology had a more positive attitude towards AI after using the system compared to participants with a relatively low affinity for technology, The physicians predicted varying disease flare risks , agreeinPhysicians generally reported a lower flare risk compared to the flare risk prediction tool and deviation was higher for the half dosage prediction . The devThe physicians rated swollen and tender joint count as the most important features, whereas DAS-28-ESR and disease duration were the most important features for the AI-powered flare risk prediction tool . SupplemThe treatment decisions were heterogenous , and allp = 0.052) mean confidence increase from 3.5 (SD = 0.95) to 3.7 (SD = 1.20) was observed. Mean confidence in escalation increased from 3.5 to 4.2 such that at T2, continuing current treatment was the decision with the least mean confidence.Similarly, confidence in treatment decisions was heterogenous regarding the different patients and participating physicians . At T2, Usability was rated good with a mean SUS score of 82/100. The NPS decreased from +40% to −20% after usage. The mean ratings were 8/10 at T1 and 7/10 at T2, indicating a passive acceptance of the tool. Physicians generally reported positive impressions of the RCM see . They esIn the present study, the accuracy of a novel CDSS, called RCM, including an AI-powered flare prediction tool was investigated. Additionally, the usability, acceptance, and potential influence of RCM on physician decision making were explored. CDSSs have already been found to be helpful for the diagnosis of RA ,33, and One of our objectives was to evaluate the prognostic quality of our model based on unknown data. Overall, the accuracy, sensitivity, and specificity of our flare prediction tool were promising and roughly in the range of those reported in previous studies ,36,37,38The degree of heterogeneity in assessments between physicians in this study may suggest the usefulness of standardizing decision aids. The decrease in the deviation between physicians and the prediction tool when physicians were given access to the prediction feature was a promising observation in this respect.Physician treatment confidence greatly varied depending on patient cases and in general. Although, to some extent, this uncertainty could also be due to the unfamiliarity of dealing with case vignettes without the possibility of interviewing and clinically examining the real patient, we again see evidence here for the need and usefulness of a CDSS to support therapy decisions. Despite the small group size, a clear trend toward increased confidence in treatment decision making was demonstrated with access to the prediction feature, highlighting another benefit of the application. This aspect was further underlined by the qualitative analysis in this study, where several users emphasized the clarity of the tool and the feeling of security when assessments were consistent. The basically positive attitude towards AI and technologies tended to even increase when trying out the new flare prediction tool.Therapeutic decisions, in which many aspects must be included and integrated at once, can be very difficult and are therefore prone to bias . Stress Using the flare prediction tool increased therapy changes in particular with respect to tapering decisions, and the majority of RCM users tapered in more patients than the treating physician. This trend toward more tapering is consistent with current developments in RA, where more and more patients are in sustained remission, for which tapering has been shown to be feasible, and an increasing cost pressure from biologicals ,50,51.The relative importance of flare prediction parameters differed between raters and the flare risk prediction tool. While the RCM heavily weighted DAS-28 ESR, disease duration, route of application of the biological, anti-CCP status, gender, and HAQ, raters were more likely to include the individual components of the DAS-28 (SJC and TJC) and patient VAS in their decision. The selection of parameters for the AI-powered risk prediction was justified by their relative importance with respect to flare prediction. The selection of the raters, however, was probably based more on experience and intuition. HAQ and gender were not included in the rater’s decision making, although previous studies have shown predictive and prognostic value for these parameters ,53,54. AThis study has some limitations. The effects achieved by the RCM appear quite small, reflecting the small sample of five relatively young physicians. Subsequent larger studies and ultimately a comparative study where the RCM is compared to the standard of care in real patients are needed. Moreover, the RCM was evaluated without including patients as the most important co-decision makers, possibly jeopardizing shared decision making. However, supporting the decision with a CDSS could also give patients, who are conflicted between drug and disease burden, confidence to participate in treatment decisions. Furthermore, we cannot eliminate the possibility that presenting an AI-predicted flare risk reduced the variability between physician’s judgments via an anchoring bias, in that they relied too heavily on the proposed value in their decision making. However, given the attempted standardization of decision making through the RCM, such an approximation is desirable under the assumption that the AI-based prediction is accurate. The qualitative analysis revealed physician concerns about the reliability of the tool and the slight decrease in the NPS after usage expressed scepticism. It is important to note that the physicians did not know the prediction tool’s accuracy when they provided their evaluations. A manual that explains the most important facts, including flare-prediction accuracy, could promote usage and trust. Concerns were expressed that the use of the tool in clinical practice could trigger uncertainty in the case of deviating assessments. However, disagreement between the physician and the prediction tool could also induce further reflection on the patient’s case and consultation with colleagues, which would ultimately have a positive impact on patient care.In conclusion, the AI-based RCM yielded promising results regarding validity, usability, and acceptance. We are now planning further longitudinal studies in larger cohorts to test its use in real clinical practice and explore patient acceptance.EP21165619.4, CN202210301121.4, US17/703,226; EP21182428.9, CN202210733378.7, US17/848,993.


Papaya is the fourth most favored tropical fruit in the global market; it has rich nutrition and can be used for medicine and food processing. However, it will soften and mature in a short time after harvest, resulting in a lot of economic losses. In this study, papaya fruits were soaked in 0, 12.5, 25, 50, and 100 ml/L ethanol solutions for 2 h and stored at 25°C for 14 days, by which we explored the effects of ethanol treatment in papaya after harvest. At an optimal concentration of ethanol treatment, color changing of the papaya fruits was delayed for 6 days, and decay incidence and average firmness of the fruits were shown as 20% and 27.7 N, respectively. Moreover, the effect of ethanol treatment on antioxidant systems in the papaya fruits was explored. It was observed that ethanol treatment contributed to diminish the development of malondialdehyde (MDA), ethylene, and superoxide anions. Furthermore, the activities of superoxide dismutase (SOD), catalase (CAT), and ascorbate peroxidase (APX) were promoted than those of control group, while the activities of peroxidase (POD), phenylalanine ammonia-lyase (PAL), and polyphenol oxidase (PPO) were brought down. In addition, the principal component analysis (PCA) showed that PAL, ethylene, and superoxide anions were the main contributors for the maturity and senescence of postharvest papaya. In this experiment, ethanol treatment had the potential of delaying the ripening and maintaining the storage quality of papaya fruits. Carica papaya L.) is a rapid-growing, latex-producing herbaceous fruit tree and is widely cultivated in the tropics and subtropics  and guaiacol peroxidase (GPX)  .2H5OH), a low-toxic plant secondary metabolite, is used as an effective and safe food additive for postharvest preservation and storage of horticultural crops. It was found that ethanol soaking or steam treatment could prolong the life of freshly cut Bougainvillea flowers , harvested in Ledong county, Hainan province, were quickly transported to the laboratory for treatments. A total of 150 papaya fruits with similar size and maturity degree of 70–80% and without mechanical damages, diseases, and insect pests were randomly divided into five groups, soaked in 0.05% sporgon solution for 5 min, and dried naturally. Then, the fruits were soaked in 0, 12.5, 25, 50, and 100 mL/L ethanol solutions for 2 h (which were separately regarded as 0 mL/L ethanol treatment (CK) for the control group and treatment groups A, B, C, and D). After drying the surface of the pericarp, the fruits were transferred to the storage place at 25°C and 80% RH. Three fruits were selected randomly from each treatment group on days 0, 2, 4, 6, 8, 10, 12, and 14 (the day of ethanol treatment was regarded as day 0) for determination of physiological indicators, cryopreserved with liquid nitrogen, and preserved in a refrigerator at −80°C for further analysis.Papaya fruits . To determine titratable acidity, 5 mL papaya fruit juice was extracted, dissolved in 50 mL distilled water, added with phenolphthalein, and then titrated with 0.1 mol/L, pH 8.3 NaOH solution, and acidity was expressed as the equivalent percentage of malic acid.g for 15 min. A 2 mL 5 g/L solution of thiobarbituric acid was added into 2 mL supernatant, water bathed for 15 min at 95°C, cooled, and centrifuged for another 15 min. We determined OD450, OD532, and OD600 values and calculated MDA content using the formula:The measuring method of MDA was based on p-aminobenzene sulfonic acid solution and 1 mL 7 mmol/L α-naphthylamine solution were added, and the mixture was stirred and kept at 37°C for 20 min. OD530 value was measured, and the blank was adjusted with distilled water.The determination method for superoxide anions content was referred to Ethylene content was determined using an enzyme-linked immunoassay (ELISA) kit  and referred to The activities of SOD, peroxidase (POD), CAT, phenylalanine ammonia-lyase (PAL), polyphenol oxidase (PPO), and APX, and total protein content were determined using assay kits  and following the manufacturer’s specification.P < 0.05. All statistical analyses were carried out with IBM SPSS Statistics .This study utilized a completely randomized design with a total of three biological repeats. Data were analyzed by one-way analysis of variance (ANOVA). Means were compared using least significant difference (LSD) with a confidence level of It can be observed from After 14 days of storage, it can be observed that the decay incidence of treatment groups B and C decreased by 20 and 30% compared with the control group, and that weight loss rate decreased by 2.25 and 2.8%, respectively, while average fruit firmness increased by 65.37 and 13.13% . In addiSoluble solid content and titratable acidity could be reduced by ethanol treatment. According to During storage, protein content generally increased slowly and peaked on day 10 , which wEthanol treatment could significantly reduce the content of MDA at the later stage of storage, and treatment group B had the best effect, followed by treatment groups C and A. On day Generally, the content of superoxide anions was increasing, and different concentrations had different effects . At the Similarly, POD activity peak was delayed after ethanol treatment . OverallThe activity of PAL decreased slightly on days 2–6, reached the highest when respiratory climacteric peaked, and then returned to the previous level . During Polyphenol oxidase activity increased rapidly on days 6-10 because of the maturity and senescence of the fruits and respiratory climacteric, while ethanol treatment could reduce the activity of enzymes to some extent compared with the control . During It can be seen from For selecting the suitable concentration of postharvest papaya, we performed the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) based on 15 physiological indicators storage day 10 . The comA principal component analysis (PCA) was performed based on nine antioxidant indicators of papaya on storage day 10, with which exploring the main factors contribute to storage quality . Three pThe biplot of the PCA is shown in Climacteric fruits mature and age rapidly after harvest, and are accompanied by deterioration, water loss in the flesh, and entire quality decline. Ethanol, a green and safe food additive, could effectively delay fruit ripening and senescence, and improve the overall storage quality, which has been proved in a variety of horticultural crops and is a potent strategy for postharvest preservation. This study demonstrated that the storage quality of postharvest papaya was improved, and that the antioxidant system was promoted by ethanol treatment.In this experiment, ethanol treatment could the delay color-changing of papaya fruits for several days, maintain firmness, and reduce fruit decay incidence and weight loss rate, which were beneficial for improving appearance quality. Oxidative stress is considered as the main cause of senescence and severe pathology , while tEthylene, a gaseous hormone, plays a significant role in regulating fruit ripening , and red2–) into O2 and H2O; APX participates in the ascorbate-glutathione (AsA-GSH) cycle to remove H2O2, and these enzymes reduce the damage of ROS to the cell membrane  and non-enzymatic antioxidant defense systems in plants, which protect plant cells from oxidative damage by eliminating reactive oxygen species . SOD commembrane . This stOn the contrary, ethanol treatment inhibited the activities of POD, PPO, and PAL, which are the main enzymes inducing enzymatic browning in horticultural crops . PhenylaThe PCA reflected the contribution of each indicator to storage quality and fruit ripening. In this study, PC1 (51.23%) accounted for half contribution rate, of which three most important indicators were PAL, ethylene, and superoxide anions, followed by PC2 (14.74%) and PC3 (12.52%). It can be seen that the increase in PAL activity and ethylene and superoxide anion content were the main contributors for papaya fruit ripening and senescence after harvest, and that ethanol treatment suppressed PAL, ethylene, and superoxide anions, which delayed the maturation of the fruits.The purpose of this study was to determine the effect of ethanol treatment on postharvest papaya and showed that ethanol soaking treatment could effectively delay color-changing, decrease decay incidence and water loss rate, and slow the decline of firmness to maintain the storage quality of papaya fruits. Moreover, ethanol treatment reduced the biosynthesis of MDA, ethylene, and superoxide anions, promoted the activities of SOD, CAT, and APX, and inhibited the activities of POD, PAL, and PPO, so as to hasten the antioxidant system and delay the maturation of postharvest papaya . In addiThe original contributions presented in this study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.PC and ZL contributed to conception and designed the experiment. ZL, YM, and HL implemented the experiment. ZL processed and analyzed the data and wrote the first draft of the manuscript. FJ, PC, and HZ revised the manuscript and made modifications. PC supervised the whole study and provided guidance. PC and HZ acquired the funding. All authors have read and approved the manuscript.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. 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Due to technical limitations, few studies on the supine position have focused on spinal curvature measurement. This study used a novel technique to measure spinal curvature in the supine position and obtain intervertebral disc pressures through computational simulation. The results revealed an increased craniocervical height when lying on a soft mattress, leading to significantly increased cervical disc loading. The lumbar lordosis was reduced when using the hard mattress, and contact pressure significantly increased. Thus, a medium mattress appeared to be more suitable to use. In cases where a soft mattress is used, the use of a softer or thinner pillow should be considered.Sleeping support systems can influence spinal curvature, and the misalignment of the spinal curvature can lead to musculoskeletal problems. Previous sleep studies on craniocervical support focused on pillow variants, but the mattress supporting the pillow has rarely been considered. This study used a cervical pillow and three mattresses of different stiffnesses, namely soft, medium, and hard, with an indentation load deflection of 20, 42, and 120 lbs, respectively. A novel electronic curvature measurement device was adopted to measure the spinal curvature, whereby the intervertebral disc loading was computed using the finite element method. Compared with the medium mattress, the head distance increased by 30.5 ± 15.9 mm, the cervical lordosis distance increased by 26.7 ± 14.9 mm, and intervertebral disc peak loading increased by 49% in the soft mattress environment. Considering that the pillow support may increase when using a soft mattress, a softer or thinner pillow is recommended. The head distance and cervical lordosis distance in the hard mattress environment were close to the medium mattress, but the lumbar lordosis distance reduced by 10.6 ± 6.8 mm. However, no significant increase in intervertebral disc loading was observed, but contact pressure increased significantly, which could cause discomfort and health problems. About eight hours of quality sleep daily is essential for maintaining health. A reasonable sleeping support system composed of a mattress and pillows is critical for good sleep. The human spine should be kept in its natural shape during sleep to minimize the internal loading of soft tissues and allow the intervertebral discs (IVD) to rehydrate ,2,3. SpiThe stiffness of the mattress can affect the alignment of the spine. If the mattress is too firm, the lower back cannot reach the mattress when lying down, leading to an abnormal spine kyphosis. If the mattress is too soft, it can cause the hip area where the weight is concentrated to sink which can lead to an unnatural kyphosis of the lumbar spine, with an over-compression of the IVD front side . HoweverGood support for the head and neck is considered an integral part of overall sleep quality . ResearcPrevious studies developed several finite element (FE) models of the human body for biomechanical analyses. These include neck ligament models ,20,21,22This study aimed to investigate the biomechanical effects of the human body, which is interactive with different stiffnesses of mattresses.Measurement of spinal curvature while lying in a supine position on different-stiffness mattresses.Computational simulation of contact pressure distribution.Investigating the internal stress of IVDs.Investigating the relationship between spinal curvature and musculoskeletal health.The contributions of this study are summarized as follows:Sixteen participants (29.4 ± 5.1 years) with no history of spinal injuries, deformities, or pathologies were recruited at the university campus. All participants could remain standing for more than 5 min with their arms extended horizontally. The patients’ average height, weight, and BMI were 170.3 ± 7.8 cm, 64.8 ± 13.6 kg, and 22.2 ± 3.4. This study was reviewed and approved by the University. The reference number for ethical approval is HSEARS 20201223001. All participants were informed of the experimental procedures and provided their written informed consent.Three polymeric foam mattresses were provided by a mattress manufacturer . The manufacturer considered them representatives of a soft mattress (SM), a medium mattress (MM), and a hard mattress (HM). The materials’ 25% compression indentation load deflection was SM: 20 lbs, MM: 42 lbs, and HM: 120 lbs. The three mattresses had the same dimensions, with a size of 1900 × 1200 × 200 mm. A typical cervical pillow  was adopted in this study to establish a realistic sleep environment. The design and dimensions of the mattresses and the pillow are shown in To minimize shipping and storage space, mattress manufacturers typically perform the foaming process of polymer foams by themselves. Thus, most of the mattress foams are non-standard. A plastic tensile test machine  measured the stress–strain properties of foams to support the computational simulation. Poisson’s ratio was obtained by measuring the material diameter during the stress–strain test. The testing results are shown in A self-developed curvature measurement tape (CMT)  was usedstart, Ystart, Xmid, Ymid, Xend, and Yend) representing the spinal curvature. In the post-processing stage, the data were loaded into CAD software  for analysis.As shown in An FE model of a male body with details of vertebrae, IVDs, major ligaments of the spine, and bulk tissue of the whole body was developed. The modeling procedures included biomedical imaging, geometric reconstruction, material properties, model assembly, loading, boundary conditions, and model validation.An adult male without spinal disease was recruited for the model reconstruction. He was 37 years old, 176 cm in height, and 74 kg in weight. The geometry of the body contour, skull, vertebrae from C1 to L5, sacrum, pelvis, ribs, shoulder blades, and primary legs structures was acquired with an MRI scanner . Before the scan, a custom-made orthosis was fabricated to replicate the contours of the participant’s rear head and back of the torso in an upright position. The orthosis and limb supporters were placed on the scan bed of the MRI scanner. The participant laid on the orthosis and supporter to maintain his body profile and spinal alignment, which were scanned similarly to an an upright standing posture.The images were segmented and processed in Mimics  and 3-matics  software. Bones, IVDs, and bulk soft tissue geometries were reconstructed as three-dimensional solid parts. Their locations and configurations were established based on the medical images and anatomy atlas ,30,31. TThis FE model contained the major spinal ligaments, including the anterior longitudinal ligament (ALL), posterior longitudinal ligament (PLL), Flava ligament (FL), cervical ligament (NL), interspinous ligament (ISL), transverse ligament (IL), and capsular ligament (CL). All ligaments were created in the form of the truss. The mechanical properties of the ligaments were obtained from the literature and are listed in Three sleeping support systems were constructed according to the experimental setup. The material properties of mattresses and the pillow were obtained from tensile tests. The hyperelastic mechanical properties under compression were entered into the FE material database. A non-deformable discrete rigid two-dimensional plane was created as the ground plate to support the mattress, pillow, and human model. The dimensions of the ground plate was 2000 × 2000 mm.The mesh creation process was performed with Abaqus 6.14 . Solid parts including the skull, brain, 24 vertebrae, 24 IVDs, sacrum, pelvis, blades, femurs, shanks, and feet were meshed using linear tetrahedral elements (C3D4). Four hundred and eighty-six trusses were modeled as ligaments to connect bony structures and the bulk tissue of the whole body; all ligaments were meshed using two-node truss elements (T3D2). Hexahedral elements (C3D8) were used to mesh the pillow, mattress, and ground plate. If the mesh is too large, it may decrease the computational accuracy, while a mesh that is too small may lead to a significant consumption of computer power and result in a long computational time. Since cervical discs have a smaller volume than other discs , the lowThe material properties used in the FE model are listed in In this study, the ground plate was locked, and the mattress above the ground plate had all degrees of freedom and could be deformed in any direction. The bottom side of the cervical pillow was in contact with the mattress top at a friction coefficient 0.6. The human model was placed above the sleeping support system, and uniform gravity (g = 9.81) was applied downward to the ground plate during the simulation .p < 0.001). A Pearson’s correlation coefficient of between 0.7 and 1.0 indicated a strong positive linear relationship through a firm, linear rule [This FE model was validated by comparing spine parameters of the experimental measurements of lying in a supine position on different-stiffness mattresses and the computational results. As shown in ear rule .This study used CMT to measure the spinal curvatures of participants lying in a supine position in SM, MM, and HM environments. The CAD software extracted the spine parameters, including TI, HD, CLD, and LLD. The experimental results are shown in Contact pressure measurement systems aim to use thin and flexible sheet sensors to minimize mattress support interference ,42. Howe2, mattress = 3962 cm2; MM: pillow = 254 cm2, mattress = 2974 mm2; HM: pillow = 251 cm2, mattress = 1915 cm2). The metrology criterion was based on the area where the pressure was higher than 1 kPa.The contact area was obtained using computational simulations . Previous studies indicated that a high IVD loading may interfere with IVD rehydration and cause neck pain [2, and the area of the pillow bottom was 55 x 35 = 1925 cm2. It can be considered that the area supporting the craniocervical site increased significantly. As a result, the area of the mattress under the pillow experiences less deformation due to the reduced pressure.Although the same pillow was used for the experiments, the craniocervical region appeared to have stronger support in the SM environment. Craniocervical loading was applied to the pillow top; the load then spread inside the foam pillow and formed a larger area at the pillow bottom to support the same loading . The defThe torso sinks deeper into the softer mattress, but the craniocervical area sinks less, which results in high IVD loading. As can be seen in p < 0.001), while the LLD values for the MM and SM environments were close, with no significant differences (p > 0.05). Variations in LLD may result in different IVD loading distributions. The peak loading of the IVD was calculated using the FE method, as shown in The LLD measured in the HM environment was 10.6 mm lower than that in the MM environment , and the test effect size was moderate power with a significance level of 5%. However, the participants were recruited on college campuses and did not reflect the population. A larger sample size considering different age groups might allow the investigation of ageing discs, ligaments, and tendons.The human body contains complex biomechanical structures, including bones, muscles, tendons, ligaments, fascia, and skin. To enhance the accuracy of the computational simulation, this FE model involved the skull, vertebrae, IVD, ligaments, and surrounding soft tissue. However, it did not consider the muscles and assumed they were relaxed in the supine position. It is undeniable that some involuntary muscles stabilize the spine in the supine position.Quadratic tetrahedral elements could improve the mesh convergence of the simulation. However, we did not apply the quadratic tetrahedral elements because of the high computing power required. Previous studies found that models meshed with quadratic tetrahedral elements required costly computational power, increasing the computation time by about three times . Since tWe focused on comparing mattresses of different stiffnesses in this study. There are various types of mattresses, such as spring, foam, and airbag mattresses. The shape of the pillow is a critical element of pillow design as it contributes to the level of neck support and the user’s overall comfort. Additionally, the sleeping support can be produced from various materials, such as cotton, polyester, foam, feather, and latex. Different materials impact both sleep time and sleep quality . Future Though males and females have different body physiques, a previous study demonstrated that there was no significant interaction between gender and pillow height on craniocervical pressure . AdditioThis study combined experimental measurements and computational simulation analyses to evaluate the influence of mattress stiffness on the musculoskeletal system. Using the same pillow, while changing the mattress stiffness from medium to soft, the cervical lordosis increased by 26.7 ± 14.9 mm, head distance increased by 30.5 ± 15.9 mm, and there was no significant change in lumbar lordosis. The increase in cervical lordosis resulted in a 49% increase in IVD peak loading. When comparing the medium and hard mattresses, cervical lordosis did not change to a considerable degree, but lumbar lordosis decreased by 10.6 ± 6.8 mm. The decrease in lumbar lordosis caused a slight increase in IVD peak loading, but this is not deemed a critical problem. However, the significant increase in contact pressure with a hard mattress can cause discomfort and health problems. When using a soft mattress, it should be noted that the pillow’s support may be magnified, and it is recommended to choose a softer or thinner pillow.