PDR was correlated with serum creatinine (OR: 2.551), NLR (OR: 1.645), MPV (OR: 1.41), and duration of diabetes (OR: 1.301). Logistic regression analysis identified three predictive models with very good discrimination power for PDR : combining duration of diabetes with NLR, MLR, and, respectively, PLR, MPV, and serum creatinine. NLR, MPV, SII, and LMR were associated with PDR and could be useful when integrated into comprehensive risk prediction models.The pathogenesis of diabetic retinopathy is still challenging, with recent evidence proving the key role of inflammation in the damage of the retinal neurovascular unit. This study aims to investigate the predictive value of the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte (PLR), lymphocyte-to-monocyte ratio (LMR), and systemic inflammation index (SII) for diabetic retinopathy (DR) and its severity. We performed a retrospective study on 129 T2DM patients, divided into three groups: without retinopathy (NDR), non-proliferative DR (NPDR), and proliferative DR (PDR). NLR, MLR, and SII were significantly higher in the PDR group when compared to NDR and NPDR (3.2 ± 1.6 vs. 2.4 ± 0.9 and 2.4 ± 1.1;  Diabetes mellitus is a major health problem globally, accounting for 537 million patients worldwide and with an expected ascendant trend reaching 700 million by 2045 ,2. For tChronic hyperglycemia induces both neurodegeneration and apoptosis of the retinal ganglion cells and photoreceptors and causes microvascular retinal damages by multiple pathways: activation of aldose-reductase and polyol pathway, activation of the protein kinase C (PKC) pathway, accumulation of reactive oxygen species (ROS), nitric oxide deficiency, and changes in the blood flow, with increased platelet adhesion and aggregation ,8. NonenThe role of ocular and systemic inflammation in the pathogenesis of diabetic retinopathy has been confirmed by many previous articles ,13,14. IBlood cell changes contribute to the pathophysiological changes observed in diabetic retinopathy through various mechanisms, including endothelial dysfunction, leukocyte adhesion and infiltration, cytokine and chemokine production, platelet activation, and neovascularization . IncreasWhite cell inflammatory biomarkers, such as neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte (PLR), lymphocyte-to-monocyte ratio (LMR), and systemic inflammation index (SII), can be easily calculated based on the complete blood cells count (CBC). In recent studies, they showed good predictive value for several inflammatory, oncologic, and cardiovascular diseases ,25. PrevHowever, there is still conflicting evidence regarding the significance of these biomarkers in clinical evaluation and individual management of diabetic patients. There are relatively wide differences reported for the “cut-off” values that could be predictive of diabetic retinopathy and its severity.In this paper, we analyzed the correlations between diabetic retinopathy and NLR, PLR, MLR, and SII. Also, we analyzed the RDW and MPV values in T2DM patients with noDR comparatively, with non-proliferative DR, and with proliferative DR. The potential predictive value of these biomarkers was studied both for the onset of any changes of DR in T2DM patients and the presence of proliferative DR (PDR). A 1-year retrospective study was performed on patients with type 2 diabetes mellitus (T2DM) admitted to the Ophthalmology Department of Surgery, Emergency University Hospital Bucharest, for cataract surgery, between January 2022 and December 2022. Data were collected from observation charts and electronic patient files.Age, sex, duration of diabetes, and associated pathologies were documented for each patient. Each patient underwent a complete ophthalmological exam. The diagnosis and staging of diabetic retinopathy were performed according to the Guidelines of the International Council of Ophthalmology for Diabetic Eye Care ,28. The Blood routine tests and complete blood count with differential were performed after collecting fasting peripheral blood from each patient. Systemic inflammatory indices NLR, PLR, and MLR were calculated as the ratios of the neutrophils, platelets, and monocytes to lymphocytes, respectively. The systemic inflammatory index (SII) was calculated by the formula SII = P × N/L, where P, N, and L are the count for platelets, neutrophils, and lymphocytes, respectively . All couPatients with systemic or ocular inflammatory conditions and oncologic or previously diagnosed hematologic conditions were excluded. From the remaining patients, paired individuals in terms of age and sex were selected to be included in the 3 study groups , to limit the effect on the final statistical analysis of these two potential cofounders .®Statistical Softwareversion 22.006  was used for statistical analysis. An ANOVA test was used for continuous variables. For the statistically significant results, a post hoc analysis was performed to establish the differences within the three groups by using the Tukey–Kramer test for all pairwise comparisons. A Pearson chi-square test, Fisher’s exact test, and Linear by Linear test were used to evaluate the association between discrete variables.MedCalcThe specificity and sensitivity of NLR, PLR, MLR, and SII in predicting DR and PDR were analyzed by ROC curves. A minimum value of 0.6 area under the curve (AUC) was considered as a criterion for an acceptable discrimination model . LogistiA total of 129 Caucasian patients with T2DM were divided into three study groups according to the presence and the severity of DR: NDR (36 patients), NPDR (49 patients), and PDR (44 patients). The general data of the patients included in the study group are presented in There were no statistically significant differences in age, sex ratio, associated diseases, lipidic profile, fasting blood glucose (FBG), Hb, and HbA1C between the three study groups. The duration of diabetes was significantly different among the three groups, being well correlated with the severity of DR. The RDW distribution was different among groups. However, the differences were not statistically significant in the post hoc analysis.p = 0.024). The Tukey–Kramer test showed significantly higher values in PDR when compared to the NDR group. The proportion of patients with abnormal serum urea and creatinine values was significantly higher in the PDR group , with no differences between NDR and NPDR groups. These data suggest a higher prevalence of associated retinal and renal microvascular damages in the PDR group. The patients in the PDR group were also associated with more diabetic micro- and macrovascular complications, when compared to NDR and NPDR groups; however, the differences were not statistically significant.Serum creatinine was significantly different among the study groups (p = 0.007). While platelet count was not significantly different among the three groups, MPV was higher in the PDR group when compared to NDR and NPDR groups, suggesting an increased platelet activation in the proliferative stage of DR.When analyzing blood cell counts for different types of white cells, only neutrophils were significantly higher in the PDR group when compared to NDR and NPDR groups  when compared to NDR and NDPR. The post hoc analysis shows no differences between NDR and NPDR groups. PLR values increased along with the severity of DR. However, the differences among groups were not statistically significant. NLR, MLR, and SII indices were significantly higher in the PDR group , the predictive power for DR and PDR was further assessed by ROC curves.None of the studied biomarkers met the criteria of a minimum AUC ROC of 0.6 for predicting DR in the study group. When the prediction value for PDR was analyzed, NLR, LMR, and SII showed good discrimination power with high specificity and low sensitivity .p-value of <0.05 and the minimal value of the confidence interval for each OR > 1 were selected as possible risk factors for PDR  with each independent variable listed in  for PDR , ensurinA higher correlation with PDR was encountered for creatinine (OR: 2.551), NLR (OR: 1.645), MPV (OR: 1.41), LMR (OR: 161.19), and duration of diabetes (OR: 1.301). Age, FBG, HbA1C, PLR, RDW, and urea were not significantly correlated with PDR.Patients with diabetes present a diverse array of comorbidities and metabolic dysregulation features that may combine and accelerate the progression to proliferative diabetic retinopathy. A multivariate logistic regression analysis was further carried out to identify the predictive models with the best discrimination value based on the analyzed variables .p < 0.05), based on comparative ROC curve analysis  and MLR .Combining SII with different other parameters resulted in a logistic regression model with slightly lower AUC ROC, compared with NLR , interleukin-6 (IL-6), interleukin-8 (IL-8), tumor necrosis factor-α (TNF-α), and interferon-γ, were found to be elevated in serum and vitreous of DR patients ,32. ActiSeveral studies investigated the clinical value of the white cell fractions and ratios but with conflicting results. A large survey by Wan et al. , compariThere are only a few studies that analyzed the value of MLR in DR. Yue et al.  found a The neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) are promising biomarkers for evaluating the inflammatory status in diabetic retinopathy (DR). However, to maximize their clinical utility, it is essential to establish the appropriate classification systems or cut-off values for NLR and PLR concerning the presence and severity of DR. The NLR values in the normal adult population vary between 0.43 and 3.53, in previous studies, with an average value of 1.56 , with slSeveral studies have attempted to establish cut-off values for NLR and PLR to differentiate between patients with and without DR or to stratify patients according to DR severity. However, the proposed cut-off values have varied considerably across studies, reflecting differences in patient populations, study designs, and statistical methods ,45.Zeng et al.  found siIn a study by Ilhan et al. , NLR wasHu et al. analyzed the predictive value of NLR for the response to anti-VEGF therapy in DR patients and found that an NLR  <  2.27 showed a better improvement in letter scores than those with NLR  >  2.27 . A recenIn a systematic review by Luo et al. , MPV mea9 cells/L as a prognostic tool in estimating 5-year survival in patients with squamous cell carcinoma of the esophagus, while Hirara et al. [While multiple studies proved that SII is an important indicator of systemic inflammation as well as for the risk of acute cardiovascular events, there is little evidence regarding the value range of SII in a normal population. A recent paper of Bai et al.  found a a et al.  showed ta et al. .Guo et aa et al.  found a a et al. , in a re9 cells/L in non-DR patients to 754.4 ± 514.4 × 109 cells/L for the proliferative DR group. This finding may be explained by the multiple micro- and macrovascular diabetes-related comorbidities often encountered in these very vulnerable patients. We also found a high cut-off value for SII in our study, of 763.8, but with a fair predictive value (AUC = 0.623) for PDR. We consider that, while not particularly specific for PDR, higher values of SII are useful in clinical management of DR patient, as an overall mirror of the level of inflammation and vascular damage.T2DM is associated with chronic inflammatory changes, higher levels of cytokines, and increased oxidative stress, which lead to neurodegeneration and destruction of blood vessels, causing damage to multiple organs . This exIn our study, we found that no statistical differences were noticed between NDR and NPDR groups for any of the white cell inflammatory biomarkers. However, significantly higher values for NLR, MLR, SII, and MPV were found in the PDR group when compared with NDR and NPDR groups. This finding may signify that the level of systemic inflammation is higher in the advanced stage of DR associated with neovascularization. These findings support the idea that diabetic retinopathy is a complex, multifactorial process. Systemic inflammation is one of the key elements that need to be further investigated. However, by combining multiple data points, the power of prediction increases and allows individualized management of each case.The number of patients enrolled in this study is one of the main limitations of this study. This study did not evaluate the correlation of systemic inflammatory biomarkers with the level of macular edema, which can also be a significant cause of visual impairment in NPDR patients. This research is retrospective, based on the standard protocol of paraclinical exams used for patients admitted for cataract surgery. Data regarding the patients’ medication were not available and were not taken into account in the statistical analysis. Several biological data were not evaluated, such as BMI, systolic and diastolic blood pressure, and the lipidic profile. Systemic inflammatory biomarkers offer valuable information regarding the level of inflammation and vascular frailty in diabetic patients, and these two elements should be taken into account in evaluating the risk for developing PDR. The main shortcoming in the context of the current knowledge is the relatively wide range for normal values of these parameters. One future approach may be a dynamic evaluation of the changes in NLR, PLR, MPV, and SII analyzed with the correlation of DR progression. Further studies, including multiple risk factors evaluation, with diverse patient populations could identify better predictive models for clinical use. Such a model may provide a more accurate and personalized assessment of the inflammatory status and DR risk in individual patients with diabetes mellitus .This paper brings new evidence that supports the role of chronic systemic inflammation in the pathology of diabetic retinopathy. Systemic white cell inflammatory biomarkers did not predict DR in our study group. However, they proved to be of clinical value in assessing PDR, reflecting better the changes associated with the proliferative diabetic retinopathy than each of the white cell count differential taken separately. There are cheap, inexpensive tools that can be valuable in clinical practice. Higher values of NLR, LMR, SII, PLR, and MPV are significantly correlated with PDR in T2DM patients. The best predictive value was obtained for NLR and MLR when combined with the duration of diabetes. Platelet-derived biomarkers (MPV and PLR) may be useful in evaluating the risk of PDR when correlated with other clinical and biological data.


Streptococcus pneumoniae, Streptococcus , Staphylococcus aureus, Bordetella pertussis, and Mycoplasma pneumoniae, have shown varying degrees of drug resistance. In order to provide such problem and related evidence for rational use of antibiotics in clinic, we reviewed the drug resistance of common bacteria to macrolides in children recent 20 years.With the widespread use of macrolide antibiotics in China, common pathogens causing children’s infections, such as  Macrolides (MLs) are a diverse class of hydrophobic compounds characterized by a macrocyclic lactone ring that typically contains at least 12 elements and distinguished by variable side chains/groups. The 23S rRNA in 50S subunit in bacterial ribosome contains a peptidyl transferase (PTC) that catalyzes peptide bond formation to link amino acids into proteins. MLs interact with the nucleic acid in the domain V of the catalytic center of the enzyme, bind to the part between PTC and nascent peptide exit tunnel and then block the channel to inhibit the synthesis of bacterial proteins, which finally playing an antibacterial effect . In addiHowever, with the widespread use of these drugs, many bacteria are showing a tendency to become more resistant to the drugs. The mechanisms of ML resistance mainly include the following: 1.erm-mediated rRNA methylation: it is mainly caused by the double methylation of 23S rRNA in bacteria. Methylation can reduce the affinity of MLs to the ribosome site to one hundred thousand times of the previous, and is completed by erm enzymes . 2. rRNAStreptococcus pneumoniae”, “Mycoplasma pneumoniae”, “Bordetella pertussis”, “Staphylococcus aureus”, “ Group A Streptococcus/Streptococcus pyogenes”, “ Group B Streptococcus/Streptococcus agalactiae,” AND “macrolide” AND “resistance”, and reference lists of identified studies. Only articles written in Chinese and English were included. Finally, only the most relevant papers for this review were cited. The characteristics of the included literatures are shown in the In China, the resistance of many bacteria to MLs is on the rise. Here, we review the situation of drug resistance in China in the past 20-30 years. Search strategy: We searched PubMed, Wanfang database, Zhiwang of China, Google Scholar for articles published before 31 December 2022, by use of the terms: “SP is the most important pathogen in otitis, sinusitis, bronchitis, and community-acquired pneumonia (CAP), as well as a predominant cause of meningitis and bacteremia. The widespread use of MLs is associated with increased resistance. Reports of SP resistance to MLs first appeared in the 1980s–1990s globally and the first report of macrolides-resistant SP (MRSP) in China was also in 1980s .A total ermB and mefA were detected in 115 (96.6%) and 64 (53.8%) isolates, respectively. Both ermB and mefA were detected in 60 (50.4%) strains. Even more frustrating, 96.6% strains had MIC >256μg/ml. Subsequently, a large number of single and multi-center studies showed that the resistance rate of SP to MLs was in the range of 79%-100% (erm(B) (~58%), followed by mef(A) (~30%). . .50 and MIC90 of AZM, CLA, and ERY were all>1024μg/ml. Only Shenzhen reported a slight downward trend in SP resistance to MLs , and the mefA gene was positive additionally in 13 of them (9.03%)  . Meanwhi (9.03%) .ermB and mefA genes are major SP of resistant to MLs.In a word, the onset time of macrolide resistance in SP is basically the same as that in other countries in China. In the past 30 years, the resistance of SP to MLs in children in China has shown an increasing trend, especially in the 1990s, when a sharp upward trend occurred, and then maintained at a high level. SA is an aerobic or facultative anaerobic gram-positive coccus. With strong pathogenicity, it can cause a variety of infections including severe sepsis, pneumonia and wound infection. A growing number of studies have reported that the bacterium is resistant to a variety of antibiotics, especially MLs .Resistance to MLs in SA was rarely reported in China before 2000. Huang  comparedSCCmec often showed multiple resistance. In conclusion, from 2000 to 2010, the resistance rate of SA to MLs showed a trend of fluctuating increase and then decreasing. MRSA had a higher resistance rate to MLs than MSSA. In Belgium, Olivier Denis found the resistance of 455 clinical MRSA strains to ERY was 64% .There have also been studies of resistance in SA from different sources of samples, but no significant differences have been found. Specimens from skin, soft tissue, pneumonia, pus, etc., all showed high (62.3%-98.5%) drug resistance rates . Some stFrom the literature above, we can see that the research on the resistance of SA to MLs first experienced a rapid rising and then showed a decreasing trend of volatility, with certain regional differences in China. And, the resistance rate of MRSA was higher than MSSA.Streptococcus pyogenes, is a Gram-positive pathogen that routinely causes a variety of non-invasive  and invasive infections . MLs is an important treatment for GAS infections. In the present study, we investigated trends in GAS resistance to MLs in China.GAS, alternatively termed In 1968, a strain of GAS resistant to ERY isolated from a throat swab in a child was reported for the first time in the world . In EuroDeng  collecteAfter 2010, the resistance rate of GAS to MLs remained high. Wang  reportedermB gene, while 14 isolates (15.4%) had the ermA gene. Ji  carried ermA and/or ermB, of which 33 (97.1%) strains were resistant to ERY, no ermC gene was found. However, 53 strains carrying mreA, 18 strains carrying mefA, and 1 ermA positive strain were still sensitive to ERY. Two resistant strains did not carry any detected genes . In Canada, a study of 32 ERY-resistant GBS strains were genotyped and 88% were found to be erm positive (54% constitutive) and 12% mef positive  strains carried both ermB and mefA. However, it should be noted that 100% of the 17 intermediate strains carried ermB and 5.9% also carried mefA. In the United States, ERY resistance rates are also high in the GBS strains that cause invasive adult and neonatal disease, at 54.8% and 44.8%, respectively  strain was reported in 1994 in a 2-month-old infant in Yuma, Arizona, US . Since tThe earliest study on the drug resistance of BP in mainland of China was published in Jun 2008 . In thisThen, in 2011, a cross-sectional study of BP seropositivity and carriage among healthy adolescents in Shandong Province, 2 ERY-resistant strains were identified with MIC>256μg/ml, and both clinical isolates had the A2047G mutation in the 23S rRNA . In 2012Due to the high difficulty of pertussis culture, many regions do not carry out pertussis culture and susceptibility tests. Therefore, there is relatively little literature on pertussis resistance in China. Nevertheless, from the few studies at present, we can still see that the probability of BP resistant to MLs has increased significantly since 2010, although there are some regional differences. In order to overcome the difficulty of BP culture and avoid the missed diagnosis of drug-resistant BP, some studies also recommend the use of PCR-based sequencing for rapid detection of possible antimicrobial resistance . Given tMP is one of the main pathogens of CAP in children and adolescents. MLs are the main drugs for the treatment of MP infection. From 1968 to 1999, ML-resistant MP (MRMP) has been reported in only a few countries . HoweverThe detection of drug resistance of MP started late in China but high rates of resistance were found at the outset. It was not until 2003 that Chinese scholars first reported that MP was 100% sensitive to AZM and roxithromycin (ROX), but all of them were resistant to ERY . To our Studies from 2010 onwards also showed high levels of resistance. Many studies showed that the resistance rate of MP to ERY in Beijing is relatively high and most of which were above 80% . Even inPrevious studies have revealed that there are 2 types of clinical isolates (type 1 and type 2), differing significantly in their P1 gene sequences , and theFrom the current study, it can be seen that the incidence of MRMP is increasingly high in China, and only relatively low in a few areas. Although the drug resistance of MP cannot enhance the virulence of the pathogen, it will increase the difficulty of clinical treatment and limit the choice of antibiotics, and will cause more complications if the treatment is not timely . TherefoermB gene are currently common causes of resistance in these pathogens.In conclusion, various common pathogens such as SP, GAS, GBS, SA, BP and MP, have shown high resistance rates and high resistance level to MLs in Chinese children. The drug resistance of some pathogens occurs suddenly (such as MP and BP), which is a high drug resistance rate and high drug resistance level. There are regional differences in the resistance level of these pathogens. Mutations in the 23S rRNA site and/or carrying the XM and LL contributed to conception and design of the study. JL, JG, HZ, XW and MX organized the database. LL and XM wrote the first draft of the manuscript. JL, HZ and JG wrote sections of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.


Mycotoxins are naturally occurring toxic secondary metabolites produced by fungi in cereals and foodstuffs during the stages of cultivation and storage. Electrochemical biosensing has emerged as a rapid, efficient, and economical approach for the detection and quantification of mycotoxins in different sample media. An electrochemical biosensor consists of two main units, a recognition receptor and a signal transducer. Natural or artificial antibodies, aptamers, molecularly imprinted polymers (MIP), peptides, and DNAzymes have been extensively employed as selective recognition receptors for the electrochemical biosensing of mycotoxins. This article affords a detailed discussion of the recent advances and future prospects of various types of recognition receptors exploited in the electrochemical biosensing of mycotoxins. Chemical and electrochemical sensors are an important field of scientific research for their practical applications ,2,3. An There are advanced analytical methods, such as high-performance liquid chromatography–mass spectrometry (HPLC–MS), gas chromatography (GC), etc. which are widely used to monitor mycotoxins. However, these methods for sensing require a great level of sophistication and control. Electrochemical sensing is an alternate methodology with ease of use, fast response, and high sensitivity. During the process of electrochemical sensing, the electrode acts as the core, and at the surface of the electrode, the recognition element is present. As the analyte and recognition element combine, the reaction signal is transformed into an electrochemical signal. A major demand in this field is the designing of recognition receptors with high sensitivity, selectivity, and stability. In the last decade, the scientific fraternity has been working on the designing of electrochemical biosensors for the recognition of mycotoxins. Till now, a number of reviews on the presence of mycotoxins in various food and feed products, their health impacts, and their biological degradation are available ,10,11,12−7 ng/mL in corn samples , peptides, and DNAzymes have been extensively employed as selective recognition receptors for electrochemical biosensing . The gen samples . Similar samples .In the next section, different recognition elements have been discussed.The word “aptamer” originated from the Latin word “aptus”, which means “to fit” . AptamerAptamers can be constructed using the computational method “Systematic Evolution of Ligands by Exponential Enrichment (SELEX)”, following steps of adsorption, recovery, and amplification . AptamerThe primary initiative in preparing an aptamer-based biosensor is to immobilize a suitable aptamer on the conducting surface, such as a carbon nanotube, metal, or polymer electrode. Aptamers can be deposited straight on the electrode surface or indirectly through chemical coupling. The selection of an immobilization approach relies on several components, such as the properties of the target analyte, the type of biological recognition element, the transducer surface, and the operating parameters of the aptasensors . To use 2, −OH, and −COOH, are incorporated onto the surface of an electrode. This functional group interacts with a customized aptamer that has a corresponding functional group. The most frequently used approach is a carbodiimide-mediated process that employs the reagents EDC and NHS for linking the aptamer to the surface of the electrode via an amide bond , sulfides (R–S–R), and disulfides (R–S–S–R) which exhibit significant assimilation onto metal surfaces, such as silver, gold, copper or platinum. These sulfur clusters adsorb impulsively onto the metal surface, generating a stable mono-molecular layer known as the self-assembled monolayer (SAM). Further, the reinforcement of immobilized aptamers with mercaptohexanol (MCH) helps in displacing the non-specifically adsorbed portions of the aptamers and confirms their upright alignment . The covide bond  33]. In. In2, −Ode films . Aptamerde films .The electrochemical aptasensors have been categorized into three groups based upon the change induced by the analyte, such as conductivity change , the conformational change , and configurational change  .Other classifications of aptasensors include label-free, labeled, signal-off (with decreased activity), and signal-on (with improved activity). The label can be covalently bonded to the aptamer, and the aptasensor’s behavior to the target relies on the response of the label following the production of an aptamer-target complex. Until now, a variety of substances, including enzymes (glucose oxidase (GOD), alkaline phosphatase (AP), and horseradish peroxidase (HRP)) and nanomaterials , and polymers) have been used as labels . The sanSince 2008, a series of studies have been reported involving aptamers specific for mycotoxins using electrochemical , colorimAzri et al.  preparedFc) went up since Fc was closer to the electrode, and a large oxidation current of methylene blue (IMB) went down due to the poor binding ability of cDNA to methylene blue. Thus, IFc/IMB ratio was used to measure OTA concentrations  was prepared for the on-line detection of OTA with modified magnetic beads and an aptamer . The des2)6 AAA AAAAAA AGAT CGG GTG TGG GTG GCG TAA AGG GAG CAT CGG ACA-3′) based on a DNA aptamer, exonuclease I (for digesting unbound aptamers) and silver metallization  for detection of OTA. Exo. I eliminated free aptamers by digesting single-stranded DNA, but it was found to be ineffective against the G quadruplex generated upon OTA binding. The signal was significantly improved by adding AgCl and then using a double reduction technique to metalize the target-bound aptamers. The result obtained demonstrated exceptional biosensing capabilities, including high selectivity, high sensitivity, better stability, and excellent reproducibility possessed by the electrochemical aptasensors. The designed aptasensor was then used for the determination of OTA in beer, with a recovery rate of 96.6% to 109.7% and less than a 5% standard deviation.Suea-Ngam et al.  fabricat2)6-SH-3′; DNA 2: 5′-AAA GAT CGG GTG TGG GTG GCG TAA AGG GAG CAT CGG ACA-3′)-based sensor for detection and quantification of wheat samples was prepared by Tong et al. 3−/4− had inadequate access to the electrode surface, resulting in a poor electrochemical signal  conjugate and exonuclease I (Exo I) was reported by Abnous et al.  Figure . The elel signal a. When Al signal b. The inA “signal-off” electrochemical aptasensor for trace level determination of AFB1 was designed by Zheng et al.  using anGoud et al.  designedWei et al.  fabricatThe advancement in aptamer-based recognition elements for the quantitative analysis of mycotoxins in food samples has facilitated dynamic testing  of agricultural products, such as maize, wheat, beer samples, and corn samples.Nanomaterials provide structural stability and biocompatibility and offer good electrical properties to the design of biosensors. These provide a wide surface area for effective binding and immobilization of the recognition element of biosensors. High conductivity and excellent catalytic activity of nanostructures also eliminate the requirement of labels and redox mediators during the fabrication of electrochemical sensors. Nowadays, biosensors with different sizes, surface areas, and electrocatalytic characteristics are being developed using nanomaterials ,54,55. NThe most widely used nanomaterials for making biosensors are those based on graphene ,61. Due Graphene is a 2-dimensional nanosheet of carbon with a higher electrical and thermal conductivity than CNTs, as well as a bigger surface area . Materia2/GCE) was used to treat graphene nanosheets before they were covalently connected to electrode surfaces (GNS/Ph-PhNH2/GCE) −3/−4 −3/−4 . Then, tThe food chain is the mechanism by which marine poisons produced by tiny algae accumulate. They have a well-established history of putting humans at risk for a number of neurological and gastrointestinal disorders . For theFor the highly sensitive analysis of OTA, a signal-on aptamer-based electrochemical sensor having a DNA-controlled layer-by-layer assembly of dual AuNP couples was created . Here, fThus, the incorporation of nanocomposites and nanostructures in the recognition receptors of electrochemical biosensors has made possible the extremely sensitive sensing of mycotoxins in food samples. The advancement of cutting-edge techniques for creating and modifying nanocomposites and nanostructures with receptors and heteroatoms has considerably enhanced the performance of biosensors. Nanomaterials have contributed to an enhancement in electrode analytical capability for the detection of mycotoxins in comparison to other pure electrode forms. Developing electrochemical biosensors with an interfacial design derived from polymer-based materials has gained much attention because of its fast and selective sensing response and applicability on a small sample. Electrochemical biosensing through polymeric materials provides high selectivity, high sensitivity, and robustness at a low cost. Polymers having functional structures, such as polypeptides, lipid bilayers, and poly (ethylene glycol) chains, have gained excellent attention due to their continuous use in drug delivery, tissue engineering, and nanoscale self-assembly ,107. AllIn the area of electrochemical biosensing of mycotoxins, Xing et al.  designedIt showed that when there was a target analyte present, the aptamer−biotin could not bind to the SSB in the presence of mycotoxins, which resulted in the weak yellow color of the solution. A strong interaction between the streptavidin (SA) and biotin, after the addition of substrate/chromogen solution, resulted in a bright yellow-colored solution. From all the obtained result author found the LOD values 112 ng/L, 319 ng/L, and 377 ng/L for AFB1, OTA, and ZEN, respectively.Zhang et al.  fabricatDuring the analysis of mycotoxins, the aptamer arrangement of OTA was attached to the 3′ end instead of the 5′ end; this was probably due to the aptamer, which was situated at the exterior of the spherical nucleic acid, which was more appropriate for the direction of OTA toward the aptamer. The aptamer probe underwent a conformational change in the presence of OTA molecules, which aided in its detection of the lowest limit of 0.88 pg/mL.Hu et al.  developeLiu et al.  developeFor the detection of AFB1, Yang et al.  also fabJoshi et al.  proposedFor the sensing of OTA, Gaun et al.  fabricat−1 at a working pH of 7 and a temperature of 298K. Thus, PEI/PVA hydrogel created an excellent aqueous environment for the enzyme. Moreover, the bonding between the protonated amino group present on PEI and the negative charge of thermolysin and citrated AuNPs also helped their dispersion over the polymer blend. When AuNPs were not dispersed over the polymer blend, no conductometric signal was observed, which indicated the insulating property of crosslinked PEI/PVA hydrogel films. The author observed that the developed sensor had a LOD of 1 nm for OTA.A sensor for conductometric analysis of OTA in olive oil samples has been proposed by Dridi et al. . This elIn this area, Wang et al.  designed2nanosheets couple. A MIP-based electrode was used for the detection of OTA in real beverage samples. The samples were tested with HPLC for comparison. Under optimized conditions of the MIP electrode, the LOD was 14 nM, and the linear range varied from 0.5–15 μM.In the field of molecularly imprinted polymer, Hu et al.  preparedPacheco et al.  also wor1, which is a carcinogenic mycotoxin found in animal feed and food items. A conducting polypyrrole-(zinc porphyrin) composite was used to fabricate the MIP nanoparticles over the Pt electrode via the electro-polymerization method. The developed chemosensor was capable of detection of FB1with high sensitivity from 1 fM–10 pM with LOD 0.03 fM to 0.7 fM. A molecularly imprinted electrochemical sensor based on metal–organic framework was designed by Jiang et al. [1. The sensor was developed by electro-polymerization of p-amino thiophenol, which was further functionalized with gold nanoparticles. The cavities formed after extraction of the template were able to recognize the AFB1 specifically via π-π interaction with aniline moieties. The prepared electrode was used for the detection of AFB1 in spiked rice sample with LOD 3 fM, and the linear range varied from 3.2 fM to 3.2 μM. Another effective electrochemical sensing technique for AFB1 and FuB1 was proposed by Singh et al. [Similarly, Munawar et al.  designedg et al.  for the h et al. . MIP math et al.  also proElectrochemical biosensing has proven as an effective tool for the detection of mycotoxins. In this review, different types of recognition receptors used for the detection of mycotoxins have been elaborated. The unique emerging materials include CNTs, molecular imprinting polymer, graphene, aptamer, nanomaterials, DNAzymes, antibodies, and peptides. They have paved the way for electrochemical sensing with huge potential for the facile detection of mycotoxins. Few biosensors have been commercialized for the detection of mycotoxins in real food samples. There is a need for more research to check their efficacy under real-life conditions. The shelf life and reusability of sensors are two main aspects that need to be explored further. Reversible binding and regeneration of recognition receptors after use can increase the shelf life and cost-effectiveness of electrochemical sensors. Another challenge that needs to be addressed is the miniaturization of the electrode of the sensor and the analyte sample containing mycotoxins. On-site detection of mycotoxins using portable, handy, and easy-to-use sensors is another important aspect that needs to be explored in order the cut the cost of sophisticated instrumentation and skilled manpower. Research on the monitoring of spoilage of food due to mycotoxins contamination at the industrial level using smartphone devices is the need of the hour so that the spoilage is automatically detected and huge economic loss to the industry can be avoided. It can be concluded that electrochemical biosensing exhibit excellent sensitivity, a wide range of detection, easy handling, real-time measurement, and a lower limit of detection.


Both sleep time and quality can be associated with overweight or obesity. In obese people, visceral fat tissue develops, which results in an increment in the production of cytokines. The increased production of inflammatory cytokines can disturb the sleep/wake cycle. Therefore, weight loss by reducing fat tissue can improve sleep disorders. Intermittent fasting diets are popular and effective diets that can decrease body weight and improve anthropometric data and body composition. The present study aimed to evaluate the effect of Alternate-day Modified Fasting (ADMF) on sleep quality, body weight, and daytime sleepiness.Classification of 56 obese or overweight women, based on age and body mass index (BMI), was done using stratified randomization. Then individuals were assigned to the ADMF group (intervention) or Daily Calorie Restriction (CR) group (control) using the random numbers table for 8 weeks. We measured the Pittsburgh sleep quality Index (PSQI), weight, BMI, and the Epworth sleepiness scale (ESS) as primary outcomes and assessed subjective sleep quality (SSQ), sleep latency, sleep disturbances, habitual sleep efficiency, daytime dysfunction, and sleep duration as secondary outcomes at baseline and after the study.P < 0.001] and BMI (kg/m2)  compared to CR. No significant differences were found in the changes of PSQI  and ESS  between two groups. Also, following the ADMF diet led to significant changes in SSQ , and daytime dysfunction  in compare with CR diet.Following an ADMF diet resulted in a greater decrease in weight (kg) [−5.23 (1.73) vs. −3.15 (0.88); These results suggested that an ADMF could be a beneficial diet for controlling body weight and BMI. The ADMF diet didn’t affect PSQI and ESS in women with overweight or obesity but significantly improved SSQ and daytime dysfunction.https://www.irct.ir/trial/64510.The Iranian Registry of Clinical Trials (IRCT20220522054958N3),  Sleep disorders affect nearly one-third of adults. The association between sleep quality and food intake has been shown in studies . Both peThis study was a randomized, controlled, trial to investigate the effect of an ADMF diet and CR diet on body weight, daytime sleepiness, and sleep quality in overweight/obese women for 8 weeks. Individuals were recruited from several health centers located in Kashan, Iran by simple random sampling. Then participants were randomly assigned into groups control (CR) and intervention (ADMF). The study protocol was registered at the Iranian Registry of Clinical Trials (IRCT20220522054958N3) and was approved by the Ethics Committee of Kashan University of Medical Sciences (IR.KAUMS.MEDNT.REC.1401.046). All patients gave written consent to participate in the study. Inclusion criteria included women between 18 and 50 years old and 40 >BMI ≥25. Exclusion criteria included pregnancy, breastfeeding, having a chronic disease such as hypertension, diabetes, gastrointestinal disorders, and heart disease, losing 1–2 kg of weight in the past month, the habit of smoking, alcohol abuse, taking specific medication or following a specific diet, taking dietary supplements for weight loss, overnight shifts, having psychological and mental disorders.In this study, 56 women were recruited by Simple Random Sampling from some health centers located in Kashan considering the inclusion and exclusion criteria. Eligible individuals were stratified based on age and BMI to make sure homogeneity of between-group. Individuals per stratum were placed into an ADMF group or CR group after baseline investigations. The allocation sequences were generated by using random numbers table by an independent statistician. The statistician was blinded throughout the entire trial.The flow diagram of the study has been presented in Participants’ adherence to the regimen was monitored every two weeks by completing food record questionnaires three times a week   The quesThe impact of ADMF and CR on primary outcomes  and secondary outcomes  was assessed by change from baseline to end of the intervention.Participants’ weight, without shoes and with light clothes, was evaluated using the scale with an accuracy of 0.1 kg. A stadiometer with an accuracy of 0.5 cm was used to measure the height of patients. BMI was computed as weight (in kilograms) divided by height (in meters squared).The sleep quality in the previous 4 weeks is evaluated self-reportedly by the PSQI. The PSQI questionnaire has 19 items and assesses 7 components of sleep: SSQ, sleep latency, sleep duration, use of sleeping medication, sleep disturbance, habitual sleep efficiency, and daytime dysfunction . The iteIn this study, daytime sleepiness was measured by the ESS, a self-reported questionnaire. Participants answered items based on how likely they were to fall asleep or doze off during sedentary activities. ESS is an eight-item questionnaire and includes a respondent format “high chance of dozing” = 3, “moderate chance of dozing” = 2, “slight chance of dozing” = 1, and “would never doze” = 0. The total ESS score was calculated by summing the total scores of eight items. The total score range was from 0 to 24. Higher values indicate higher levels of sleepiness. The change scores were evaluated as the changes between the total ESS scores at the beginning and the end of the study .The physical activity questionnaire, based on metabolic equivalents (MET), was used to evaluate physical activity in this trial. It consisted of nine activity levels, ranging from rest and sleep with a metabolic equivalent of 0.9 to intense activity with a metabolic equivalent of ≥6 . Each let-test was used to evaluate between-group differences in quantitative data. To compare the mean of the quantitative data (within the group) at the beginning and end of the study, the paired t-test was used in parametric conditions and the Wilcoxon test was used in non-parametric conditions. To compare the mean between the two groups, a t-test was used in parametric conditions and the Mann-Whitney test was used in non-parametric conditions.The Kolmogorov-Smirnov test was applied to investigate the normality of data distribution. A Chi-square test was used to compare qualitative data between the two groups. An independent n = 1), discontinued intervention (n = 3), and migration (n = 1)] and 4 patients from the control group [discontinued intervention (n = 4)] were excluded. Finally, 47 participants completed the study and were consisted in the final analysis. The baseline characteristics of the participants in the present analysis were shown in As shown in the study diagram , 56 womeP <0.001] and BMI (kg/m2)  was observed in ADMF group in comparison to CR. The significant differences weren’t observed in the change of PSQI  and ESS  between 2 groups. Also, no significant differences were found in physical activity before and after the intervention, in the ADMF group  and CR group    . The advP= <0.001] and daytime dysfunction  in compare with CR diet. However, significant negative changes were seen in sleep latency  and sleep duration  and in parameters of habitual sleep efficiency  and sleep disturbances  no significant changes were observed between groups (Following the ADMF diet led to significant positive changes in SSQ [−0.69 (0.47) vs. −0.08 (0.40); n groups .The present study indicated that following the ADMF diet for 8 weeks among women with obesity or overweight improved BMI, body weight, SSQ, and daytime dysfunction compared to CR, however, had no effect on PSQI, ESS, habitual sleep efficiency, and sleep disturbances and harmed sleep duration and sleep latency. Our research indicated that although both diets (ADMF and CR) could result in weight loss after 8 weeks; the effect of the ADMF diet on body weight and BMI loss was higher than the CR. This finding was consistent with several previous studies, for example, Johnson et al.  and RazaSleep disturbances are common findings in obese people , which aSince obesity has been suggested as one of the main causes of sleep disorders, weight loss diets may play a role in sleep quality. The present study suggests that ADMF is an efficacious dietary method for decreasing body weight and managing BMI in women with obesity or overweight. In addition, we indicated that an ADMF diet can increase SSQ and improve daytime dysfunction, in comparison with a CR diet. These results give us better insight into the ability of ADMF versus CR for the management of weight and BMI. However, more studies are required to address the direction of causality and generalize the results to other diverse population groups with different health statuses. Moreover, more research is required to study the long-term effects of ADMF on holistic and metabolic health. The findings of this study will increase our information on fasting diets, which can be applied to ameliorate dietary recommendations. The addressing of the public health effect of sleep behaviors in the prevention of chronic diseases corroborates this call for further research.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Ethics Committee of Kashan University of Medical Sciences (IR.KAUMS.MEDNT.REC.1401.046). The patients/participants provided their written informed consent to participate in this study.SJ conceived the trial, designed the experiment, and was the Chief Investigator. AY analyzed the data and was responsible for the statistical design of the study. SH assisted with the conduction of the study and wrote the manuscript. All study authors read and approved the final version of the manuscript.This work was financially supported by the Kashan University of Medical Sciences and Health Services .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.


Nephrolithiasis is one of the most common urinary disorders in dogs. Although a majority of kidney calculi are non-obstructive and are likely to be asymptomatic, they can lead to parenchymal loss and obstruction as they progress. Thus, early diagnosis of kidney calculi is important for patient monitoring and better prognosis. However, detecting kidney calculi and monitoring changes in the sizes of the calculi from computed tomography (CT) images is time-consuming for clinicians. This study, in a first of its kind, aims to develop a deep learning model for automatic kidney calculi detection using pre-contrast CT images of dogs. A total of 34,655 transverseimage slices obtained from 76 dogs with kidney calculi were used to develop the deep learning model. Because of the differences in kidney location and calculi sizes in dogs compared to humans, several processing methods were used. The first stage of the models, based on the Attention U-Net (AttUNet), was designed to detect the kidney for the coarse feature map. Five different models–AttUNet, UTNet, TransUNet, SwinUNet, and RBCANet–were used in the second stage to detect the calculi in the kidneys, and the performance of the models was evaluated. Compared with a previously developed model, all the models developed in this study yielded better dice similarity coefficients (DSCs) for the automatic segmentation of the kidney. To detect kidney calculi, RBCANet and SwinUNet yielded the best DSC, which was 0.74. In conclusion, the deep learning model developed in this study can be useful for the automated detection of kidney calculi. Urinary calculi in the kidneys and upper and lower urinary tracts are among the most common abnormal findings in canine urinary disorders. According to a recent study, prevalence of upper urinary tract and lower urinary tract uroliths were reported to be 19 and 41%, respectively . Less thRenal calculi can be asymptomatic in many dogs; however, when the size or location of the calculi change, they are no longer silent, and can lead to clinical problems such as partial or complete ureteropelvic junction obstruction, hydronephrosis, renal parenchymal loss due to growing calculi, hematuria, and urinary tract infection due to infected calculi . In addiTherefore, early detection and size quantification of urinary calculi are important to prevent severe kidney diseases associated with calculi, and to provide better and timely treatment. Owing to their importance, several diagnostic imaging modalities, including X-rays, ultrasound, and computed tomography (CT), have been used to detect urinary calculi in both veterinary and human medicine. Among these methods, CT is reported to be the most accurate for detecting calculi with high sensitivity and specificity . HoweverOf late, numerous studies in human medicine have shown that deep learning models can be successfully applied to medical imaging fields for aspects such as classification, segmentation, and lesion detection –13. ConvIn human medicine, many studies have proposed various deep learning models for the segmentation and detection of kidneys –20 and kIn this study, we aimed to develop deep learning models for the automatic detection of kidney calculi and kidneys from non-contrast CT scans in dogs, and to evaluate the performance of these models.A total of 167 pre-contrast CT scans  of 167 dogs were randomly collected from multiple centers. Among the 167 pre-contrast CT scans, 34,655 transverseimages from 76 CT scans included kidney calculi, and were used for training and validation. The imaging protocols were as follows; 120 kVp, 150 mAs, 512 × 512 matrix and 0.75 rotation time (Alexion); 120 kVp, 84 mAs, 512 × 512 matrix, and 1 rotation time (Revolution ACT); and 120 kVp, 69 mAs, 512 × 512 matrix, and 1 rotation time (Brivo CT385). The slice thickness of the CT scans included in the study varied from 0.75 mm to 2.5 mm. Postcontrast CT scans were not included in the present study.The precontrast CT images included in this study were divided into training and validation data at a ratio of 80 to 20. Therefore, a total of 61 CT scans were randomly chosen as the training data and 15 CT scans were used as the validation data. The CT scans of dogs without medical records were excluded from the study. In addition, scans with motion artifacts, without volume information, or with an axis smaller than a certain size were excluded.In this study, 76 CT scans from 76 dogs with kidney calculi who underwent CT scans were included. The medical records of the dogs, including data on age, sex, neutering status, body weight, and laboratory examination results, were collected.The pre-contrast CT scans of the dogs included in the study were manually segmented by 10 clinicians  using the Medilabel software . From the images, all kidneys were segmented into the following three classes: (1) renal parenchyma; (2) renal pelvis and surrounding fat; and (3) calculi . The renThe training data were converted into a numpy array and pre-processed using the following steps: data resampling, intensity normalization, and non-zero region cropping, using Python and Pytorch framework.A non-zero crop is a process used for exclusively obtaining the actual region of interest (RoI) by cropping out the background. External structures, such as fixation frames for fixing animals on the CT table during scans, were masked, and voxels with certain values (under−1000 Hounsfield Unit) were considered as the background, and cropped. Data were resized to 512 × 512 pixels.Intensity normalization was performed to clip the minimum and maximum to Hounsfield Unit (HU) values of −155 and 195, respectively.To address variations in the spatial spacing and slice thickness of the CT scans used in this study, resampling was performed to adjust the various pixel dimensions and standardize the data to an isotropic voxel spacing of x = 0.5, y = 0.5, and z = 1.4 (mm). To preprocess the training data, which only included pre-contrast CT scans, the window width and level were set to 350 and 30 HU, respectively, and the minimum and maximum HUvalues were clipped to −155 and 195, respectively, before applying min-max normalization  , UTNet , TransUNThe AttUNet model extends the original U-Net architecture by incorporating attention gates into the skip connections . The arcUTNet has a U-shaped architecture, similar to the original U-Net, with an encoder-decoder structure and skip connections . The maiTransUNet combines the U-Net architecture with a vision transformer to create a hybrid model . The visSwinUNet incorporates the Swin transformer as its encoder, replacing the standard convolutional layers of the U-Net architecture . The swiThe RBCANet architecture utilizes a pre-trained DenseNet-161 encoder and U-Net's encoder–decoder structure to effectively capture hierarchical features . The atrOne of the main impediment factors of this study was the variation of data source owing to the relatively small size of calculi in the full CT images, various size of dogs and the fact that the data included in this study were collected from multicenter. To overcome this problem, our approach involved two stages and employed five architectures. In Stage 1, we utilized an AttUNet-based model to obtain an approximate RoI of the kidney through coarse feature maps. This step involved automatic segmentation of the kidney from the input CT image, followed by extraction of the RoI by cropping a non-zero region that excludes the kidney. The extracted RoI was then resized to 128 × 128 pixels and used as input for Stage 2.In Stage 2, the RoI obtained from Stage 1 was used as input. We evaluated the performance of five models for kidney stone segmentation: UTNet , TransUNThe input channel 1 and the output channel 3 were utilized. The PyTorch framework was used to construct the models. The combined loss function, including the weight addition of the cross-entropy and the dice loss function, is known to improve the performance of the segmentation network –33. In tpn, predicted map of foreground label of number of image elements; rn, ground truth of kidney and calculi; l, foreground label; wl, .Several evaluation metrics were used to evaluate model performance. Dice Similarity coefficient (DSC) measures the relative voxel overlap between the ground truth and the predicted segmentation to evaluate the similarity between segmentations using an automated model and the ground truth. A DSC close to one implies high similarity. The DSC was measured using the following formula: Sp, predicted pixel value; Sg, segmentation pixel value of ground truth).Intersection over Union, which is similar to DSC but penalizes under-segmentation and over-segmentation more than the DSC, was also used. The formula for Intersection over Union is as follows: As sensitivity and specificity are recognized as standard metrics for performance evaluation in the medical field, both of the above were used in this study , 36. TheSensitivity = Precision and Accuracy were also used to evaluate the models. The formulae for precision and accuracy are as follows: Precision = Receiver Operating Characteristic (ROC) is a line plot that depicts the diagnostic ability of a classifier based on its performance with different thresholds. A ROC curve was established as a standard metric for comparing multiple models, and was used to evaluate the models . The areFor the statistical evaluation of the characteristics of the dogs included in the study, Kolmogorov–Smirnov test and Shapiro–Wilk test were performed as tests for normal distribution. Mann–Whitney tests were used to evaluate differences in age among dogs with and without kidney calculi.In Stage 1, SwinUNet showed the best DSC (0.943), followed by RBCANet (0.942), UTNet (0.935), and AttUNet and TransUNet (0.934). As the DSC measures the relative pixel overlap between the manual segmentation and the prediction of the models, DSC close to 1 is considered to have higher similarity between two segmentations in this study.The sensitivity and specificity at the Youden point of the models were the highest for RBCANet , followed by TransUNet , SwinUNet , UTNet , and AttUNet .The ROC curve for kidney detection for the five models on the test set is shown in The IOU and precision metrics of the models are summarized in The performance of several models in detecting kidney calculi was assessed in this study. AttUNet outperformed the other models, with the highest DSC of 0.741, suggesting its superior capability to accurately delineate the intricate structures of kidney calculi. The DSCs of the other models were as follows: SwinUNet, 0.736; RBCANet, 0.733; UTNet, 0.701; and TransUNet, 0.682.In terms of sensitivity and specificity at the Youden point, TransUNet topped the list with values of 0.89 and 0.99, respectively. UTNet, SwinUNet, RBCANet, and AttUNet also exhibited competitive sensitivity and specificity values, demonstrating their ability to correctly identify true positive and true negative cases. The sensitivity and specificity of the models are summarized in Further comparisons were made based on the ROC curves shown in Among the dogs with kidney calculi included in this study, 32 were neutered males, 3 were intact males, 32 were neutered females, and 9 were intact female dogs. The mean age of dogs with kidney calculi was 10.79 ± 4.00 years (mean ± SD), ranging from 7 months to 18 years. The mean body weight (BW) of the dogs was 5.25 ± 2.49 kg (mean ± SD), ranging from 1.7 kg to 14.85 kg. The distribution of breeds among these dogs was as follow: 21 Malteses, 10 Poodles, 7 Shih Tzus, 6 Yorkshire terriers, 6 mixed breeds, 5 Pomeranians, 5 Schnauzers, 2 Cocker Spaniels, 2 Dachshunds, and 12 others.Among the dogs without kidney calculi, 45 were neutered males, 6 were intact males, 32 were neutered females, and 8 were intact female dogs. The mean age of the dogs without kidney calculi was 7.72 ± 4.08 years (mean ± SD), ranging from 4 months to 16 years. The mean BW of the dogs was 7.87 ± 7.52 kg (mean ± SD), ranging from 1.75 kg to 41.5 kg. The breed distribution of these dogs was as follows: 18 Malteses, 15 Poodles, 7 Mixed breeds, 7 Shih Tzus, 6 Pomeranians, 4 Cocker Spaniels, 4 Dachshunds, 3 Bichon Frises, and 27 others.p < 0.001) and BW (p = 0.005) showed statistically significant differences between dogs with and without kidney calculi. Dogs with kidney calculi were significantly older and smaller than those without calculi. The age and BW of each group are depicted using a box plot in Mann-Whitney tests for age , had the lowest AUC, suggesting potential limitations in its overall prediction accuracy. TransUNet demonstrated a balanced performance with the highest AUC but the lowest DSC for kidney calculi detection, suggesting possible challenges for accurate structure delineation. In addition, despite excelling in kidney detection, RBCANet showed a lower DSC and AUC for calculi detection. This suggests that while RBCANet is proficient at handling larger structures (such as kidneys) owing to its effective hierarchical feature capture, it may not be able to effectively identify smaller structures such as kidney stones. Therefore, the selection of an appropriate model should be tailored to the specific task, and should consider the unique strengths and weaknesses of each model.Compared with the results of models developed in the human medical field, the models developed in this study showed lower DSC values for detecting kidneys. Compared to the current study, a previous study in veterinary medicine using UNet Transformer reported a lower DSC value; a wide range of body sizes associated with various breeds was considered an obstacle in the training process and a factor leading to the lower DSC compared to other models developed for humans –21, 28. 3) and decreased as the size decreased. The size of the kidney calculi included in the current study varied, but the majority of the calculi were small (<3 mm), which meets the criteria of the small size group in human medicine. Compared to the results of previous models in human medicine, our present models showed promising performance in detecting small calculi. Further studies, including more data on larger kidney calculi, can help improve the performance of the models.The main reason for the lower DSC values for detecting kidney calculi of our model compared to those developed in human medicine was the smaller size of the kidneys and calculi. The size of the calculi is reportedly associated with model performance. In a previous study in human medicine, kidney calculi were classified into small (0–6 mm), medium (6–20 mm), and large (above 20 mm) sizes . The DSCIn this study, the age of dogs with kidney calculi was significantly higher than that of dogs without calculi, which is consistent with the results of previous studies. In a human medicine study, it was reported that the prevalence of kidney calculi increased with age, and increasing age was considered a risk factor . HoweverThe BW of the dogs with calculi was significantly lower than that of the dogs without calculi. Similarly, a recent study that investigated the prevalence and predictors of upper urinary tract uroliths in dogs found that dogs with upper urinary tract uroliths were significantly older and smaller than those without urolithiasis, which is consistent with our results . Also, aOne of the limitations of this study was the relatively small size of the kidneys and kidney calculi in dogs compared to those in humans, which acted as an impediment factor for model development. Moreover, some calculi were smaller than the minimum pixel of the labeling program. Therefore, the margins of several calculi in the labeled image did not meet the actual margins of the calculi, which could have resulted the lower DSC values for small calculi in this study and could be considered as false negative. Another limitation of this study was the small number of CT scans included. Further studies with more CT scans may result in a better model performance. In addition, despite the result of this study, the lack of external validation in this study can be considered as a limitation. External validation using independent datasets from clinical fields with different image conditions and qualities would help demonstrate the applicability of the models in the context of practical approach. Another limitation of this study is that the data used for the model development were retrospectively collected from multicenter with different CT scanners, which acted as a major impediment factor of this study. Further prospective study with controlled data could result in the development of models with advanced performance.In conclusion, the deep learning models proposed in this study showed promising results for the detection of small kidney calculi and highly encouraging results for the automatic segmentation of kidneys from pre-contrast CT images in dogs. These models can potentially assist clinicians in the detection of kidney calculi. Further studies using models that can automatically provide accurate volumes of calculi may have considerable clinical utility.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.The animal studies were approved by the Institutional Animal Care and Use Committee of Jeonbuk National University. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent was obtained from the owners for the participation of their animals in this study.YJ and HY: conception, design, drafting, and acquisition of data. YJ, GH, SL, KL, and HY: analysis, interpretation of data, revision for intellectual content, and final approval of the completed article. All authors contributed to the article and approved the submitted version.


Older age is associated with the deterioration of physical functioning (PF), and low PF is strongly related to poor quality of life among older people. We conducted a study to examine the trajectories of PF between middle and old age, considering sex differences as well as the association between socioeconomic status (SES) at different life stages and changes in PF.We analyzed data from the Polish arm of the HAPIEE  study, including 1,116 men and 1,178 women aged 45–64 years at baseline. Adult and childhood SES and social mobility were assessed using a retrospectively focused questionnaire. PF was assessed using the 10-question SF-36 scale at baseline examination, face-to-face re-examination, and three postal surveys, covering up to 20 years . We employed Generalized Estimating Equations models to assess changes in PF scores over time and compare PF trajectories across different SES categories.After adjusting for age and other covariates, we found that, in both sexes, participants with always middle or high SES, as well as those who reported upward mobility, had higher PF scores at baseline compared to those with always low SES. A decline in PF between middle and old age was observed in all SES groups; however, the decline was slower in participants with always middle or high SES compared to those with always low SES.This cohort study revealed that lower SES and downward social mobility were cross-sectionally associated with poorer PF, while upward social mobility seemed to largely reverse the effect of low childhood SES. In addition to the cross-sectional associations observed at baseline, advantaged SES was also significantly associated with a slower decline in PF over an 18-year follow-up period. The development of PF limitations is a lifelong process, and it is widely recognized that low PF is strongly related to poor quality of life among older people. Maintaining PF is a key component of healthy aging , 2. HoweSocioeconomic status (SES) has been extensively studied as a determinant of health, both within and between countries –6. The eIndividual SES is a complex concept, and the significance of its components varies depending on the culture and overall socioeconomic position of the population . In addiOn the other hand, low SES in adulthood was related to higher exposure to cardiovascular disease risk factors, such as smoking, physical inactivity, overweight, poor dietary quality , and unfA wealth of evidence exists on the relationship between SES and PF. For example, a recent study of over 5,000 participants in the English Longitudinal Study of Aging revealed that lower SES was associated with greater declines in grip strength, gait speed, chair stand failure, sensory function, cognitive function, depressive symptoms, reduced enjoyment of life, poorer social functioning, and some markers of physiological function , underscoring the critical role of SES in the aging process .Although the relationship between socioeconomic status and physical functioning is well documented, most studies use one point estimate on physical functioning and few address the problem of social mobility. Recently, there has been an increased focus on the life course approach in studying the effects of SES on health. This approach allows for investigations beyond simple measures of childhood and adulthood SES and provides a better understanding of the role of SES exposure in disease development , 28. VerHealth disparities by SES are pronounced in Central and Eastern Europe, yet relatively few studies have focused on older populations in this region –36. Stud2.2.1.The present analysis was conducted within the Polish arm of the HAPIEE study. This multicenter prospective cohort study aims to investigate the psychosocial and dietary factors that contribute to cardiovascular diseases (CVD) and other chronic conditions in Central and Eastern Europe. Detailed information regarding the study design and methods has been previously published . The pre2.2.In the HAPIEE Study, the assessment of PF was conducted using a subset of the SF-36 scale, which consists of 10 questions related to activities involving vigorous and moderate physical exertion, lifting groceries, mobility, and self-care tasks . Partici2.3.The information regarding SES was collected at baseline (2002–2005) for both childhood SES (using a retrospective manner) and adulthood SES. Childhood SES was determined by considering the educational level of both parents  and information about housing standards at around the age of 10 . These factors were combined into an index to measure the amenities experienced during childhood.Adulthood SES was assessed using five items: educational attainment, professional activity, household amenities, and current financial situation. The details of how the SES index was constructed have been previously described , and a sA two-step clustering algorithm was employed to classify participants into homogeneous groups of low, middle, or high SES for both childhood and adulthood. The classification was based on reasonable evidence of the cluster structure, indicated by the silhouette coefficient s(i) of 0.51 . Based o2.4.2). Participants were asked about any self-reported diagnosis or hospitalization for spine or joint problems in the past year. Consumption of alcohol was assessed using the graduated frequency questionnaire (GFQ). By assessing drinking frequency and amounts consumed, the total annual alcohol consumption was calculated. It was assumed that 100 mL of beer, wine, and spirits contained 4, 10, or 36 g of ethanol, respectively (During the baseline examination (2002–2005), a standardized questionnaire was administered to collect information on age, gender, education, marital status, and smoking habits. Body weight and height were measured to calculate the body mass index . The normal distribution of variables was tested using the Shapiro–Wilk test. Categorical variables were presented as percentages. The Chi-square test was used to compare the distribution of categorical variables. The unpaired Student’s Given that the PF score was assessed at least four times during the follow-up, the Generalized Estimating Equations (GEE) technique was employed to model the longitudinal data . This app < 0.05 was accepted as statistically significant (The results of the GEE models were reported as coefficients with standard errors (SE) for differences at baseline and for differences in the trajectories of the PF score during the follow-up (interaction with time), presented as the decline per 1 year. Statistical analyses were performed using IBM Corp. software released in 2021  or R Core Team (2021). . nificant .3.A total of 1,116 men and 1,178 women were included in the analysis, all of whom completed at least four examinations, including the first and last ones. The participants included in the analysis were younger [mean (SD) age of 56 (6.4) years vs. 58 (7.1) years], had higher levels of education (42% vs. 25% with university-level education), were more frequently married or cohabiting (80.6% vs. 75.0%), and had a lower prevalence of smoking (24.3% vs. 34.2%) compared to the remaining participants from the baseline examination (2002–2005) of the entire sample participating in the Polish part of the HAPIEE study. There was no difference in gender distribution . Men also had slightly higher mean BMI and a higher prevalence of spine/joint problems .Regarding childhood SES, there were no differences in the distribution by SES category between men and women. Each of the three categories was equally represented by both sexes. However, in adulthood, a higher proportion of women had low SES (24.1% vs. 16.7%), while a higher proportion of men had high SES (60.9% vs. 53.7%). This difference reflects the fact that among women, the categories of always low and downward social mobility were more frequent compared to men. On the other hand, the proportions of participants in the categories of always middle or high and upward mobility were higher in men than in women.p for interaction = 0.70). The decline in PF scores over time was consistent across all subsequent statistical models.At the baseline examination (2002–2005), the mean PF score was higher in men compared to women (89 vs. 83 points). Over time, there was a decline in PF scores in both sexes . HoweverIn women, compared to those with low SES in adulthood, those with middle and high SES in adulthood had higher mean PF scores by more than 10.5 and 11.2 points, respectively. Similarly, compared to those with always low SES, women with upward social mobility and women who always had middle or high SES had higher PF scores by approximately 10.5 and 12.8 points, respectively.In men, the differences in PF scores according to adulthood SES and social mobility categories were similar to those observed in women although the magnitude of the differences was slightly smaller .The trajectories of PF between the baseline and final examinations varied significantly according to childhood SES, adulthood SES, and social mobility categories in both men and women.In men, those with middle and high SES in childhood experienced a slower decline in PF compared to those with low childhood SES. Similarly, men with middle and high SES in adulthood experienced a slower decline in PF compared to those with low adulthood SES. Regarding social mobility, only men who had always high or middle SES demonstrated a slower decline in PF compared to those who had always low SES (by 0.47 points per year).Among women, those with high SES in childhood (but not those with middle childhood SES) had a slower decline in PF compared to those with low childhood SES. Women with middle adulthood SES and women with high adulthood SES also experienced a slower decline in PF compared to those with low adulthood SES. Similar to men, in the social mobility analyses, only women with always high or middle SES demonstrated a slower decline in PF compared to those who had always low SES (by 0.31 points per year).Additional adjustments for covariates such as marital status, history of spine/joint problems, BMI, smoking, and alcohol consumption did not significantly change most of the results. The only exception was the difference in the trajectories of PF between men with middle adulthood SES and men with low adulthood SES, which lost significance after adjustment for covariates although the change in the estimate was minimal , 2.4.In this population-based cohort study conducted in Central and Eastern Europe, we observed significant differences in PF based on SES indicators measured at baseline. Moreover, we identified variations in the decline of PF over time among different SES categories. The analysis of trajectories suggests that while health disparities related to childhood SES may diminish during middle age if people improve their socioeconomic position, these disparities may reappear as a delayed effect during the transition from middle to old age.Previous studies have proposed that upward social mobility, along with the stress associated with climbing the social ladder, could have harmful effects on health –49. HoweThe findings from our previous study on disparities in PF in three populations from Central and Eastern Europe provided support for the cumulative advantage/disadvantage hypothesis. High SES was associated with better PF at baseline and a slower decline during the follow-up period . Our resOverall, our study confirmed earlier findings that psychological and social factors play an important role in slowing down the age-related decline in PF and that lower social status is associated with accelerated aging , 58. In In a previous Polish study involving over 2,500 adults across a wide age range, participants from low social classes reported worse self-rated health. However, upward social mobility was only related to better psychological well-being, not self-rated health. The differences between that study and our findings, where upward social mobility was significantly associated with better PF, could be explained by variations in study design , outcomes assessed , and the method used to classify SES  . Additio4.1.There are several limitations to our study that should be taken into consideration. Firstly, the measures of childhood and adulthood SES were self-reported, which introduces the possibility of reporting bias and misclassification. Additionally, the selected measures may not capture the full range of all relevant SES indicators that could potentially influence health and PF.Secondly, we lacked prospective data on SES and PF over the entire life course. While childhood circumstances and educational attainment tend to remain stable since early adulthood, material conditions may have changed during the follow-up period. Moreover, the assessment of covariates at a single time point is a concern, as these factors may have fluctuated over the course of nearly two decades. In addition, the use of interviews to collect these data may introduce biases. For example, the assessment of alcohol intake relied on a questionnaire method, which is known to underestimate actual consumption. However, it should be noted that this method is reliable in ranking participants based on their alcohol consumption .Thirdly, there may be concerns regarding the assessment of PF. Although we used a standardized method validated for the Polish population throughout the follow-up, the interview-based assessment could still be influenced by local culture, social roles, cognitive abilities, and other factors.Finally, the analyses included a healthier subset of the population from which the sample was selected. This is due to both natural attrition, as over 30% of participants did not survive or were lost to follow-up until the final examination, and the low participation rate in at least four assessments of PF. In a previous report, we found that non-participants were at a higher risk of death . Therefo4.2.This study examined a general population sample from Central Europe, a region with relatively lower life expectancy compared to Western Europe and the United States . This re5.As the proportion of older people in the population continues to rise, the issue of declining PF with age has become a critical public health concern. This cohort study revealed that lower SES and downward social mobility were associated with worse PF, while upward social mobility appeared to counteract the negative effects of low childhood SES. Furthermore, in addition to the cross-sectional associations observed at baseline, higher SES was significantly linked to a slower decline in PF over an 18-year follow-up period. Addressing childhood social inequalities and maintaining high SES levels appear to be crucial in mitigating the decline in PF during old age and could help to maintain the self-sufficiency and social engagement of older people. Future long-term observational studies should focus on investigating the extent to which reducing childhood inequalities could impact the decline of PF in adulthood and old age.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.AP, MP, MK, AD, and MB contributed to the intellectual conceptualization of this study. AP an MP designed the paper and wrote the first draft and finalized the paper. MP performed the statistical analyses. AP and MB jointly designed the HAPIEE study. AD and MK participated in the coordination of data collection. All authors contributed to the article and approved the submitted version.This work was supported by a grant from the Polish National Science Centre (grant 2018/29/B/NZ7/02118). The HAPIEE study was funded by the Welcome Trust (grant WT064947 and WT081081), the US National Institute of Aging (grant R01 AG23522), and the MacArthur Foundation. MB’s contribution was supported by the European Union’s Horizon 2020 Research and Innovation Program projects CETOCOEN Excellence (grant agreement 857560) and R-Exposome Chair (grant agreement 857487).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.


Cancer ratio (CR), which is defined as serum lactate dehydrogenase (LDH) to pleural fluid adenosine deaminase (ADA) ratio, has been reported to be a useful diagnostic marker for malignant pleural effusion (MPE). Whether its diagnostic accuracy is affected by age remains unknown. This study aimed to investigate the effects of age on the diagnostic accuracy of CR.The participants in this study were from a prospective cohort  and a retrospective cohort . All participants were patients with undiagnosed pleural effusion (PE). We used receiver operating characteristic (ROC) curves to evaluate the diagnostic accuracy of CR. The effect of age on the diagnostic accuracy of CR was investigated by adjusting the upper limit of age for participant enrolment.Eighty-eight MPE patients were verified in the SIMPLE cohort, and thirty-five MPE patients were verified in the BUFF cohort. The AUCs of CR in the SIMPLE and BUFF cohorts were 0.60 (95% CI: 0.52–0.68) and 0.63 (95% CI: 0.54–0.71), respectively. In both cohorts, the AUCs of CR decreased with the advancement of age.Age can affect the diagnostic accuracy of CR for MPE. CR has limited diagnostic value in older patients.Cancer ratio is a promising diagnostic marker for malignant pleural effusion.This study revealed that its diagnostic accuracy decreased in older patients.Its diagnostic accuracy is overestimated by previous studies using tuberculosis and pneumonia patients as controls. Pleural effusion (PE) is a common sign in clinical practice, and its differential diagnosis is challenging for clinicians. It can be caused by various disorders, including tuberculosis, heart failure (HF), malignancy, and pneumonia . PE causBiomarkers in the pleural fluid have the advantages of mini-invasiveness, low cost, and short turn-around time and thus represent valuable diagnostic tools for MPE . To dateStudy Investigating Markers in PLeural Effusion) is a prospective, double-blind diagnostic test accuracy study, and its design details have been described previously [This study included two cohorts, named the SIMPLE cohort and the BUFF cohort. The SIMPLE . BUFF is a retrospective study investigating the diagnostic value of serum or pleural biomarkers [The second cohort is the BUFF cohort , and informed consent was obtained from all participants. The BUFF study was approved by the ethics committee of the AHIMMU (NO: KY2021014). Informed consent was waived because of the retrospective design.Mycobacterium tuberculosis (Mtb) culture, Ziehl-Neelsen staining, and response to anti-tuberculosis therapy. HF was diagnosed based on the clinical findings, imaging features , laboratory tests , transudate and response to diuretics.The diagnosis procedure was the same in the BUFF and SIMPLE cohorts. Briefly, MPE was diagnosed with pleural fluid cytology, thoracoscopy and pleural biopsy. In some patients, MPE was defined as the presence of a primary or metastatic tumor, and BPE can be excluded by their clinical characteristics and treatment response. Parapneumonic effusion (PPE) was diagnosed based on imaging, microbiology, biopsy, and response to antibiotic treatment . TPE wasp-value < 0.05 was defined as statistically significant.We used the Kolmogorov-Smirnov method to test the distribution of continuous variables, including serum and pleural fluid biochemistries. The independent Student’s t-test was used to compare the means of the continuous variables with a normal distribution , and the Mann-Whitney U test was used to compare the continuous variables with skewed distribution . The Chi-square test was used to compare categorical variables . The diagnostic accuracy of CR  for MPE was assessed with receiver operating characteristic (ROC) curves. To determine the effect of age on the diagnostic accuracy of CR for MPE, we resampled patients by adjusting the upper limit of age for the patient selection. For example, we set the upper limit of age to 55 years and only analyzed the diagnostic accuracy of CR in patients under 55 years. The details of this method have been reported in our previous study . All anaFigure Normally distributed continuous data were expressed as the mean ± standard deviation (SD), and skewed distributed continuous data were expressed as the median and quartile. Categorical data were expressed as absolute numbers and percentages. WBC, white blood cell; ADA, adenosine deaminase; LDH, lactate dehydrogenase; BPE, benign pleural effusion; MPE, malignant pleural effusion.Figure We searched the PubMed database and found that several studies have investigated the diagnostic accuracy of CR for MPE. The characteristics of these studies are summarized in Table Figure In this study, we validated the diagnostic accuracy of CR for MPE. With two cohorts, we found that the diagnostic accuracy of CR for MPE was limited because its AUC was < 0.70. The accuracy of CR was affected by the participant’s age, and it was decreased in old patients. In addition, the component of BPE may be another factor affecting the diagnostic accuracy of CR.(Table 3). To date, more than 50 causes of PE have been recognized [Compared with previous studies , 28, 30,cognized , and patcognized , 35. In We found that age could affect the diagnostic accuracy of CR. This finding is biologically plausible. The definition of CR is the ratio of serum LDH to pleural ADA. These two biomarkers are affected by age, as reported by previous studies. Specifically, pleural ADA was inversely correlated with age in patients with pleural effusion , 37, andSomeone may argue that the direct factor that affects the diagnostic accuracy of CR may not be “age” but “the percentage of TPE among BPE.“ As shown in Table Although CR is a low-cost diagnostic marker for MPE, our study revealed that its AUC is approximately 0.60, indicating that its diagnostic accuracy is unsatisfactory. However, in the BUFF study, we observed that the AUC was approximately 0.90 in patients aged < 50 years. Therefore, we conclude that CR may be a useful diagnostic marker for MPE in non-elderly patients. However, because the sensitivity and specificity of CR are not 1.00, the results of CR should be interpreted along with the patient’s clinical signs, symptoms and other laboratory results.Our study has some limitations. The first limitation is the retrospective design of the BUFF cohort, which may affect the representativeness of the participants. The second limitation is that a large portion of the participants are from the AHIMMU, and previous studies have revealed that the causes of PE vary across different countries or regions. Notably, the mean age of participants in our cohorts was higher than that in previous studies. The final diagnoses of the participants and their prevalence may affect the diagnostic accuracy , 39. TheTaken together, our study found that the diagnostic accuracy of CR for MPE is affected by age and the disease spectrum of BPE. Its diagnostic accuracy decreases in old patients. Therefore, age and disease spectrum should be considered when interpreting the results of CR.